# Sample: Public health, Kampala: report Project: Sample: Public health, Kampala Goal: Describe the project Sample: Public health, Kampala: run the analyses that tell the most about its data, draw every result, and write a cited report. Run: 12778c77-4c3f-4379-9aca-c9e85676282e (running, 19 agent run(s)) Generated: 2026-10-19T04:37:18Z, version 1 Model cost: USD 0.0000 over 0 model call(s) ## Data used | Data | Kind | Rows | Time span | Summary | Source | | --- | --- | --- | --- | --- | --- | | Health sub-districts | vector, Polygon | 18 | | 18 polygon (area) features; 'population' 18,870 to 128,117 (mean 4.538e+04); 'households' 4,102 to 27,851 (mean 9866); named by 'name' (e.g. Eastfield sub-district, Westbrook sub-district, Millbrook sub-district); total area 90.07 km2; extent 32.5362,0.2729 to 32.6260,0.3543. Areas (Health sub-districts) with population to compare and map. | [150] | | Cases | vector, Point | 2,290 | 2025-10-01 to 2026-09-30 | 2,290 point features; dated by 'onset_date' from 2025-10-01 to 2026-09-30; 'disease' has 3 values (top: malaria 1700, cholera 330, typhoid 260); 'age_group' has 4 values (top: 15-49 797, 0-4 660, 5-14 580); extent 32.5366,0.2735 to 32.6255,0.3539. Dated point events by disease: where and when Cases happen. | [2] | | Clinics | vector, Point | 35 | | 35 point features; 'level' has 4 values (top: HC III 19, HC II 10, HC IV 5); 'beds' 1 to 120 (mean 17.37); 'staff' 6 to 113 (mean 56.8); named by 'name' (e.g. Canal Side HC III, Station Quarter HC III, Brickfield HC III); extent 32.5409,0.2790 to 32.6190,0.3514. Point locations of Clinics by level. | [151] | | Water points | vector, Point | 150 | | 150 point features; 'type' has 4 values (top: protected spring 43, unprotected well 39, borehole 35); 'functional' has 2 values (top: True 133, False 17); extent 32.5387,0.2747 to 32.6248,0.3534. Point locations of Water points by type. | [152] | | Community health worker visits | vector, Point | 1,000 | 2026-04-01 to 2026-09-30 | 1,000 point features; dated by 'visited_at' from 2026-04-01 to 2026-09-30; 'purpose' has 4 values (top: bed net check 257, referral 256, case follow-up 244); 'fever_found' has 2 values (top: False 813, True 187); extent 32.5375,0.2740 to 32.6255,0.3539. Dated point events by purpose: where and when Community health worker visits happen. | [3] | | Outbreak response teams | vector, Point | 6 | | 6 point features; 'focus' has 2 values (top: cholera 4, malaria 2); 'members' 4 to 9 (mean 7.333); named by 'name' (e.g. Rapid response team 1, Rapid response team 2, Rapid response team 3); extent 32.5429,0.2770 to 32.6146,0.3071. Point locations of Outbreak response teams by focus. | [153] | | Vaccination sessions | vector, Point | 200 | 2026-01-05 to 2026-09-29 | 200 point features; dated by 'held_on' from 2026-01-05 to 2026-09-29; 'vaccine' has 4 values (top: measles 61, polio 55, cholera OCV 43); 'district_code' has 18 values (top: HSD07 32, HSD15 23, HSD06 22); 'doses' 44 to 477 (mean 146.5); extent 32.5374,0.2745 to 32.6254,0.3530. Dated point events by vaccine: where and when Vaccination sessions happen. | [4] | | Weekly cases by sub-district | table | 936 | 2025-10-06 to 2026-09-28 | Table of 936 rows and 5 columns without geometry; dated by 'week_start' from 2025-10-06 to 2026-09-28; 'area_code' has 18 values (top: HSD01 52, HSD02 52, HSD03 52); 'cases' 0 to 115 (mean 2.429); 'tests' 5 to 340 (mean 24.62); 'positivity_pct' 0 to 34.8 (mean 8.224). A table of Weekly cases by sub-district without locations. | [5] | | Clinic stock by month | table | 315 | 2026-01-01 to 2026-09-01 | Table of 315 rows and 6 columns without geometry; dated by 'month' from 2026-01-01 to 2026-09-01; 'rdt_kits' 0 to 400 (mean 213.3); 'act_doses' 4 to 600 (mean 307.4); 'ors_sachets' 3 to 900 (mean 446.3). A table of Clinic stock by month without locations. | [6] | | Epidemiological situation report | document | | | 1-page pdf document 'Epidemiological situation report'; language en; mentions 6 place(s): Parkside sub-district (2), 0.29731, 32.59548 (1), Parkside (1), Rapid response team 1 (1), Eastfield sub-district (1). A document about Parkside sub-district, 0.29731, 32.59548, Parkside; it names organizations Malaria; event terms cholera, outbreak. | [154] | | Sample: Public health, Kampala | record | | | | [155] | | Emerging hot spots: Community health worker visits | layer, Polygon | 124 | | | [156] | | Emerging hot spots: Vaccination sessions | layer, Polygon | 24 | | | [157] | | Emerging hot spots: Cases | layer, Polygon | 125 | | | [158] | | Hot spot analysis: Community health worker visits | layer, Polygon | 390 | | | [159] | | Hot spot analysis: Cases | layer, Polygon | 2,259 | | | [98] | | Hot spot analysis: Vaccination sessions | layer, Polygon | 99 | | | [160] | | Summarize within: Health sub-districts | layer, Polygon | 18 | | | [161] | | Summarize within: Health sub-districts | layer, Polygon | 18 | | | [88] | | Summarize within: Health sub-districts | layer, Polygon | 18 | | | [162] | | Period change: Community health worker visits (week of 2026-09-21 to week of 2026-09-28) | layer, Polygon | 14 | | | [10] | | Period change: Vaccination sessions (week of 2026-06-29 to week of 2026-07-06) | layer, Polygon | 10 | | | [12] | | Period change: Cases (week of 2026-05-18 to week of 2026-05-25) | layer, Polygon | 18 | | | [14] | | Calculate density: Community health worker visits | layer, Polygon | 2,452 | | | [93] | | Calculate density: Cases | layer, Polygon | 2,508 | | | [124] | | Calculate density: Vaccination sessions | layer, Polygon | 616 | | | [163] | | Find point clusters: Community health worker visits | layer, Point | 1,000 | | | [164] | | Find point clusters: Cases | layer, Point | 2,290 | | | [165] | | Find point clusters: cluster hulls | layer, Polygon | 1 | | | [104] | | Find outliers: Health sub-districts | layer, Polygon | 18 | | | [166] | | Change beside the counts per health sub-district | layer, Polygon | 18 | | | [89] | | Dense areas of community health worker visits | layer, MultiPolygon | 5 | | | [111] | | Change of vaccination sessions where density is high | layer, Polygon | 10 | | | [94] | | Hot cells of cases per health sub-district | layer, Polygon | 18 | | | [99] | | Nearest outbreak response team to each cluster of cases | layer, Polygon | 1 | | | [105] | | Change of vaccination sessions per health sub-district | layer, Polygon | 18 | | | [114] | | Outbreak response teams within 250 m of each cluster of cases | layer, Polygon | 1 | | | [119] | | Density of community health worker visits and cases together | layer, Polygon | 4,960 | | | [125] | ## Timeline ![Timeline: events per week by dataset](charts/37-timeline-events-per-week-by-dataset.png) *Figure 1. Timeline: events per week by dataset [1]. Per week, 2025-09-29 to 2026-09-28. Source: Cases, Community health worker visits, Vaccination sessions, Weekly cases by sub-district and 1 more (4,741 features). The data is in the appendix.* ![Timeline lanes: events per week by dataset](charts/38-timeline-lanes-events-per-week-by-dataset.png) *Figure 2. Timeline lanes: events per week by dataset [7]. Per week, 2025-09-29 to 2026-09-28. Source: Cases, Community health worker visits, Vaccination sessions, Weekly cases by sub-district and 1 more (4,741 features). The data is in the appendix.* ![Timeline: change from one week to the next](charts/39-timeline-change-from-one-week-to-the-next.png) *Figure 3. Timeline: change from one week to the next [8]. Per week, 2025-10-06 to 2026-09-28. Source: Cases, Community health worker visits, Vaccination sessions, Weekly cases by sub-district and 1 more (4,741 features). The data is in the appendix.* The timeline counts 4,741 dated events in Cases, Community health worker visits, Vaccination sessions and 2 more per week, from week of 2025-09-29 to week of 2026-09-28 [1][2][3][4][5][6]. By dataset: Cases 2,290; Community health worker visits 1,000; Weekly cases by sub-district 936; Clinic stock by month 315; Vaccination sessions 200 [1][7]. The trend is +1.18 events per week; the second half of the weeks has 3,039 against 1,597 in the first (+90.3%) [1]. Week over week, the largest rise was from week of 2026-04-20 to week of 2026-04-27 (119 to 183, +64) and the largest fall was from week of 2026-06-01 to week of 2026-06-08 (147 to 87, -60) [1][8]. The count fell from 36 in week of 2026-09-21 to 18 in week of 2026-09-28 (-50.0%), beyond what chance would give (p = 0.0198) [9][10]. Between the two, the largest rise was in Riverside sub-district (1 to 3, +2) and the largest fall in Canal Side sub-district (5 to 1, -4) [9][10]. The count fell from 9 in week of 2026-06-29 to 2 in week of 2026-07-06 (-77.8%), within what chance would give (p = 0.0654) [11][12]. Between the two, the largest rise was in Canal Side sub-district (0 to 1, +1) and the largest fall in Fernhill sub-district (2 to 0, -2) [11][12]. The count fell from 136 in week of 2026-05-18 to 86 in week of 2026-05-25 (-36.8%), beyond what chance would give (p = 0.000963) [13][14]. Between the two, the largest rise was in Foundry Row sub-district (5 to 8, +3) and the largest fall in Parkside sub-district (77 to 39, -38) [13][14]. **Busiest weeks** - week of 2026-05-11: 220 events [1] - week of 2026-05-18: 200 events [1] - week of 2026-05-04: 190 events [1] **Quietest weeks** - week of 2025-09-29: 16 events [1] - week of 2025-10-13: 38 events [1] - week of 2026-02-09: 40 events [1] ## Overview Overview: the top 3 results in the report's order, each in one distinct color, over Health sub-districts as context [15]. ![Overview: the top 3 results](maps/02-overview-the-top-3-results.png) *Figure 4. Overview: the top 3 results [15]* ## Spatial structure Health sub-districts are 18 areas of 5.004 km2 on average, covering 100 percent of their extent, with no overlap [16]. Cases are clustered (nearest-neighbor ratio 0.90, z = -9.4; features lie 87.9 m apart on average against 98 m if random) [17]. Cases are dated by 'onset_date' from 2025-10-01 to 2026-09-30 at a week grain [17]. Over time the pattern changes: the earlier half is random (ratio 0.9714) and the later half clustered (ratio 0.8894); the center moved 783 m [17]. Clinics are evenly spread (dispersed) (nearest-neighbor ratio 1.58, z = +6.6; features lie 1.115 km apart on average against 705 m if random) [18]. Water points are spread at random (nearest-neighbor ratio 0.96, z = -0.9; features lie 359 m apart on average against 373 m if random) [19]. Community health worker visits are clustered (nearest-neighbor ratio 0.95, z = -3.3; features lie 139 m apart on average against 147 m if random) [20]. Outbreak response teams are evenly spread (dispersed) (nearest-neighbor ratio 1.55, z = +2.6; features lie 1.635 km apart on average against 1.052 km if random) [21]. Vaccination sessions are spread at random (nearest-neighbor ratio 1.00, z = +0.1; features lie 327 m apart on average against 326 m if random) [22]. Vaccination sessions are dated by 'held_on' from 2026-01-05 to 2026-09-29 at a week grain [22]. Over time the pattern changes: the earlier half is random (ratio 1.0941) and the later half dispersed (ratio 1.122); the center moved 259 m [22]. 100 percent of Cases lie inside Health sub-districts (2290 of 2290) [16][17]. 100 percent of Clinics lie inside Health sub-districts (35 of 35) [16][18]. 100 percent of Community health worker visits lie inside Health sub-districts (1000 of 1000) [16][20]. 100 percent of Outbreak response teams lie inside Health sub-districts (6 of 6) [16][21]. 100 percent of Vaccination sessions lie inside Health sub-districts (200 of 200) [16][22]. 100 percent of Water points lie inside Health sub-districts (150 of 150) [16][19]. Outbreak response teams lie 35.3 m from the nearest of Cases (median); 83 percent lie within 100 m [17][21]. Water points lie 71.9 m from the nearest of Cases (median); 71 percent lie within 100 m [17][19]. Community health worker visits lie 77.4 m from the nearest of Cases (median); 62 percent lie within 100 m [17][20]. Vaccination sessions lie 79.5 m from the nearest of Cases (median); 64 percent lie within 100 m [17][22]. | Result | Value | | --- | --- | | layers profiled | 7 | | clustered point layers | 2 | | dispersed point layers | 2 | | random point layers | 2 | **Findings** - **Evidenced:** Health sub-districts are 18 areas of 5.004 km2 on average, covering 100 percent of their extent, with no overlap [16]. - **Evidenced:** Cases are clustered (nearest-neighbor ratio 0.90, z = -9.4; features lie 87.9 m apart on average against 98 m if random) [17]. - **Evidenced:** Cases are dated by 'onset_date' from 2025-10-01 to 2026-09-30 at a week grain [17]. - **Evidenced:** Over time the pattern changes: the earlier half is random (ratio 0.9714) and the later half clustered (ratio 0.8894); the center moved 783 m [17]. - **Evidenced:** Clinics are evenly spread (dispersed) (nearest-neighbor ratio 1.58, z = +6.6; features lie 1.115 km apart on average against 705 m if random) [18]. - **Evidenced:** Water points are spread at random (nearest-neighbor ratio 0.96, z = -0.9; features lie 359 m apart on average against 373 m if random) [19]. - **Evidenced:** Community health worker visits are clustered (nearest-neighbor ratio 0.95, z = -3.3; features lie 139 m apart on average against 147 m if random) [20]. - **Evidenced:** Outbreak response teams are evenly spread (dispersed) (nearest-neighbor ratio 1.55, z = +2.6; features lie 1.635 km apart on average against 1.052 km if random) [21]. - **Evidenced:** Vaccination sessions are spread at random (nearest-neighbor ratio 1.00, z = +0.1; features lie 327 m apart on average against 326 m if random) [22]. - **Evidenced:** Vaccination sessions are dated by 'held_on' from 2026-01-05 to 2026-09-29 at a week grain [22]. - **Evidenced:** Over time the pattern changes: the earlier half is random (ratio 1.0941) and the later half dispersed (ratio 1.122); the center moved 259 m [22]. - **Evidenced:** 100 percent of Cases lie inside Health sub-districts (2290 of 2290) [16][17]. - **Evidenced:** 100 percent of Clinics lie inside Health sub-districts (35 of 35) [16][18]. - **Evidenced:** 100 percent of Community health worker visits lie inside Health sub-districts (1000 of 1000) [16][20]. - **Evidenced:** 100 percent of Outbreak response teams lie inside Health sub-districts (6 of 6) [16][21]. - **Evidenced:** 100 percent of Vaccination sessions lie inside Health sub-districts (200 of 200) [16][22]. - **Evidenced:** 100 percent of Water points lie inside Health sub-districts (150 of 150) [16][19]. - **Evidenced:** Outbreak response teams lie 35.3 m from the nearest of Cases (median); 83 percent lie within 100 m [17][21]. - **Evidenced:** Water points lie 71.9 m from the nearest of Cases (median); 71 percent lie within 100 m [17][19]. - **Evidenced:** Community health worker visits lie 77.4 m from the nearest of Cases (median); 62 percent lie within 100 m [17][20]. - **Evidenced:** Vaccination sessions lie 79.5 m from the nearest of Cases (median); 64 percent lie within 100 m [17][22]. ## Space time hot spots **What this analysis is.** Emerging hot spots: Where hot spots are new, consecutive, intensifying, persistent, diminishing, sporadic, oscillating or historical over time [23]. In ArcGIS it is Emerging Hot Spot Analysis or Create Space Time Cube [23]. **Why it was run on this data.** Community health worker visits carry dates in 'visited_at': emerging hot spot analysis finds new, intensifying and fading hot spots per week [24]. **What it found.** Space time hot spots on Community health worker visits: Emerging hot spots: 1 layer saved: Emerging hot spots: Community health worker visits (124 features) [20][25]. It reported result, count 124; result, elapsed ms 17; result, resolution 8; result, features used 1,000; result, time neighbors 0; billing, cost 0.15 [25]. | Result | Value | | --- | --- | | result, count | 124 | | result, elapsed ms | 17 | | result, resolution | 8 | | result, features used | 1,000 | | result, time neighbors | 0 | | billing, cost | 0.15 | | billing, list price | 0.15 | | billing, upstream cost | 0 | ![Emerging hot spots: community health worker visits and cases](maps/03-emerging-hot-spots-community-health-worker-visits-and-cases.png) *Figure 5. Emerging hot spots: community health worker visits and cases [26]* **What the map shows.** The map draws Emerging hot spots: community health worker visits by category in 2 classes: Sporadic hot spot, No pattern detected [26]. The map draws Health sub-districts with one symbol [26]. The map draws Emerging hot spots: cases with one symbol [26]. It draws 267 features [26]. It covers longitude 32.5 to 32.661 and latitude 0.252 to 0.372, at about 1:74,770 [26]. **Findings** - **Evidenced:** Space time hot spots on Community health worker visits: Emerging hot spots: 1 layer saved: Emerging hot spots: Community health worker visits (124 features) [20][25]. - **Evidenced:** It reported result, count 124; result, elapsed ms 17; result, resolution 8; result, features used 1,000; result, time neighbors 0; billing, cost 0.15 [25]. *Tool gp_space_time_hot_spots; parameters {"project": "2612067c-badd-4310-9678-8f580d5e3d32", "features": {"layer": "Community health worker visits (4d70d1ce)"}, "interval": "week", "bin_shape": "h3", "resolution": 8, "time_field": "visited_at"}* ## Space time hot spots **What this analysis is.** Emerging hot spots: Where hot spots are new, consecutive, intensifying, persistent, diminishing, sporadic, oscillating or historical over time [23]. In ArcGIS it is Emerging Hot Spot Analysis or Create Space Time Cube [23]. **Why it was run on this data.** Vaccination sessions carry dates in 'held_on': emerging hot spot analysis finds new, intensifying and fading hot spots per week [27]. Measured: the pattern of Vaccination sessions changes over time (earlier half random, later half dispersed; the center moved 259 m) [27]. **What it found.** The analysis of vaccination sessions using the `gp_space_time_hot_spots` tool resulted in the generation of 24 features, saving the output as "Emerging hot spots: Vaccination sessions" [28]. This computation had an elapsed time of 5 milliseconds [28]. The process utilized 200 features and had a resolution of 7 [28]. The total cost incurred for this operation was 0.11 [28]. | Result | Value | | --- | --- | | result, count | 24 | | result, elapsed ms | 5 | | result, resolution | 7 | | result, features used | 200 | | result, time neighbors | 0 | | billing, cost | 0.11 | | billing, list price | 0.11 | | billing, upstream cost | 0 | ![Emerging hot spots: vaccination sessions](maps/04-emerging-hot-spots-vaccination-sessions.png) *Figure 6. Emerging hot spots: vaccination sessions [29]* **What the map shows.** The map draws Emerging hot spots: Vaccination sessions by category in 2 classes: Sporadic hot spot, No pattern detected [29]. The map draws Health sub-districts with one symbol [29]. It draws 42 features [29]. It covers longitude 32.489 to 32.673 and latitude 0.239 to 0.376, at about 1:85,295 [29]. **Findings** - **Evidenced:** Space time hot spots on Vaccination sessions: Emerging hot spots: 1 layer saved: Emerging hot spots: Vaccination sessions (24 features) [22][28]. - **Evidenced:** It reported result, count 24; result, elapsed ms 5; result, resolution 7; result, features used 200; result, time neighbors 0; billing, cost 0.11 [28]. *Tool gp_space_time_hot_spots; parameters {"project": "2612067c-badd-4310-9678-8f580d5e3d32", "features": {"layer": "Vaccination sessions (6798c97c)"}, "interval": "week", "bin_shape": "h3", "resolution": 7, "time_field": "held_on"}* ## Space time hot spots **What this analysis is.** Emerging hot spots: Where hot spots are new, consecutive, intensifying, persistent, diminishing, sporadic, oscillating or historical over time [23]. In ArcGIS it is Emerging Hot Spot Analysis or Create Space Time Cube [23]. **Why it was run on this data.** Cases carry dates in 'onset_date': emerging hot spot analysis finds new, intensifying and fading hot spots per week [30]. Measured: the pattern of Cases changes over time (earlier half random, later half clustered; the center moved 783 m) [30]. **What it found.** Space time hot spots on Cases: Emerging hot spots: 1 layer saved: Emerging hot spots: Cases (125 features) [17][31]. It reported result, count 125; result, elapsed ms 26; result, resolution 8; result, features used 2,290; result, time neighbors 0; billing, cost 0.2145 [31]. | Result | Value | | --- | --- | | result, count | 125 | | result, elapsed ms | 26 | | result, resolution | 8 | | result, features used | 2,290 | | result, time neighbors | 0 | | billing, cost | 0.2145 | | billing, list price | 0.2145 | | billing, upstream cost | 0 | **What the map shows.** Drawn with space time hot spots on the map Emerging hot spots: community health worker visits and cases [26]. **Findings** - **Evidenced:** Space time hot spots on Cases: Emerging hot spots: 1 layer saved: Emerging hot spots: Cases (125 features) [17][31]. - **Evidenced:** It reported result, count 125; result, elapsed ms 26; result, resolution 8; result, features used 2,290; result, time neighbors 0; billing, cost 0.2145 [31]. *Tool gp_space_time_hot_spots; parameters {"project": "2612067c-badd-4310-9678-8f580d5e3d32", "features": {"layer": "Cases (03c8f72b)"}, "interval": "week", "bin_shape": "h3", "resolution": 8, "time_field": "onset_date"}* ## Hot spots **What this analysis is.** Hot spot analysis: Getis-Ord Gi* hot and cold spots with z-scores, p-values and confidence bins [32]. In ArcGIS it is Find Hot Spots or Hot Spot Analysis (Getis-Ord Gi*); in QGIS, qgis:hotspot (plugin) [32]. **Why it was run on this data.** 1000 points: Getis-Ord Gi* tests whether the concentrations in Community health worker visits are statistically significant [33]. Measured: Community health worker visits are clustered (nearest-neighbor ratio 0.95, z = -3.3), median spacing 124 m [33]. Cells of 500 m and a band of 1000 m from the measured spacing [33]. **What it found.** Hot spots on Community health worker visits: Hot spot analysis: 1 layer saved: Hot spot analysis: Community health worker visits (390 features) [20][34]. It reported result, bins 390; result, count 390; result, elapsed ms 17; result, cell size m 500; result, incidents used 1,000; result, distance band m 1,000 [34]. | Result | Value | | --- | --- | | result, bins | 390 | | result, count | 390 | | result, elapsed ms | 17 | | result, cell size m | 500 | | result, incidents used | 1,000 | | result, distance band m | 1,000 | | billing, cost | 0.07 | | billing, list price | 0.07 | | billing, upstream cost | 0 | ![Hot spots: community health worker visits](maps/05-hot-spots-community-health-worker-visits.png) *Figure 7. Hot spots: community health worker visits [35]* **What the map shows.** The map draws Hot spot analysis: Community health worker visits by gi_bin in 7 classes: Hot spot - 99% confidence, Hot spot - 95% confidence, Hot spot - 90% confidence, Not significant, Cold spot - 90% confidence, Cold spot - 95% confidence and 1 more [35]. The map draws Health sub-districts with one symbol [35]. It draws 408 features [35]. It covers longitude 32.51 to 32.652 and latitude 0.257 to 0.366, at about 1:66,000 [35]. **Findings** - **Evidenced:** Hot spots on Community health worker visits: Hot spot analysis: 1 layer saved: Hot spot analysis: Community health worker visits (390 features) [20][34]. - **Evidenced:** It reported result, bins 390; result, count 390; result, elapsed ms 17; result, cell size m 500; result, incidents used 1,000; result, distance band m 1,000 [34]. *Tool gp_hot_spots; parameters {"project": "2612067c-badd-4310-9678-8f580d5e3d32", "features": {"layer": "Community health worker visits (4d70d1ce)"}, "aggregation": "hexagon", "cell_size_m": 500.0, "distance_band_m": 1000.0}* ## Hot spots **What this analysis is.** Hot spot analysis: Getis-Ord Gi* hot and cold spots with z-scores, p-values and confidence bins [32]. In ArcGIS it is Find Hot Spots or Hot Spot Analysis (Getis-Ord Gi*); in QGIS, qgis:hotspot (plugin) [32]. **Why it was run on this data.** 2290 points: Getis-Ord Gi* tests whether the concentrations in Cases are statistically significant [36]. Measured: Cases are clustered (nearest-neighbor ratio 0.90, z = -9.4), median spacing 75.6 m [36]. Cells of 200 m and a band of 500 m from the measured spacing [36]. **What it found.** Hot spots on Cases: Hot spot analysis: 1 layer saved: Hot spot analysis: Cases (2259 features) [17][37]. It reported result, bins 2,259; result, count 2,259; result, elapsed ms 75; result, cell size m 200; result, incidents used 2,290; result, distance band m 500 [37]. | Result | Value | | --- | --- | | result, bins | 2,259 | | result, count | 2,259 | | result, elapsed ms | 75 | | result, cell size m | 200 | | result, incidents used | 2,290 | | result, distance band m | 500 | | billing, cost | 0.0958 | | billing, list price | 0.0958 | | billing, upstream cost | 0 | ![Hot spots: cases](maps/06-hot-spots-cases.png) *Figure 8. Hot spots: cases [38]* **What the map shows.** The map draws Hot spot analysis: Cases by gi_bin in 7 classes: Hot spot - 99% confidence, Hot spot - 95% confidence, Hot spot - 90% confidence, Not significant, Cold spot - 90% confidence, Cold spot - 95% confidence and 1 more [38]. The map draws Health sub-districts with one symbol [38]. It draws 2,277 features [38]. It covers longitude 32.512 to 32.649 and latitude 0.26 to 0.365, at about 1:63,311 [38]. **Findings** - **Evidenced:** Hot spots on Cases: Hot spot analysis: 1 layer saved: Hot spot analysis: Cases (2259 features) [17][37]. - **Evidenced:** It reported result, bins 2,259; result, count 2,259; result, elapsed ms 75; result, cell size m 200; result, incidents used 2,290; result, distance band m 500 [37]. *Tool gp_hot_spots; parameters {"project": "2612067c-badd-4310-9678-8f580d5e3d32", "features": {"layer": "Cases (03c8f72b)"}, "aggregation": "hexagon", "cell_size_m": 200.0, "distance_band_m": 500.0}* ## Hot spots **What this analysis is.** Hot spot analysis: Getis-Ord Gi* hot and cold spots with z-scores, p-values and confidence bins [32]. In ArcGIS it is Find Hot Spots or Hot Spot Analysis (Getis-Ord Gi*); in QGIS, qgis:hotspot (plugin) [32]. **Why it was run on this data.** 200 points: Getis-Ord Gi* tests whether the concentrations in Vaccination sessions are statistically significant [39]. **What it found.** Hot spots on Vaccination sessions: Hot spot analysis: 1 layer saved: Hot spot analysis: Vaccination sessions (99 features) [22][40]. It reported result, bins 99; result, count 99; result, elapsed ms 5; result, cell size m 1,000; result, incidents used 200; result, distance band m 2,000 [40]. | Result | Value | | --- | --- | | result, bins | 99 | | result, count | 99 | | result, elapsed ms | 5 | | result, cell size m | 1,000 | | result, incidents used | 200 | | result, distance band m | 2,000 | | billing, cost | 0.054 | | billing, list price | 0.054 | | billing, upstream cost | 0 | ![Hot spots: vaccination sessions](maps/07-hot-spots-vaccination-sessions.png) *Figure 9. Hot spots: vaccination sessions [41]* **What the map shows.** The map draws Hot spot analysis: Vaccination sessions by gi_bin in 7 classes: Hot spot - 99% confidence, Hot spot - 95% confidence, Hot spot - 90% confidence, Not significant, Cold spot - 90% confidence, Cold spot - 95% confidence and 1 more [41]. The map draws Health sub-districts with one symbol [41]. It draws 117 features [41]. It covers longitude 32.499 to 32.659 and latitude 0.251 to 0.373, at about 1:73,740 [41]. **Findings** - **Evidenced:** Hot spots on Vaccination sessions: Hot spot analysis: 1 layer saved: Hot spot analysis: Vaccination sessions (99 features) [22][40]. - **Evidenced:** It reported result, bins 99; result, count 99; result, elapsed ms 5; result, cell size m 1,000; result, incidents used 200; result, distance band m 2,000 [40]. *Tool gp_hot_spots; parameters {"project": "2612067c-badd-4310-9678-8f580d5e3d32", "features": {"layer": "Vaccination sessions (6798c97c)"}, "aggregation": "hexagon", "cell_size_m": 1000.0, "distance_band_m": 2000.0}* ## Summarize within **What this analysis is.** Summarize within: Count and aggregate the features inside each polygon (or bin), proportionally for lines and polygons [42]. In ArcGIS it is Summarize Within or Aggregate Points; in QGIS, native:countpointsinpolygon or native:sumlinelengths [42]. **Why it was run on this data.** Count Community health worker visits inside each feature of Health sub-districts by purpose; the counts map as a choropleth (natural breaks, sequential colors) [43]. **What it found.** Summarize within on Community health worker visits, Health sub-districts: Summarize within: 1 layer saved: Summarize within: Health sub-districts (18 features) [16][20][44]. It reported result, count 18; result, elapsed ms 2; billing, cost 0.03036; billing, list price 0.03036; billing, upstream cost 0 [44]. | Result | Value | | --- | --- | | result, count | 18 | | result, elapsed ms | 2 | | billing, cost | 0.03036 | | billing, list price | 0.03036 | | billing, upstream cost | 0 | ![Counts: community health worker visits, by health sub-district](maps/08-counts-community-health-worker-visits-by-health-sub-district.png) *Figure 10. Counts: community health worker visits, by health sub-district [45]* **What the map shows.** The map draws Summarize within: Health sub-districts by count in 5 classes: 14 to 28, 28 to 53, 53 to 75, 75 to 116, 116 to 150 [45]. It draws 18 features [45]. It covers longitude 32.515 to 32.648 and latitude 0.261 to 0.363, at about 1:61,580 [45]. **Findings** - **Evidenced:** Summarize within on Community health worker visits, Health sub-districts: Summarize within: 1 layer saved: Summarize within: Health sub-districts (18 features) [16][20][44]. - **Evidenced:** It reported result, count 18; result, elapsed ms 2; billing, cost 0.03036; billing, list price 0.03036; billing, upstream cost 0 [44]. *Tool gp_summarize_within; parameters {"stats": [], "project": "2612067c-badd-4310-9678-8f580d5e3d32", "features": {"layer": "Community health worker visits (4d70d1ce)"}, "group_by": "purpose", "polygons": {"layer": "Health sub-districts (433b88cc)"}}* ## Summarize within **What this analysis is.** Summarize within: Count and aggregate the features inside each polygon (or bin), proportionally for lines and polygons [42]. In ArcGIS it is Summarize Within or Aggregate Points; in QGIS, native:countpointsinpolygon or native:sumlinelengths [42]. **Why it was run on this data.** Count Community health worker visits inside each feature of Health sub-districts by purpose; the counts map as a choropleth (natural breaks, sequential colors) [43]. **What it found.** Summarize within on Vaccination sessions, Health sub-districts: Summarize within: 1 layer saved: Summarize within: Health sub-districts (18 features) [16][22][46]. It reported result, count 18; result, elapsed ms 0; billing, cost 0.01438; billing, list price 0.01438; billing, upstream cost 0 [46]. | Result | Value | | --- | --- | | result, count | 18 | | result, elapsed ms | 0 | | billing, cost | 0.01438 | | billing, list price | 0.01438 | | billing, upstream cost | 0 | ![Counts: vaccination sessions, by health sub-district](maps/09-counts-vaccination-sessions-by-health-sub-district.png) *Figure 11. Counts: vaccination sessions, by health sub-district [47]* **What the map shows.** The map draws Summarize within: Health sub-districts by count in 5 classes: 3 to 7, 7 to 11, 11 to 19, 19 to 27.5, 27.5 to 32 [47]. It draws 18 features [47]. It covers longitude 32.515 to 32.648 and latitude 0.261 to 0.363, at about 1:61,580 [47]. **Findings** - **Evidenced:** Summarize within on Vaccination sessions, Health sub-districts: Summarize within: 1 layer saved: Summarize within: Health sub-districts (18 features) [16][22][46]. - **Evidenced:** It reported result, count 18; result, elapsed ms 0; billing, cost 0.01438; billing, list price 0.01438; billing, upstream cost 0 [46]. *Tool gp_summarize_within; parameters {"stats": [{"stat": "sum", "field": "doses"}], "project": "2612067c-badd-4310-9678-8f580d5e3d32", "features": {"layer": "Vaccination sessions (6798c97c)"}, "group_by": "vaccine", "polygons": {"layer": "Health sub-districts (433b88cc)"}}* ## Summarize within **What this analysis is.** Summarize within: Count and aggregate the features inside each polygon (or bin), proportionally for lines and polygons [42]. In ArcGIS it is Summarize Within or Aggregate Points; in QGIS, native:countpointsinpolygon or native:sumlinelengths [42]. **Why it was run on this data.** Count Community health worker visits inside each feature of Health sub-districts by purpose; the counts map as a choropleth (natural breaks, sequential colors) [43]. **What it found.** The `gp_summarize_within` function summarized Health sub-districts, which contained 18 features [48]. | Result | Value | | --- | --- | | result, count | 18 | | result, elapsed ms | 3 | | billing, cost | 0.05616 | | billing, list price | 0.05616 | | billing, upstream cost | 0 | ![Counts: cases, by health sub-district](maps/10-counts-cases-by-health-sub-district.png) *Figure 12. Counts: cases, by health sub-district [49]* **What the map shows.** The map draws Summarize within: Health sub-districts by count in 5 classes: 33 to 64, 64 to 108, 108 to 291, 291 to 402, 402 to 513 [49]. It draws 18 features [49]. It covers longitude 32.515 to 32.648 and latitude 0.261 to 0.363, at about 1:61,580 [49]. **Findings** - **Evidenced:** Summarize within on Cases, Health sub-districts: Summarize within: 1 layer saved: Summarize within: Health sub-districts (18 features) [16][17][48]. - **Evidenced:** It reported result, count 18; result, elapsed ms 3; billing, cost 0.05616; billing, list price 0.05616; billing, upstream cost 0 [48]. *Tool gp_summarize_within; parameters {"stats": [], "project": "2612067c-badd-4310-9678-8f580d5e3d32", "features": {"layer": "Cases (03c8f72b)"}, "group_by": "disease", "polygons": {"layer": "Health sub-districts (433b88cc)"}}* ## Period change **What this analysis is.** Period change: Counts or sums per period (week over week, month over month, any two periods, or before and after a date), per area, per H3 cell or in total, with the places of largest rise and fall and a significance note [50]. In ArcGIS it is Summarize Percent Change (Crime Analysis) or Change Point Detection [50]. **Why it was run on this data.** Community health worker visits carry dates in 'visited_at' over 26 weeks: the count per week, week over week, with the places of largest rise and fall per feature of Health sub-districts and whether the change is beyond chance [51]. **What it found.** Period change on Health sub-districts, Community health worker visits produced Period change: Community health worker visits (week of 2026-09-21 to week of 2026-09-28) (14 features) [9][10][16][20]. Community health worker visits: count per week shows a trend of -0.0873 per week; the second half of its weeks has 488 against 472 in the first (+3.4%) [53]. The chart Community health worker visits: week of 2026-09-21 to week of 2026-09-28 by place is drawn from 28 rows [54]. The chart Period change: Community health worker visits (week of 2026-09-21 to week of 2026-09-28) is drawn from 5 rows [55]. ![Period change: community health worker visits, by health sub-district](maps/11-period-change-community-health-worker-visits-by-health-sub-d.png) *Figure 13. Period change: community health worker visits, by health sub-district [52]* **What the map shows.** The map draws Period change: Community health worker visits (week of 2026-09-21 to week of 2026-09-28) by change in 5 classes: < -1.5 Std; Dev, -1.5 to -0.5 Std; Dev, -0.5 to 0.5 Std; Dev, 0.5 to 1.5 Std; Dev, > 1.5 Std; Dev [52]. It draws 14 features [52]. It covers longitude 32.512 to 32.65 and latitude 0.261 to 0.364, at about 1:63,751 [52]. ![Community health worker visits: count per week](charts/01-community-health-worker-visits-count-per-week.png) *Figure 14. Community health worker visits: count per week [53]. Per week, 2026-03-30 to 2026-09-28. Source: Community health worker visits (1,000 features). The data is in the appendix.* **What the chart shows.** The time series chart illustrates community health worker visits per week from March 30, 2026, to September 28, 2026 [53]. The overall trend is a minor decrease of -0.0873 per week, with the first half averaging 472 and the second half averaging 488, which is a +3.4% increase [53]. The data shows fluctuations throughout the period, with the count reaching a peak of approximately 48 around the week of July 6 and a noticeable drop to 18 in the final week of September 28 [53]. The analysis highlights a specific fall from 36 in the week of September 21 to 18 in the week of September 28 (a -50.0% change), which is stated as unlikely to be chance (p = 0.0198) [53]. *Inferred: read from the image by gemma4:e4b, not evidence.* ![Community health worker visits: week of 2026-09-21 to week of 2026-09-28 by place](charts/02-community-health-worker-visits-week-of-2026-09-21-to-week-of.png) *Figure 15. Community health worker visits: week of 2026-09-21 to week of 2026-09-28 by place [54]. Source: Community health worker visits (1,000 features). The data is in the appendix.* ![Period change: Community health worker visits (week of 2026-09-21 to week of 2026-09-28)](charts/18-period-change-community-health-worker-visits-week-of-2026-09.png) *Figure 16. Period change: Community health worker visits (week of 2026-09-21 to week of 2026-09-28) [55]. Source: Period change: Community health worker visits (week of 2026-09-21 to week of 2026-09-28) (14 features). The data is in the appendix.* **Findings** - **Evidenced:** Period change on Health sub-districts, Community health worker visits produced Period change: Community health worker visits (week of 2026-09-21 to week of 2026-09-28) (14 features) [9][10][16][20]. - **Evidenced:** Community health worker visits: count per week shows a trend of -0.0873 per week; the second half of its weeks has 488 against 472 in the first (+3.4%) [53]. - **Evidenced:** The chart Community health worker visits: week of 2026-09-21 to week of 2026-09-28 by place is drawn from 28 rows [54]. - **Evidenced:** The chart Period change: Community health worker visits (week of 2026-09-21 to week of 2026-09-28) is drawn from 5 rows [55]. *Tool gp_period_change; parameters {"by": "area", "areas": {"layer": "Health sub-districts (433b88cc)"}, "period": "week", "project": "2612067c-badd-4310-9678-8f580d5e3d32", "features": {"layer": "Community health worker visits (4d70d1ce)"}, "area_field": "name", "time_field": "visited_at"}* ## Period change **What this analysis is.** Period change: Counts or sums per period (week over week, month over month, any two periods, or before and after a date), per area, per H3 cell or in total, with the places of largest rise and fall and a significance note [50]. In ArcGIS it is Summarize Percent Change (Crime Analysis) or Change Point Detection [50]. **Why it was run on this data.** Community health worker visits carry dates in 'visited_at' over 26 weeks: the count per week, week over week, with the places of largest rise and fall per feature of Health sub-districts and whether the change is beyond chance [51]. **What it found.** Period change on Health sub-districts, Vaccination sessions produced Period change: Vaccination sessions (week of 2026-06-29 to week of 2026-07-06) (10 features) [11][12][16][22]. Vaccination sessions: count per week shows a trend of -0.00587 per week; the second half of its weeks has 103 against 95 in the first (+8.4%) [57]. The chart Vaccination sessions: week of 2026-06-29 to week of 2026-07-06 by place is drawn from 20 rows [58]. The chart Period change: Vaccination sessions (week of 2026-06-29 to week of 2026-07-06) is drawn from 5 rows [59]. ![Period change: vaccination sessions, by health sub-district](maps/12-period-change-vaccination-sessions-by-health-sub-district.png) *Figure 17. Period change: vaccination sessions, by health sub-district [56]* **What the map shows.** The map draws Period change: Vaccination sessions (week of 2026-06-29 to week of 2026-07-06) by change in 4 classes: < -0.5 Std; Dev, -0.5 to 0.5 Std; Dev, 0.5 to 1.5 Std; Dev, > 1.5 Std; Dev [56]. It draws 10 features [56]. It covers longitude 32.512 to 32.65 and latitude 0.261 to 0.364, at about 1:63,751 [56]. ![Vaccination sessions: count per week](charts/03-vaccination-sessions-count-per-week.png) *Figure 18. Vaccination sessions: count per week [57]. Per week, 2026-01-05 to 2026-09-28. Source: Vaccination sessions (200 features). The data is in the appendix.* **What the chart shows.** Overall, the vaccination sessions count fluctuates significantly over the 39-week period from January to September 20 [57]. *Inferred: read from the image by gemma4:e4b, not evidence.* ![Vaccination sessions: week of 2026-06-29 to week of 2026-07-06 by place](charts/04-vaccination-sessions-week-of-2026-06-29-to-week-of-2026-07-0.png) *Figure 19. Vaccination sessions: week of 2026-06-29 to week of 2026-07-06 by place [58]. Source: Vaccination sessions (200 features). The data is in the appendix.* ![Period change: Vaccination sessions (week of 2026-06-29 to week of 2026-07-06)](charts/19-period-change-vaccination-sessions-week-of-2026-06-29-to-wee.png) *Figure 20. Period change: Vaccination sessions (week of 2026-06-29 to week of 2026-07-06) [59]. Source: Period change: Vaccination sessions (week of 2026-06-29 to week of 2026-07-06) (10 features). The data is in the appendix.* **Findings** - **Evidenced:** Period change on Health sub-districts, Vaccination sessions produced Period change: Vaccination sessions (week of 2026-06-29 to week of 2026-07-06) (10 features) [11][12][16][22]. - **Evidenced:** Vaccination sessions: count per week shows a trend of -0.00587 per week; the second half of its weeks has 103 against 95 in the first (+8.4%) [57]. - **Evidenced:** The chart Vaccination sessions: week of 2026-06-29 to week of 2026-07-06 by place is drawn from 20 rows [58]. - **Evidenced:** The chart Period change: Vaccination sessions (week of 2026-06-29 to week of 2026-07-06) is drawn from 5 rows [59]. *Tool gp_period_change; parameters {"by": "area", "areas": {"layer": "Health sub-districts (433b88cc)"}, "period": "week", "project": "2612067c-badd-4310-9678-8f580d5e3d32", "features": {"layer": "Vaccination sessions (6798c97c)"}, "area_field": "name", "time_field": "held_on"}* ## Period change **What this analysis is.** Period change: Counts or sums per period (week over week, month over month, any two periods, or before and after a date), per area, per H3 cell or in total, with the places of largest rise and fall and a significance note [50]. In ArcGIS it is Summarize Percent Change (Crime Analysis) or Change Point Detection [50]. **Why it was run on this data.** Community health worker visits carry dates in 'visited_at' over 26 weeks: the count per week, week over week, with the places of largest rise and fall per feature of Health sub-districts and whether the change is beyond chance [51]. **What it found.** Period change on Health sub-districts, Cases produced Period change: Cases (week of 2026-05-18 to week of 2026-05-25) (18 features) [13][14][16][17]. Cases: count per week shows a trend of -0.115 per week; the second half of its weeks has 1,289 against 980 in the first (+31.5%) [61]. The chart Cases: week of 2026-05-18 to week of 2026-05-25 by place is drawn from 30 rows [62]. The chart Period change: Cases (week of 2026-05-18 to week of 2026-05-25) is drawn from 6 rows [63]. ![Period change: cases, by health sub-district](maps/13-period-change-cases-by-health-sub-district.png) *Figure 21. Period change: cases, by health sub-district [60]* **What the map shows.** The map draws Period change: Cases (week of 2026-05-18 to week of 2026-05-25) by change in 6 classes: < -3.5 Std; Dev, -3.5 to -2.5 Std; Dev, -2.5 to -1.5 Std; Dev, -1.5 to -0.5 Std; Dev, -0.5 to 0.5 Std; Dev, > 0.5 Std; Dev [60]. It draws 18 features [60]. It covers longitude 32.512 to 32.65 and latitude 0.261 to 0.364, at about 1:63,751 [60]. ![Cases: count per week](charts/05-cases-count-per-week.png) *Figure 22. Cases: count per week [61]. Per week, 2025-09-29 to 2026-09-28. Source: Cases (2,290 features). The data is in the appendix.* ![Cases: week of 2026-05-18 to week of 2026-05-25 by place](charts/06-cases-week-of-2026-05-18-to-week-of-2026-05-25-by-place.png) *Figure 23. Cases: week of 2026-05-18 to week of 2026-05-25 by place [62]. Source: Cases (2,290 features). The data is in the appendix.* ![Period change: Cases (week of 2026-05-18 to week of 2026-05-25)](charts/20-period-change-cases-week-of-2026-05-18-to-week-of-2026-05-25.png) *Figure 24. Period change: Cases (week of 2026-05-18 to week of 2026-05-25) [63]. Source: Period change: Cases (week of 2026-05-18 to week of 2026-05-25) (18 features). The data is in the appendix.* **Findings** - **Evidenced:** Period change on Health sub-districts, Cases produced Period change: Cases (week of 2026-05-18 to week of 2026-05-25) (18 features) [13][14][16][17]. - **Evidenced:** Cases: count per week shows a trend of -0.115 per week; the second half of its weeks has 1,289 against 980 in the first (+31.5%) [61]. - **Evidenced:** The chart Cases: week of 2026-05-18 to week of 2026-05-25 by place is drawn from 30 rows [62]. - **Evidenced:** The chart Period change: Cases (week of 2026-05-18 to week of 2026-05-25) is drawn from 6 rows [63]. *Tool gp_period_change; parameters {"by": "area", "areas": {"layer": "Health sub-districts (433b88cc)"}, "period": "week", "project": "2612067c-badd-4310-9678-8f580d5e3d32", "features": {"layer": "Cases (03c8f72b)"}, "area_field": "name", "time_field": "onset_date"}* ## Calculate density **What this analysis is.** Calculate density: Kernel density per square kilometer on a grid [64]. In ArcGIS it is Calculate Density or Kernel Density; in QGIS, native:heatmapkerneldensityestimation [64]. **Why it was run on this data.** 1000 points in Community health worker visits: a density surface shows where they concentrate [65]. Measured: Community health worker visits are clustered (nearest-neighbor ratio 0.95, z = -3.3), median spacing 124 m [65]. **What it found.** Calculate density on Community health worker visits: Calculate density: 1 layer saved: Calculate density: Community health worker visits (2452 features) [20][66]. It reported result, count 2,452; result, elapsed ms 43; result, bandwidth m 500; result, cell size m 200; result, excluded null 0; result, max density per km2 34.43 [66]. | Result | Value | | --- | --- | | result, count | 2,452 | | result, elapsed ms | 43 | | result, bandwidth m | 500 | | result, cell size m | 200 | | result, excluded null | 0 | | result, max density per km2 | 34.43 | | billing, cost | 0.07 | | billing, list price | 0.07 | | billing, upstream cost | 0 | ![Density: community health worker visits](maps/14-density-community-health-worker-visits.png) *Figure 25. Density: community health worker visits [67]* **What the map shows.** The map draws Calculate density: Community health worker visits by density_per_km2 in 6 classes: 2.99 to 6.52, 6.52 to 9.98, 9.98 to 13.6, 13.6 to 18, 18 to 23.7, 23.7 to 34.4 [67]. The map draws Health sub-districts with one symbol [67]. The map draws Dense areas of community health worker visits with one symbol [67]. It draws 2,475 features [67]. It covers longitude 32.509 to 32.654 and latitude 0.257 to 0.368, at about 1:66,921 [67]. **Findings** - **Evidenced:** Calculate density on Community health worker visits: Calculate density: 1 layer saved: Calculate density: Community health worker visits (2452 features) [20][66]. - **Evidenced:** It reported result, count 2,452; result, elapsed ms 43; result, bandwidth m 500; result, cell size m 200; result, excluded null 0; result, max density per km2 34.43 [66]. *A model draft was discarded because a sentence did not cite a source of this section.* *Tool gp_calculate_density; parameters {"kernel": "quartic", "project": "2612067c-badd-4310-9678-8f580d5e3d32", "features": {"layer": "Community health worker visits (4d70d1ce)"}, "bandwidth_m": 500.0, "cell_size_m": 200.0}* ## Calculate density **What this analysis is.** Calculate density: Kernel density per square kilometer on a grid [64]. In ArcGIS it is Calculate Density or Kernel Density; in QGIS, native:heatmapkerneldensityestimation [64]. **Why it was run on this data.** 2290 points in Cases: a density surface shows where they concentrate [68]. Measured: Cases are clustered (nearest-neighbor ratio 0.90, z = -9.4), median spacing 75.6 m [68]. **What it found.** The density was calculated using the Cases input [17] and processed 2,508 features [69]. | Result | Value | | --- | --- | | result, count | 2,508 | | result, elapsed ms | 49 | | result, bandwidth m | 500 | | result, cell size m | 200 | | result, excluded null | 0 | | result, max density per km2 | 282 | | billing, cost | 0.0958 | | billing, list price | 0.0958 | | billing, upstream cost | 0 | ![Density: cases](maps/15-density-cases.png) *Figure 26. Density: cases [70]* **What the map shows.** The map draws Calculate density: Cases by density_per_km2 in 6 classes: 9.49 to 19.7, 19.7 to 29.4, 29.4 to 57.9, 57.9 to 111, 111 to 189, 189 to 282 [70]. The map draws Health sub-districts with one symbol [70]. It draws 2,526 features [70]. It covers longitude 32.508 to 32.653 and latitude 0.257 to 0.367, at about 1:66,921 [70]. **Findings** - **Evidenced:** Calculate density on Cases: Calculate density: 1 layer saved: Calculate density: Cases (2508 features) [17][69]. - **Evidenced:** It reported result, count 2,508; result, elapsed ms 49; result, bandwidth m 500; result, cell size m 200; result, excluded null 0; result, max density per km2 282 [69]. *Tool gp_calculate_density; parameters {"kernel": "quartic", "project": "2612067c-badd-4310-9678-8f580d5e3d32", "features": {"layer": "Cases (03c8f72b)"}, "bandwidth_m": 500.0, "cell_size_m": 200.0}* ## Calculate density **What this analysis is.** Calculate density: Kernel density per square kilometer on a grid [64]. In ArcGIS it is Calculate Density or Kernel Density; in QGIS, native:heatmapkerneldensityestimation [64]. **Why it was run on this data.** 200 points in Vaccination sessions: a density surface shows where they concentrate [71]. **What it found.** Calculate density on Vaccination sessions: Calculate density: 1 layer saved: Calculate density: Vaccination sessions (616 features) [22][72]. It reported result, count 616; result, elapsed ms 105; result, bandwidth m 2,000; result, cell size m 500; result, excluded null 0; result, max density per km2 5.525 [72]. | Result | Value | | --- | --- | | result, count | 616 | | result, elapsed ms | 105 | | result, bandwidth m | 2,000 | | result, cell size m | 500 | | result, excluded null | 0 | | result, max density per km2 | 5.525 | | billing, cost | 0.054 | | billing, list price | 0.054 | | billing, upstream cost | 0 | ![Density: vaccination sessions](maps/16-density-vaccination-sessions.png) *Figure 27. Density: vaccination sessions [73]* **What the map shows.** The map draws Calculate density: Vaccination sessions by density_per_km2 in 6 classes: 0.404 to 1.01, 1.01 to 1.59, 1.59 to 2.25, 2.25 to 3.27, 3.27 to 4.47, 4.47 to 5.53 [73]. The map draws Health sub-districts with one symbol [73]. It draws 634 features [73]. It covers longitude 32.49 to 32.674 and latitude 0.241 to 0.382, at about 1:85,295 [73]. **Findings** - **Evidenced:** Calculate density on Vaccination sessions: Calculate density: 1 layer saved: Calculate density: Vaccination sessions (616 features) [22][72]. - **Evidenced:** It reported result, count 616; result, elapsed ms 105; result, bandwidth m 2,000; result, cell size m 500; result, excluded null 0; result, max density per km2 5.525 [72]. *Tool gp_calculate_density; parameters {"kernel": "quartic", "project": "2612067c-badd-4310-9678-8f580d5e3d32", "features": {"layer": "Vaccination sessions (6798c97c)"}, "bandwidth_m": 2000.0, "cell_size_m": 500.0}* ## Find point clusters **What this analysis is.** Find point clusters: Density-based clusters (DBSCAN, HDBSCAN or OPTICS, optionally space-time) with hulls and noise [74]. In ArcGIS it is Find Point Clusters or Density-based Clustering; in QGIS, native:dbscanclustering or native:stdbscanclustering [74]. **Why it was run on this data.** DBSCAN finds dense groups of Community health worker visits and separates noise [75]. Measured: Community health worker visits are clustered (nearest-neighbor ratio 0.95, z = -3.3), median spacing 124 m [75]. DBSCAN at 200 m (twice the measured spacing) [75]. **What it found.** Find point clusters on Community health worker visits: Find point clusters: 1 layer saved: Find point clusters: Community health worker visits (1000 features) [20][76]. It reported result, count 1,000; result, noise 1,000; result, distance m 200; result, elapsed ms 576; result, min points 20; billing, cost 0.07 [76]. | Result | Value | | --- | --- | | result, count | 1,000 | | result, noise | 1,000 | | result, distance m | 200 | | result, elapsed ms | 576 | | result, min points | 20 | | billing, cost | 0.07 | | billing, list price | 0.07 | | billing, upstream cost | 0 | ![Clusters: community health worker visits](maps/17-clusters-community-health-worker-visits.png) *Figure 28. Clusters: community health worker visits [77]* **What the map shows.** The map draws Find point clusters: Community health worker visits with one symbol [77]. It draws 1,000 features [77]. It covers longitude 32.516 to 32.647 and latitude 0.263 to 0.363, at about 1:60,312 [77]. **Findings** - **Evidenced:** Find point clusters on Community health worker visits: Find point clusters: 1 layer saved: Find point clusters: Community health worker visits (1000 features) [20][76]. - **Evidenced:** It reported result, count 1,000; result, noise 1,000; result, distance m 200; result, elapsed ms 576; result, min points 20; billing, cost 0.07 [76]. *Tool gp_find_point_clusters; parameters {"method": "dbscan", "project": "2612067c-badd-4310-9678-8f580d5e3d32", "features": {"layer": "Community health worker visits (4d70d1ce)"}, "distance_m": 200.0, "min_points": 20}* ## Find point clusters **What this analysis is.** Find point clusters: Density-based clusters (DBSCAN, HDBSCAN or OPTICS, optionally space-time) with hulls and noise [74]. In ArcGIS it is Find Point Clusters or Density-based Clustering; in QGIS, native:dbscanclustering or native:stdbscanclustering [74]. **Why it was run on this data.** DBSCAN finds dense groups of Cases and separates noise [78]. Measured: Cases are clustered (nearest-neighbor ratio 0.90, z = -9.4), median spacing 75.6 m [78]. DBSCAN at 200 m (twice the measured spacing) [78]. **What it found.** Find point clusters on Cases: Find point clusters: 2 layers saved: Find point clusters: Cases (2290 features); Find point clusters: cluster hulls (1 feature) [17][79]. It reported result, count 2,290; result, noise 1,961; result, distance m 200; result, elapsed ms 24; result, min points 20; billing, cost 0.0958 [79]. | Result | Value | | --- | --- | | result, count | 2,290 | | result, noise | 1,961 | | result, distance m | 200 | | result, elapsed ms | 24 | | result, min points | 20 | | billing, cost | 0.0958 | | billing, list price | 0.0958 | | billing, upstream cost | 0 | ![Clusters: cases](maps/18-clusters-cases.png) *Figure 29. Clusters: cases [80]* **What the map shows.** The map draws Find point clusters: cluster hulls with one symbol [80]. The map draws Find point clusters: Cases by cluster_id in 2 classes: Cluster 0, Noise [80]. It draws 2,291 features [80]. It covers longitude 32.516 to 32.647 and latitude 0.262 to 0.363, at about 1:60,732 [80]. **Findings** - **Evidenced:** Find point clusters on Cases: Find point clusters: 2 layers saved: Find point clusters: Cases (2290 features); Find point clusters: cluster hulls (1 feature) [17][79]. - **Evidenced:** It reported result, count 2,290; result, noise 1,961; result, distance m 200; result, elapsed ms 24; result, min points 20; billing, cost 0.0958 [79]. *Tool gp_find_point_clusters; parameters {"method": "dbscan", "project": "2612067c-badd-4310-9678-8f580d5e3d32", "features": {"layer": "Cases (03c8f72b)"}, "distance_m": 200.0, "min_points": 20}* ## Outliers **What this analysis is.** Find outliers: Anselin local Moran’s I: HH, LL clusters and HL, LH outliers [81]. In ArcGIS it is Find Outliers or Cluster and Outlier Analysis (Anselin Local Moran’s I) [81]. **Why it was run on this data.** Local Moran's I maps clusters and outliers of 'population' [82]. Measured: Health sub-districts have 39 neighboring pairs (4.33 neighbors per area, 0 isolated), so neighbors can be compared [82]. **What it found.** The outlier analysis process used Health sub-districts as input data [16]. The operation, which saved 1 layer for Health sub-districts, successfully analyzed 18 features [83]. | Result | Value | | --- | --- | | result, count | 18 | | result, elapsed ms | 2 | | result, permutations | 199 | | result, excluded null | 0 | | result, distance band m | 2,467 | | billing, cost | 0.05054 | | billing, list price | 0.05054 | | billing, upstream cost | 0 | ![Outliers: health sub-districts](maps/19-outliers-health-sub-districts.png) *Figure 30. Outliers: health sub-districts [84]* **What the map shows.** The map draws Find outliers: Health sub-districts by category in 5 classes: High-high cluster, High-low outlier, Low-high outlier, Low-low cluster, Not significant [84]. It draws 18 features [84]. It covers longitude 32.515 to 32.648 and latitude 0.261 to 0.363, at about 1:61,580 [84]. **Findings** - **Evidenced:** Outliers on Health sub-districts: Find outliers: 1 layer saved: Find outliers: Health sub-districts (18 features) [16][83]. - **Evidenced:** It reported result, count 18; result, elapsed ms 2; result, permutations 199; result, excluded null 0; result, distance band m 2,467; billing, cost 0.05054 [83]. *Tool gp_outliers; parameters {"field": "population", "project": "2612067c-badd-4310-9678-8f580d5e3d32", "features": {"layer": "Health sub-districts (433b88cc)"}, "aggregation": "hexagon"}* ## What follows from the results Change beside the counts per health sub-district, from Period change: Cases (week of 2026-05-18 to week of 2026-05-25) and Summarize within: Health sub-districts: the busiest area, Parkside sub-district (32 in all), changed by -38 [87][13][46]. Change of vaccination sessions where density is high, from Period change: Vaccination sessions (week of 2026-06-29 to week of 2026-07-06) and Calculate density: Community health worker visits: the largest rise (+1, Canal Side sub-district) is where density averages 10.88 per square kilometer [92][11][66]. Hot cells of cases per health sub-district, from Hot spot analysis: Cases: 8 of 18 areas hold hot spot cells; Parkside sub-district holds the most (103) [97][37]. Nearest outbreak response team to each cluster of cases, from Find point clusters: cluster hulls: the 1 clusters lie 0 to 0 m from the nearest facility (Rapid response team 1 and others) [103][79]. Dense areas of community health worker visits, from Calculate density: Community health worker visits: the densest of five classes joins 491 cells into one area [110][66]. Change of vaccination sessions per health sub-district, from Period change: Vaccination sessions (week of 2026-06-29 to week of 2026-07-06): Canal Side sub-district rose the most (+1) and Fernhill sub-district fell the most (-2) [113][11]. Outbreak response teams within 250 m of each cluster of cases, from Find point clusters: cluster hulls: 1 of 1 clusters have one within 250 m [118][79]. Density of community health worker visits and cases together, from Calculate density: Community health worker visits and Calculate density: Cases: 4960 features from 2 sources [123][66][69]. **Findings** - **Evidenced:** Change beside the counts per health sub-district, from Period change: Cases (week of 2026-05-18 to week of 2026-05-25) and Summarize within: Health sub-districts: the busiest area, Parkside sub-district (32 in all), changed by -38 [87][13][46]. - **Evidenced:** Change of vaccination sessions where density is high, from Period change: Vaccination sessions (week of 2026-06-29 to week of 2026-07-06) and Calculate density: Community health worker visits: the largest rise (+1, Canal Side sub-district) is where density averages 10.88 per square kilometer [92][11][66]. - **Evidenced:** Hot cells of cases per health sub-district, from Hot spot analysis: Cases: 8 of 18 areas hold hot spot cells; Parkside sub-district holds the most (103) [97][37]. - **Evidenced:** Nearest outbreak response team to each cluster of cases, from Find point clusters: cluster hulls: the 1 clusters lie 0 to 0 m from the nearest facility (Rapid response team 1 and others) [103][79]. - **Evidenced:** Dense areas of community health worker visits, from Calculate density: Community health worker visits: the densest of five classes joins 491 cells into one area [110][66]. - **Evidenced:** Change of vaccination sessions per health sub-district, from Period change: Vaccination sessions (week of 2026-06-29 to week of 2026-07-06): Canal Side sub-district rose the most (+1) and Fernhill sub-district fell the most (-2) [113][11]. - **Evidenced:** Outbreak response teams within 250 m of each cluster of cases, from Find point clusters: cluster hulls: 1 of 1 clusters have one within 250 m [118][79]. - **Evidenced:** Density of community health worker visits and cases together, from Calculate density: Community health worker visits and Calculate density: Cases: 4960 features from 2 sources [123][66][69]. ## Change beside the counts per health sub-district **What this analysis is.** Spatial join: Attach attributes (or aggregates) of matching features from another layer, by location, distance, time and attributes [85]. In ArcGIS it is Join Features or Spatial Join; in QGIS, native:joinattributesbylocation or native:joinbylocationsummary [85]. **Why it was run on this data.** It was run toward the goal "Describe the project Sample: Public health, Kampala: run the analyses that tell the most about its data, draw every result, and write a cited report" [86]. **What it found.** Spatial join on Summarize within: Health sub-districts, Period change: Cases (week of 2026-05-18 to week of 2026-05-25) produced Change beside the counts per health sub-district (18 features) [14][87][88][89]. It reported result, count 18; result, elapsed ms 1; billing, cost 0.01074; billing, list price 0.01074; billing, upstream cost 0 [87]. The chart Change beside the counts per health sub-district is drawn from 6 rows [91]. | Result | Value | | --- | --- | | result, count | 18 | | result, elapsed ms | 1 | | billing, cost | 0.01074 | | billing, list price | 0.01074 | | billing, upstream cost | 0 | ![Change beside the counts per health sub-district](maps/20-change-beside-the-counts-per-health-sub-district.png) *Figure 31. Change beside the counts per health sub-district [90]* **What the map shows.** The map draws Change beside the counts per health sub-district by count in 5 classes: 3 to 7, 7 to 11, 11 to 19, 19 to 27.5, 27.5 to 32 [90]. It draws 18 features [90]. It covers longitude 32.515 to 32.648 and latitude 0.261 to 0.363, at about 1:61,580 [90]. ![Change beside the counts per health sub-district](charts/27-change-beside-the-counts-per-health-sub-district.png) *Figure 32. Change beside the counts per health sub-district [91]. Source: Change beside the counts per health sub-district (18 features). The data is in the appendix.* **Findings** - **Evidenced:** Spatial join on Summarize within: Health sub-districts, Period change: Cases (week of 2026-05-18 to week of 2026-05-25) produced Change beside the counts per health sub-district (18 features) [14][87][88][89]. - **Evidenced:** It reported result, count 18; result, elapsed ms 1; billing, cost 0.01074; billing, list price 0.01074; billing, upstream cost 0 [87]. - **Evidenced:** The chart Change beside the counts per health sub-district is drawn from 6 rows [91]. *Tool gp_spatial_join; parameters {"join": {"layer": "Summarize within: Health sub-districts (9220b574)"}, "fields": ["count"], "target": {"layer": "Period change: Cases (week of 2026-05-18 to week of 2026-05-25) (66f934f7)"}, "project": "2612067c-badd-4310-9678-8f580d5e3d32", "predicate": "largest_overlap", "output_name": "Change beside the counts per health sub-district"}* ## Change of vaccination sessions where density is high **What this analysis is.** Spatial join: Attach attributes (or aggregates) of matching features from another layer, by location, distance, time and attributes [85]. In ArcGIS it is Join Features or Spatial Join; in QGIS, native:joinattributesbylocation or native:joinbylocationsummary [85]. **Why it was run on this data.** It was run toward the goal "Describe the project Sample: Public health, Kampala: run the analyses that tell the most about its data, draw every result, and write a cited report" [86]. **What it found.** The analysis utilized a spatial join [92], integrating data derived from Calculate density: Community health worker visits [93] and the specific period change for Vaccination sessions (week of 2026-06- | Result | Value | | --- | --- | | result, count | 10 | | result, elapsed ms | 3 | | billing, cost | 0.05926 | | billing, list price | 0.05926 | | billing, upstream cost | 0 | ![Change of vaccination sessions where density is high](maps/21-change-of-vaccination-sessions-where-density-is-high.png) *Figure 33. Change of vaccination sessions where density is high [95]* **What the map shows.** The map draws Change of vaccination sessions where density is high by join_count in 5 classes: 75 to 126, 126 to 151, 151 to 218, 218 to 292, 292 to 366 [95]. The map draws Health sub-districts with one symbol [95]. It draws 28 features [95]. It covers longitude 32.515 to 32.648 and latitude 0.261 to 0.363, at about 1:61,580 [95]. ![Change of vaccination sessions where density is high](charts/28-change-of-vaccination-sessions-where-density-is-high.png) *Figure 34. Change of vaccination sessions where density is high [96]. Source: Change of vaccination sessions where density is high (10 features). The data is in the appendix.* **Findings** - **Evidenced:** Spatial join on Calculate density: Community health worker visits, Period change: Vaccination sessions (week of 2026-06-29 to week of 2026-07-06) produced Change of vaccination sessions where density is high (10 features) [12][92][93][94]. - **Evidenced:** It reported result, count 10; result, elapsed ms 3; billing, cost 0.05926; billing, list price 0.05926; billing, upstream cost 0 [92]. - **Evidenced:** The chart Change of vaccination sessions where density is high is drawn from 5 rows [96]. *Tool gp_spatial_join; parameters {"join": {"layer": "Calculate density: Community health worker visits (3aeb4280)"}, "stats": [{"agg": "mean", "field": "density_per_km2"}], "target": {"layer": "Period change: Vaccination sessions (week of 2026-06-29 to week of 2026-07-06) (3345972d)"}, "project": "2612067c-badd-4310-9678-8f580d5e3d32", "predicate": "intersects", "output_name": "Change of vaccination sessions where density is high* ## Hot cells of cases per health sub-district **What this analysis is.** Summarize within: Count and aggregate the features inside each polygon (or bin), proportionally for lines and polygons [42]. In ArcGIS it is Summarize Within or Aggregate Points; in QGIS, native:countpointsinpolygon or native:sumlinelengths [42]. **Why it was run on this data.** Count Community health worker visits inside each feature of Health sub-districts by purpose; the counts map as a choropleth (natural breaks, sequential colors) [43]. **What it found.** Summarize within on Hot spot analysis: Cases, Health sub-districts produced Hot cells of cases per health sub-district (18 features) [16][97][98][99]. It reported result, count 18; result, elapsed ms 38; billing, cost 0.05554; billing, list price 0.05554; billing, upstream cost 0 [97]. The chart Hot cells of cases per health sub-district is drawn from 6 rows [101]. | Result | Value | | --- | --- | | result, count | 18 | | result, elapsed ms | 38 | | billing, cost | 0.05554 | | billing, list price | 0.05554 | | billing, upstream cost | 0 | ![Hot cells of cases per health sub-district](maps/22-hot-cells-of-cases-per-health-sub-district.png) *Figure 35. Hot cells of cases per health sub-district [100]* **What the map shows.** The map draws Hot cells of cases per health sub-district by count in 5 classes: 73 to 96, 96 to 151, 151 to 215, 215 to 290, 290 to 358 [100]. It draws 18 features [100]. It covers longitude 32.515 to 32.648 and latitude 0.261 to 0.363, at about 1:61,580 [100]. ![Hot cells of cases per health sub-district](charts/29-hot-cells-of-cases-per-health-sub-district.png) *Figure 36. Hot cells of cases per health sub-district [101]. Source: Hot cells of cases per health sub-district (18 features). The data is in the appendix.* **Findings** - **Evidenced:** Summarize within on Hot spot analysis: Cases, Health sub-districts produced Hot cells of cases per health sub-district (18 features) [16][97][98][99]. - **Evidenced:** It reported result, count 18; result, elapsed ms 38; billing, cost 0.05554; billing, list price 0.05554; billing, upstream cost 0 [97]. - **Evidenced:** The chart Hot cells of cases per health sub-district is drawn from 6 rows [101]. *Tool gp_summarize_within; parameters {"project": "2612067c-badd-4310-9678-8f580d5e3d32", "features": {"layer": "Hot spot analysis: Cases (7dc9eb55)"}, "group_by": "gi_bin", "polygons": {"layer": "Health sub-districts (433b88cc)"}, "output_name": "Hot cells of cases per health sub-district"}* ## Nearest outbreak response team to each cluster of cases **What this analysis is.** Find nearest: The k nearest features for each input, with geodesic distance and bearing [102]. In ArcGIS it is Find Nearest or Near; in QGIS, native:joinbynearest or native:nearestneighbouranalysis [102]. **Why it was run on this data.** It was run toward the goal "Describe the project Sample: Public health, Kampala: run the analyses that tell the most about its data, draw every result, and write a cited report" [86]. **What it found.** The `gp_find_nearest` tool was executed using the Outbreak response teams input data [21]. This analysis used the cluster hulls from [104] to determine the nearest outbreak response team to each cluster of cases [105]. The operation generated one result, completing in 2 [103] milliseconds and incurring a cost of 0.01014 [103]. A chart detailing the nearest outbreak response team to each cluster of cases was generated, containing 1 row [107]. | Result | Value | | --- | --- | | result, count | 1 | | result, elapsed ms | 2 | | billing, cost | 0.01014 | | billing, list price | 0.01014 | | billing, upstream cost | 0 | ![Nearest outbreak response team to each cluster of cases](maps/23-nearest-outbreak-response-team-to-each-cluster-of-cases.png) *Figure 37. Nearest outbreak response team to each cluster of cases [106]* **What the map shows.** The map draws Nearest outbreak response team to each cluster of cases with one symbol [106]. The map draws Health sub-districts with one symbol [106]. It draws 19 features [106]. It covers longitude 32.585 to 32.606 and latitude 0.289 to 0.305, at about 1:10,087 [106]. ![Nearest outbreak response team to each cluster of cases](charts/30-nearest-outbreak-response-team-to-each-cluster-of-cases.png) *Figure 38. Nearest outbreak response team to each cluster of cases [107]. Source: Nearest outbreak response team to each cluster of cases (1 features). The data is in the appendix.* **Findings** - **Evidenced:** Find nearest on Outbreak response teams, Find point clusters: cluster hulls produced Nearest outbreak response team to each cluster of cases (1 features) [21][103][104][105]. - **Evidenced:** It reported result, count 1; result, elapsed ms 2; billing, cost 0.01014; billing, list price 0.01014; billing, upstream cost 0 [103]. - **Evidenced:** The chart Nearest outbreak response team to each cluster of cases is drawn from 1 rows [107]. *Tool gp_find_nearest; parameters {"k": 1, "to": {"layer": "Outbreak response teams (1b8f0e74)"}, "from": {"layer": "Find point clusters: cluster hulls (53d9a3e2)"}, "project": "2612067c-badd-4310-9678-8f580d5e3d32", "output_name": "Nearest outbreak response team to each cluster of cases"}* ## Dense areas of community health worker visits **What this analysis is.** Pipeline: Run up to 20 GIS operations in one engine call; a step's params may use "$id" (an earlier step's result) or "$id.key", and layer references [108]. Every step's map outputs are saved as styled layers [108]. **Why it was run on this data.** Health sub-districts holds population in 'population': Community health worker visits per 10,000 people in each feature compares features of different size; mapped as a choropleth (natural breaks, sequential colors) [109]. **What it found.** A pipeline of 2 steps (classify_field > dissolve) was executed, saving 1 layer [110]. | Result | Value | | --- | --- | | result, count | 5 | | result, elapsed ms | 46 | | billing, cost | 0.06905 | | billing, list price | 0.06905 | | billing, upstream cost | 0 | | list price | 0.06905 | **What the map shows.** Drawn with calculate density on the map Density: community health worker visits [67]. ![Dense areas of community health worker visits](charts/31-dense-areas-of-community-health-worker-visits.png) *Figure 39. Dense areas of community health worker visits [112]. Source: Dense areas of community health worker visits (5 features). The data is in the appendix.* **Findings** - **Evidenced:** Pipeline on Calculate density: Community health worker visits produced Dense areas of community health worker visits (5 features) [93][110][111]. - **Evidenced:** It reported result, count 5; result, elapsed ms 46; billing, cost 0.06905; billing, list price 0.06905; billing, upstream cost 0; list price 0.06905 [110]. - **Evidenced:** The chart Dense areas of community health worker visits is drawn from 5 rows [112]. *Tool gp_pipeline; parameters {"save": "final", "steps": [{"id": "classes", "params": {"field": "density_per_km2", "method": "quantile", "classes": 5, "features": {"layer": "Calculate density: Community health worker visits (3aeb4280)"}}, "operation": "classify_field"}, {"id": "areas", "params": {"by": ["class"], "features": "$classes"}, "operation": "dissolve"}], "project": "2612067c-badd-4310-9678-8f580d5e3d32", "output_name* ## Change of vaccination sessions per health sub-district **What this analysis is.** Summarize within: Count and aggregate the features inside each polygon (or bin), proportionally for lines and polygons [42]. In ArcGIS it is Summarize Within or Aggregate Points; in QGIS, native:countpointsinpolygon or native:sumlinelengths [42]. **Why it was run on this data.** Count Community health worker visits inside each feature of Health sub-districts by purpose; the counts map as a choropleth (natural breaks, sequential colors) [43]. **What it found.** The analysis utilized the input of 'Health sub-districts' [16] and a 'Period change: Vaccination sessions (week of 2026-06-29 to week of 2026-07-06)' [12] to generate the 'Change of vaccination sessions per health sub-district' [114] using the `gp_summarize_within` tool [113]. This resulted in a count of 18 features for the layer [113]. Furthermore, the chart displaying the change in vaccination sessions is drawn from 6 rows [116]. | Result | Value | | --- | --- | | result, count | 18 | | result, elapsed ms | 1 | | billing, cost | 0.01058 | | billing, list price | 0.01058 | | billing, upstream cost | 0 | ![Change of vaccination sessions per health sub-district](maps/24-change-of-vaccination-sessions-per-health-sub-district.png) *Figure 40. Change of vaccination sessions per health sub-district [115]* **What the map shows.** The map draws Change of vaccination sessions per health sub-district by sum_change in 2 classes: -2 to -1, -1 to 1 [115]. It draws 18 features [115]. It covers longitude 32.515 to 32.648 and latitude 0.261 to 0.363, at about 1:61,580 [115]. ![Change of vaccination sessions per health sub-district](charts/32-change-of-vaccination-sessions-per-health-sub-district.png) *Figure 41. Change of vaccination sessions per health sub-district [116]. Source: Change of vaccination sessions per health sub-district (18 features). The data is in the appendix.* **Findings** - **Evidenced:** Summarize within on Period change: Vaccination sessions (week of 2026-06-29 to week of 2026-07-06), Health sub-districts produced Change of vaccination sessions per health sub-district (18 features) [12][16][113][114]. - **Evidenced:** It reported result, count 18; result, elapsed ms 1; billing, cost 0.01058; billing, list price 0.01058; billing, upstream cost 0 [113]. - **Evidenced:** The chart Change of vaccination sessions per health sub-district is drawn from 6 rows [116]. *Tool gp_summarize_within; parameters {"stats": [{"agg": "sum", "field": "change"}], "project": "2612067c-badd-4310-9678-8f580d5e3d32", "features": {"layer": "Period change: Vaccination sessions (week of 2026-06-29 to week of 2026-07-06) (3345972d)"}, "polygons": {"layer": "Health sub-districts (433b88cc)"}, "output_name": "Change of vaccination sessions per health sub-district"}* ## Outbreak response teams within 250 m of each cluster of cases **What this analysis is.** Pipeline: Run up to 20 GIS operations in one engine call; a step's params may use "$id" (an earlier step's result) or "$id.key", and layer references [108]. Every step's map outputs are saved as styled layers [108]. **Why it was run on this data.** Health sub-districts holds population in 'population': Outbreak response teams per 10,000 people in each feature compares features of different size; mapped as a choropleth (natural breaks, sequential colors) [117]. **What it found.** Pipeline on Find point clusters: cluster hulls, Outbreak response teams produced Outbreak response teams within 250 m of each cluster of cases (1 features) [21][104][118][119]. It reported result, count 1; result, elapsed ms 0; billing, cost 0.02015; billing, list price 0.02015; billing, upstream cost 0; list price 0.02015 [118]. The chart Outbreak response teams within 250 m of each cluster of cases is drawn from 1 rows [121]. | Result | Value | | --- | --- | | result, count | 1 | | result, elapsed ms | 0 | | billing, cost | 0.02015 | | billing, list price | 0.02015 | | billing, upstream cost | 0 | | list price | 0.02015 | ![Outbreak response teams within 250 m of each cluster of cases](maps/25-outbreak-response-teams-within-250-m-of-each-cluster-of-case.png) *Figure 42. Outbreak response teams within 250 m of each cluster of cases [120]* **What the map shows.** The map draws Outbreak response teams within 250 m of each cluster of cases with one symbol [120]. The map draws Health sub-districts with one symbol [120]. It draws 19 features [120]. It covers longitude 32.581 to 32.61 and latitude 0.286 to 0.308, at about 1:13,495 [120]. ![Outbreak response teams within 250 m of each cluster of cases](charts/33-outbreak-response-teams-within-250-m-of-each-cluster-of-case.png) *Figure 43. Outbreak response teams within 250 m of each cluster of cases [121]. Source: Outbreak response teams within 250 m of each cluster of cases (1 features). The data is in the appendix.* **Findings** - **Evidenced:** Pipeline on Find point clusters: cluster hulls, Outbreak response teams produced Outbreak response teams within 250 m of each cluster of cases (1 features) [21][104][118][119]. - **Evidenced:** It reported result, count 1; result, elapsed ms 0; billing, cost 0.02015; billing, list price 0.02015; billing, upstream cost 0; list price 0.02015 [118]. - **Evidenced:** The chart Outbreak response teams within 250 m of each cluster of cases is drawn from 1 rows [121]. *Tool gp_pipeline; parameters {"save": "final", "steps": [{"id": "buffer", "params": {"features": {"layer": "Find point clusters: cluster hulls (53d9a3e2)"}, "distance_m": 250}, "operation": "buffer"}, {"id": "count", "params": {"features": {"layer": "Outbreak response teams (1b8f0e74)"}, "polygons": "$buffer"}, "operation": "summarize_within"}], "project": "2612067c-badd-4310-9678-8f580d5e3d32", "output_name": "Outbreak respo* ## Density of community health worker visits and cases together **What this analysis is.** Merge layers: Append several layers into one [122]. In ArcGIS it is Merge Layers or Merge; in QGIS, native:mergevectorlayers [122]. **Why it was run on this data.** It was run toward the goal "Describe the project Sample: Public health, Kampala: run the analyses that tell the most about its data, draw every result, and write a cited report" [86]. **What it found.** The `gp_merge_layers` tool combined inputs for Community health worker visits [93] and Cases [124] to create the merged layer [123]. | Result | Value | | --- | --- | | result, count | 4,960 | | result, elapsed ms | 109 | | billing, cost | 0.01 | | billing, list price | 0.01 | | billing, upstream cost | 0 | ![Density of community health worker visits and cases together](charts/34-density-of-community-health-worker-visits-and-cases-together.png) *Figure 44. Density of community health worker visits and cases together [126]. Source: Density of community health worker visits and cases together (4,960 features). The data is in the appendix.* **Findings** - **Evidenced:** Merge layers on Calculate density: Community health worker visits, Calculate density: Cases produced Density of community health worker visits and cases together (4,960 features) [93][123][124][125]. - **Evidenced:** It reported result, count 4,960; result, elapsed ms 109; billing, cost 0.01; billing, list price 0.01; billing, upstream cost 0 [123]. - **Evidenced:** The chart Density of community health worker visits and cases together is drawn from 14 rows [126]. *Tool gp_merge_layers; parameters {"layers": [{"layer": "Calculate density: Community health worker visits (3aeb4280)"}, {"layer": "Calculate density: Cases (b48a2b54)"}], "project": "2612067c-badd-4310-9678-8f580d5e3d32", "output_name": "Density of community health worker visits and cases together", "source_field": "source"}* ## Maps and charts The run also made 1 map image(s) [127] and 19 chart(s) [128][129][130][131][132][133][134][135][136][137][138][139][140][141][142][143][144][145][146]. ![Health sub-districts by population](maps/01-health-sub-districts-by-population.png) *Figure 45. Health sub-districts by population [127]* ![Cases over time (onset_date)](charts/07-cases-over-time-onset-date.png) *Figure 46. Cases over time (onset_date) [128]. Per week, 2025-09-29 to 2026-09-28. Source: Cases (2,290 features). The data is in the appendix.* ![Clinic stock by month over time (month)](charts/08-clinic-stock-by-month-over-time-month.png) *Figure 47. Clinic stock by month over time (month) [129]. Per week, 2025-12-29 to 2026-08-31. Source: Clinic stock by month (315 features). The data is in the appendix.* ![Emerging hot spots: Community health worker visits](charts/09-emerging-hot-spots-community-health-worker-visits.png) *Figure 48. Emerging hot spots: Community health worker visits [130]. Source: Emerging hot spots: Community health worker visits (124 features). The data is in the appendix.* ![Emerging hot spots: Vaccination sessions](charts/10-emerging-hot-spots-vaccination-sessions.png) *Figure 49. Emerging hot spots: Vaccination sessions [131]. Source: Emerging hot spots: Vaccination sessions (24 features). The data is in the appendix.* ![Emerging hot spots: Cases](charts/11-emerging-hot-spots-cases.png) *Figure 50. Emerging hot spots: Cases [132]. Source: Emerging hot spots: Cases (125 features). The data is in the appendix.* ![Hot spot analysis: Community health worker visits](charts/12-hot-spot-analysis-community-health-worker-visits.png) *Figure 51. Hot spot analysis: Community health worker visits [133]. Source: Hot spot analysis: Community health worker visits (390 features). The data is in the appendix.* ![Hot spot analysis: Cases](charts/13-hot-spot-analysis-cases.png) *Figure 52. Hot spot analysis: Cases [134]. Source: Hot spot analysis: Cases (2,259 features). The data is in the appendix.* ![Hot spot analysis: Vaccination sessions](charts/14-hot-spot-analysis-vaccination-sessions.png) *Figure 53. Hot spot analysis: Vaccination sessions [135]. Source: Hot spot analysis: Vaccination sessions (99 features). The data is in the appendix.* ![Summarize within: Health sub-districts](charts/15-summarize-within-health-sub-districts.png) *Figure 54. Summarize within: Health sub-districts [136]. Source: Summarize within: Health sub-districts (18 features). The data is in the appendix.* ![Summarize within: Health sub-districts](charts/16-summarize-within-health-sub-districts.png) *Figure 55. Summarize within: Health sub-districts [137]. Source: Summarize within: Health sub-districts (18 features). The data is in the appendix.* ![Summarize within: Health sub-districts](charts/17-summarize-within-health-sub-districts.png) *Figure 56. Summarize within: Health sub-districts [138]. Source: Summarize within: Health sub-districts (18 features). The data is in the appendix.* ![Calculate density: Community health worker visits](charts/21-calculate-density-community-health-worker-visits.png) *Figure 57. Calculate density: Community health worker visits [139]. Source: Calculate density: Community health worker visits (2,452 features). The data is in the appendix.* ![Calculate density: Cases](charts/22-calculate-density-cases.png) *Figure 58. Calculate density: Cases [140]. Source: Calculate density: Cases (2,508 features). The data is in the appendix.* ![Calculate density: Vaccination sessions](charts/23-calculate-density-vaccination-sessions.png) *Figure 59. Calculate density: Vaccination sessions [141]. Source: Calculate density: Vaccination sessions (616 features). The data is in the appendix.* ![Find point clusters: Cases](charts/24-find-point-clusters-cases.png) *Figure 60. Find point clusters: Cases [142]. Period: 2025-10-01 to 2026-09-30 (onset_date). Source: Find point clusters: Cases (2,290 features). The data is in the appendix.* ![Find point clusters: cluster hulls](charts/25-find-point-clusters-cluster-hulls.png) *Figure 61. Find point clusters: cluster hulls [143]. Source: Find point clusters: cluster hulls (1 features). The data is in the appendix.* ![Find outliers: Health sub-districts](charts/26-find-outliers-health-sub-districts.png) *Figure 62. Find outliers: Health sub-districts [144]. Source: Find outliers: Health sub-districts (18 features). The data is in the appendix.* ![Find point clusters: Cases over time](charts/35-find-point-clusters-cases-over-time.png) *Figure 63. Find point clusters: Cases over time [145]. Per week, 2025-09-29 to 2026-09-28. Source: Find point clusters: Cases (2,290 features). The data is in the appendix.* ![Find point clusters: Community health worker visits over time](charts/36-find-point-clusters-community-health-worker-visits-over-time.png) *Figure 64. Find point clusters: Community health worker visits over time [146]. Per week, 2026-03-30 to 2026-09-28. Source: Find point clusters: Community health worker visits (1,000 features). The data is in the appendix.* ## More maps 1 more map(s), beyond the 24 the report body holds, in the composer's order: Density of community health worker visits and cases together [147]. ![Density of community health worker visits and cases together](maps/26-density-of-community-health-worker-visits-and-cases-together.png) *Figure 65. Density of community health worker visits and cases together [147]* ## Documents **What the documents name.** Epidemiological situation report (1 page, 1 page(s) from the text layer) names 1 organization, 4 event terms, 6 places and 1 quantity [148]. Organizations named in Epidemiological situation report: Malaria (page 1, line 5) [149]. Event terms in Epidemiological situation report: cholera (page 1, line 3, 2 mentions), outbreak (page 1, line 3, 2 mentions) [149]. Places named in Epidemiological situation report: Parkside sub-district (page 1, line 3, 2 mentions), 029731, 3259548 (page 1, line 3), Rapid response team 1 (page 1, line 11), Eastfield sub-district (page 1, line 12) [149]. | Result | Value | | --- | --- | | Documents | 1 | | Entities | 12 | | Organizations | 1 | | Event terms | 4 | | Places | 6 | | Quantities | 1 | **Findings** - **Evidenced:** Epidemiological situation report (1 page, 1 page(s) from the text layer) names 1 organization, 4 event terms, 6 places and 1 quantity. - **Evidenced:** Organizations named in Epidemiological situation report: Malaria (page 1, line 5). - **Evidenced:** Event terms in Epidemiological situation report: cholera (page 1, line 3, 2 mentions), outbreak (page 1, line 3, 2 mentions). - **Evidenced:** Places named in Epidemiological situation report: Parkside sub-district (page 1, line 3, 2 mentions), 029731, 3259548 (page 1, line 3), Rapid response team 1 (page 1, line 11), Eastfield sub-district (page 1, line 12). ## Changes since the last run 89 number(s) changed since the report of 2026-10-19T04:03:52Z. | Analysis | Measure | Before | After | Change | | --- | --- | --- | --- | --- | | Space time hot spots | result, count | 24 | 124 | 100 | | Space time hot spots | result, elapsed ms | 4 | 17 | 13 | | Space time hot spots | result, resolution | 7 | 8 | 1 | | Space time hot spots | result, features used | 200 | 1,000 | 800 | | Space time hot spots | billing, cost | 0.11 | 0.15 | 0.04 | | Space time hot spots | billing, list price | 0.11 | 0.15 | 0.04 | | Space time hot spots | result, elapsed ms | 4 | 5 | 1 | | Space time hot spots | result, count | 24 | 125 | 101 | | Space time hot spots | result, elapsed ms | 4 | 26 | 22 | | Space time hot spots | result, resolution | 7 | 8 | 1 | | Space time hot spots | result, features used | 200 | 2,290 | 2,090 | | Space time hot spots | billing, cost | 0.11 | 0.2145 | 0.1045 | | Space time hot spots | billing, list price | 0.11 | 0.2145 | 0.1045 | | Hot spots | result, bins | 93 | 390 | 297 | | Hot spots | result, count | 93 | 390 | 297 | | Hot spots | result, elapsed ms | 4 | 17 | 13 | | Hot spots | result, cell size m | 1,000 | 500 | -500 | | Hot spots | result, incidents used | 150 | 1,000 | 850 | | Hot spots | result, distance band m | 2,000 | 1,000 | -1,000 | | Hot spots | billing, cost | 0.053 | 0.07 | 0.017 | | Hot spots | billing, list price | 0.053 | 0.07 | 0.017 | | Hot spots | result, bins | 93 | 2,259 | 2,166 | | Hot spots | result, count | 93 | 2,259 | 2,166 | | Hot spots | result, elapsed ms | 4 | 75 | 71 | | Hot spots | result, cell size m | 1,000 | 200 | -800 | | Hot spots | result, incidents used | 150 | 2,290 | 2,140 | | Hot spots | result, distance band m | 2,000 | 500 | -1,500 | | Hot spots | billing, cost | 0.053 | 0.0958 | 0.0428 | | Hot spots | billing, list price | 0.053 | 0.0958 | 0.0428 | | Hot spots | result, bins | 93 | 99 | 6 | | Hot spots | result, count | 93 | 99 | 6 | | Hot spots | result, elapsed ms | 4 | 5 | 1 | | Hot spots | result, incidents used | 150 | 200 | 50 | | Hot spots | billing, cost | 0.053 | 0.054 | 0.001 | | Hot spots | billing, list price | 0.053 | 0.054 | 0.001 | | Summarize within | result, elapsed ms | 1 | 2 | 1 | | Summarize within | billing, cost | 0.01058 | 0.03036 | 0.01978 | | Summarize within | billing, list price | 0.01058 | 0.03036 | 0.01978 | | Summarize within | result, elapsed ms | 1 | 0 | -1 | | Summarize within | billing, cost | 0.01058 | 0.01438 | 0.0038 | | Summarize within | billing, list price | 0.01058 | 0.01438 | 0.0038 | | Summarize within | result, elapsed ms | 1 | 3 | 2 | | Summarize within | billing, cost | 0.01058 | 0.05616 | 0.04558 | | Summarize within | billing, list price | 0.01058 | 0.05616 | 0.04558 | | Calculate density | result, count | 558 | 2,452 | 1,894 | | Calculate density | result, elapsed ms | 11 | 43 | 32 | | Calculate density | result, bandwidth m | 2,000 | 500 | -1,500 | | Calculate density | result, cell size m | 500 | 200 | -300 | | Calculate density | result, max density per km2 | 6.956 | 34.43 | 27.47 | | Calculate density | billing, cost | 0.053 | 0.07 | 0.017 | | Calculate density | billing, list price | 0.053 | 0.07 | 0.017 | | Calculate density | result, count | 558 | 2,508 | 1,950 | | Calculate density | result, elapsed ms | 11 | 49 | 38 | | Calculate density | result, bandwidth m | 2,000 | 500 | -1,500 | | Calculate density | result, cell size m | 500 | 200 | -300 | | Calculate density | result, max density per km2 | 6.956 | 282 | 275.1 | | Calculate density | billing, cost | 0.053 | 0.0958 | 0.0428 | | Calculate density | billing, list price | 0.053 | 0.0958 | 0.0428 | | Calculate density | result, count | 558 | 616 | 58 | | Calculate density | result, elapsed ms | 11 | 105 | 94 | | Calculate density | result, max density per km2 | 6.956 | 5.525 | -1.431 | | Calculate density | billing, cost | 0.053 | 0.054 | 0.001 | | Calculate density | billing, list price | 0.053 | 0.054 | 0.001 | | Find point clusters | result, count | 2,290 | 1,000 | -1,290 | | Find point clusters | result, noise | 1,961 | 1,000 | -961 | | Find point clusters | result, elapsed ms | 23 | 576 | 553 | | Find point clusters | billing, cost | 0.0958 | 0.07 | -0.0258 | | Find point clusters | billing, list price | 0.0958 | 0.07 | -0.0258 | | Find point clusters | result, elapsed ms | 23 | 24 | 1 | | Change beside the counts per health sub-district | result, count | 10 | 18 | 8 | | Change beside the counts per health sub-district | result, elapsed ms | 2 | 1 | -1 | | Change beside the counts per health sub-district | billing, cost | 0.06038 | 0.01074 | -0.04964 | | Change beside the counts per health sub-district | billing, list price | 0.06038 | 0.01074 | -0.04964 | | Change of vaccination sessions where density is high | result, elapsed ms | 2 | 3 | 1 | | Change of vaccination sessions where density is high | billing, cost | 0.06038 | 0.05926 | -0.00112 | | Change of vaccination sessions where density is high | billing, list price | 0.06038 | 0.05926 | -0.00112 | | Hot cells of cases per health sub-district | result, elapsed ms | 1 | 38 | 37 | | Hot cells of cases per health sub-district | billing, cost | 0.01058 | 0.05554 | 0.04496 | | Hot cells of cases per health sub-district | billing, list price | 0.01058 | 0.05554 | 0.04496 | | Dense areas of community health worker visits | result, count | 1 | 5 | 4 | | Dense areas of community health worker visits | result, elapsed ms | 0 | 46 | 46 | | Dense areas of community health worker visits | billing, cost | 0.02015 | 0.06905 | 0.0489 | | Dense areas of community health worker visits | billing, list price | 0.02015 | 0.06905 | 0.0489 | | Dense areas of community health worker visits | list price | 0.02015 | 0.06905 | 0.0489 | | Density of community health worker visits and cases together | result, count | 2,352 | 4,960 | 2,608 | | Density of community health worker visits and cases together | result, elapsed ms | 58 | 109 | 51 | | Documents | Entities | 1 | 12 | 11 | | Documents | Event terms | 1 | 4 | 3 | | Documents | Places | 1 | 6 | 5 | New findings: - Calculate density on Vaccination sessions: Calculate density: 1 layer saved: Calculate density: Vaccination sessions (616 features). - Change beside the counts per health sub-district, from Period change: Cases (week of 2026-05-18 to week of 2026-05-25) and Summarize within: Health sub-districts: the busiest area, Parkside sub-district (32 in all), changed by -38 [87][13][46]. - Change of vaccination sessions per health sub-district, from Period change: Vaccination sessions (week of 2026-06-29 to week of 2026-07-06): Canal Side sub-district rose the most (+1) and Fernhill sub-district fell the most (-2) [113][11]. - Change of vaccination sessions where density is high, from Period change: Vaccination sessions (week of 2026-06-29 to week of 2026-07-06) and Calculate density: Community health worker visits: the largest rise (+1, Canal Side sub-district) is where density averages 10.88 per square kilometer [92][11][66]. - Dense areas of community health worker visits, from Calculate density: Community health worker visits: the densest of five classes joins 491 cells into one area [110][66]. - Density of community health worker visits and cases together, from Calculate density: Community health worker visits and Calculate density: Cases: 4960 features from 2 sources [123][66][69]. - Hot spots on Vaccination sessions: Hot spot analysis: 1 layer saved: Hot spot analysis: Vaccination sessions (99 features). - It reported result, bins 2,259; result, count 2,259; result, elapsed ms 75; result, cell size m 200; result, incidents used 2,290; result, distance band m 500. - It reported result, bins 99; result, count 99; result, elapsed ms 5; result, cell size m 1,000; result, incidents used 200; result, distance band m 2,000. - It reported result, count 1,000; result, noise 1,000; result, distance m 200; result, elapsed ms 576; result, min points 20; billing, cost 0.07. - It reported result, count 10; result, elapsed ms 3; billing, cost 0.05926; billing, list price 0.05926; billing, upstream cost 0. - It reported result, count 125; result, elapsed ms 26; result, resolution 8; result, features used 2,290; result, time neighbors 0; billing, cost 0.2145. - It reported result, count 18; result, elapsed ms 0; billing, cost 0.01438; billing, list price 0.01438; billing, upstream cost 0. - It reported result, count 18; result, elapsed ms 38; billing, cost 0.05554; billing, list price 0.05554; billing, upstream cost 0. - It reported result, count 2,290; result, noise 1,961; result, distance m 200; result, elapsed ms 24; result, min points 20; billing, cost 0.0958. - It reported result, count 2,452; result, elapsed ms 43; result, bandwidth m 500; result, cell size m 200; result, excluded null 0; result, max density per km2 34.43. - It reported result, count 2,508; result, elapsed ms 49; result, bandwidth m 500; result, cell size m 200; result, excluded null 0; result, max density per km2 282. - It reported result, count 24; result, elapsed ms 5; result, resolution 7; result, features used 200; result, time neighbors 0; billing, cost 0.11. - It reported result, count 4,960; result, elapsed ms 109; billing, cost 0.01; billing, list price 0.01; billing, upstream cost 0. - It reported result, count 5; result, elapsed ms 46; billing, cost 0.06905; billing, list price 0.06905; billing, upstream cost 0; list price 0.06905. - It reported result, count 616; result, elapsed ms 105; result, bandwidth m 2,000; result, cell size m 500; result, excluded null 0; result, max density per km2 5.525. - Merge layers on Calculate density: Community health worker visits, Calculate density: Cases produced Density of community health worker visits and cases together (4,960 features). - Outbreak response teams within 250 m of each cluster of cases, from Find point clusters: cluster hulls: 1 of 1 clusters have one within 250 m [118][79]. - Pipeline on Calculate density: Community health worker visits produced Dense areas of community health worker visits (5 features). - Spatial join on Calculate density: Community health worker visits, Period change: Vaccination sessions (week of 2026-06-29 to week of 2026-07-06) produced Change of vaccination sessions where density is high (10 features). - Summarize within on Vaccination sessions, Health sub-districts: Summarize within: 1 layer saved: Summarize within: Health sub-districts (18 features). - The chart Dense areas of community health worker visits is drawn from 5 rows. - The chart Density of community health worker visits and cases together is drawn from 14 rows. ## Sources [1] Rendering: Chart: Timeline: events per week by dataset. 265 rows (id 9ed316de-c13f-4c96-9941-d0ab254813c6) [2] Dataset: Cases. vector, 2290 rows (id 59cb2ed5-709d-4ca6-94aa-fd8eb621eb37) [3] Dataset: Community health worker visits. vector, 1000 rows (id 44c21bd2-0729-457b-ba05-d7a36b9885ed) [4] Dataset: Vaccination sessions. vector, 200 rows (id 46c023c6-ea3b-4a1d-9a6c-130a334b6790) [5] Dataset: Weekly cases by sub-district. table, 936 rows (id 1ce22bbc-4659-446b-a2ab-9d6f1bfe6418) [6] Dataset: Clinic stock by month. table, 315 rows (id 930b3eee-3ebe-4aff-8fa0-a2c38a029a36) [7] Rendering: Chart: Timeline lanes: events per week by dataset. 265 rows (id dc67edf4-bf01-4d54-a054-9cccf4719256) [8] Rendering: Chart: Timeline: change from one week to the next. 52 rows (id b08e07a6-2857-423d-9faf-2edf4b6db093) [9] Tool result: gp_period_change, step 0. The count fell from 36 in week of 2026-09-21 to 18 in week of 2026-09-28 (-50.0%); a change this large is unlikely to be chance (p = 0.0198). (id da0d02de-5858-490e-a2a5-d4daf11b4a77:0) [10] Layer: Period change: Community health worker visits (week of 2026-09-21 to week of 2026-09-28) (id 3bec1b01-80c0-4802-8f6f-e05ccd607485) [11] Tool result: gp_period_change, step 1. The count fell from 9 in week of 2026-06-29 to 2 in week of 2026-07-06 (-77.8%); a change this small is within what chance would give (p = 0.0654). (id da0d02de-5858-490e-a2a5-d4daf11b4a77:1) [12] Layer: Period change: Vaccination sessions (week of 2026-06-29 to week of 2026-07-06) (id 3345972d-58bd-4b02-b103-633f6c7a1c21) [13] Tool result: gp_period_change, step 2. The count fell from 136 in week of 2026-05-18 to 86 in week of 2026-05-25 (-36.8%); a change this large is unlikely to be chance (p = 0.000963). (id da0d02de-5858-490e-a2a5-d4daf11b4a77:2) [14] Layer: Period change: Cases (week of 2026-05-18 to week of 2026-05-25) (id 66f934f7-4d7f-4b71-bfe5-db9cf372b64d) [15] Rendering: Map: Overview: the top 3 results. view longitude 32.48887 to 32.67295, latitude 0.23939 to 0.37643 (id 56c16576-ebcf-441b-9376-fc19268f64e4) [16] Layer: Health sub-districts. spatial profile (id 433b88cc-cf57-4d48-b158-220cc8b77697) [17] Layer: Cases. spatial profile (id 03c8f72b-99c1-4176-8f56-aabff1d062b7) [18] Layer: Clinics. spatial profile (id ac5b28b9-59c1-40e0-b7bd-1b0014ea13e0) [19] Layer: Water points. spatial profile (id c63bde31-7c18-420e-894d-3e824a409c2d) [20] Layer: Community health worker visits. spatial profile (id 4d70d1ce-8162-42eb-b3d7-d864edf2721e) [21] Layer: Outbreak response teams. spatial profile (id 1b8f0e74-8ce3-4ca6-bbf8-e76348e17d6e) [22] Layer: Vaccination sessions. spatial profile (id 6798c97c-ff1d-462f-b9b0-5baab95708a5) [23] Catalog: Emerging hot spots (operation catalog, gp_space_time_hot_spots). Where hot spots are new, consecutive, intensifying, persistent, diminishing, sporadic, oscillating or historical over time. ArcGIS: Emerging Hot Spot Analysis, Create Space Time Cube. (id gp_space_time_hot_spots) [24] Plan: Space time hot spots of Community health worker visits (the Start plan's candidate c_70590e6d2eeb). Community health worker visits carry dates in 'visited_at': emerging hot spot analysis finds new, intensifying and fading hot spots per week. (id 502cfe77-e27d-4781-888d-59ecc8241e24:c_70590e6d2eeb) [25] Tool result: gp_space_time_hot_spots, step 0. Emerging hot spots: 1 layer saved: Emerging hot spots: Community health worker visits (124 features). (id ca57d695-3247-4efe-bf1e-0e4a458c66f4:0) [26] Rendering: Map: Emerging hot spots: community health worker visits and cases. view longitude 32.49952 to 32.66088, latitude 0.25211 to 0.37225 (id 7e95523d-3393-400e-8c04-5e9bb1751ad0) [27] Plan: Space time hot spots of Vaccination sessions (the Start plan's candidate c_136a5b017678). Vaccination sessions carry dates in 'held_on': emerging hot spot analysis finds new, intensifying and fading hot spots per week. Measured: the pattern of Vaccination sessions changes over time (earlier half random, later half dispersed; the center moved 259 m). (id 502cfe77-e27d-4781-888d-59ecc8241e24:c_136a5b017678) [28] Tool result: gp_space_time_hot_spots, step 1. Emerging hot spots: 1 layer saved: Emerging hot spots: Vaccination sessions (24 features). (id ca57d695-3247-4efe-bf1e-0e4a458c66f4:1) [29] Rendering: Map: Emerging hot spots: vaccination sessions. view longitude 32.48887 to 32.67295, latitude 0.23939 to 0.37643 (id 56aebece-6125-4fb6-b449-86d97ba94702) [30] Plan: Space time hot spots of Cases (the Start plan's candidate c_24319662e98f). Cases carry dates in 'onset_date': emerging hot spot analysis finds new, intensifying and fading hot spots per week. Measured: the pattern of Cases changes over time (earlier half random, later half clustered; the center moved 783 m). (id 502cfe77-e27d-4781-888d-59ecc8241e24:c_24319662e98f) [31] Tool result: gp_space_time_hot_spots, step 2. Emerging hot spots: 1 layer saved: Emerging hot spots: Cases (125 features). (id ca57d695-3247-4efe-bf1e-0e4a458c66f4:2) [32] Catalog: Hot spot analysis (operation catalog, gp_hot_spots). Getis-Ord Gi* hot and cold spots with z-scores, p-values and confidence bins. ArcGIS: Find Hot Spots, Hot Spot Analysis (Getis-Ord Gi*). QGIS: qgis:hotspot (plugin). (id gp_hot_spots) [33] Plan: Hot spots of Community health worker visits (the Start plan's candidate c_6211f60c8ad6). 1000 points: Getis-Ord Gi* tests whether the concentrations in Community health worker visits are statistically significant. Measured: Community health worker visits are clustered (nearest-neighbor ratio 0.95, z = -3.3), median spacing 124 m. Cells of 500 m and a band of 1000 m from the measured spacing. (id 502cfe77-e27d-4781-888d-59ecc8241e24:c_6211f60c8ad6) [34] Tool result: gp_hot_spots, step 0. Hot spot analysis: 1 layer saved: Hot spot analysis: Community health worker visits (390 features). (id c2d5c745-9aa1-4cd9-a0a8-2ff3ab77a62a:0) [35] Rendering: Map: Hot spots: community health worker visits. view longitude 32.50994 to 32.65238, latitude 0.25713 to 0.36631 (id 98052aef-9d4d-4f54-ba5f-c8b6a1168787) [36] Plan: Hot spots of Cases (the Start plan's candidate c_9fb20ef66d1e). 2290 points: Getis-Ord Gi* tests whether the concentrations in Cases are statistically significant. Measured: Cases are clustered (nearest-neighbor ratio 0.90, z = -9.4), median spacing 75.6 m. Cells of 200 m and a band of 500 m from the measured spacing. (id 502cfe77-e27d-4781-888d-59ecc8241e24:c_9fb20ef66d1e) [37] Tool result: gp_hot_spots, step 1. Hot spot analysis: 1 layer saved: Hot spot analysis: Cases (2259 features). (id c2d5c745-9aa1-4cd9-a0a8-2ff3ab77a62a:1) [38] Rendering: Map: Hot spots: cases. view longitude 32.5125 to 32.64913, latitude 0.25979 to 0.36453 (id a56eeb41-2edc-432a-88c7-a24c0f5a72b3) [39] Plan: Hot spots of Vaccination sessions (the Start plan's candidate c_e1e27b62d2c9). 200 points: Getis-Ord Gi* tests whether the concentrations in Vaccination sessions are statistically significant. (id 502cfe77-e27d-4781-888d-59ecc8241e24:c_e1e27b62d2c9) [40] Tool result: gp_hot_spots, step 2. Hot spot analysis: 1 layer saved: Hot spot analysis: Vaccination sessions (99 features). (id c2d5c745-9aa1-4cd9-a0a8-2ff3ab77a62a:2) [41] Rendering: Map: Hot spots: vaccination sessions. view longitude 32.49938 to 32.65852, latitude 0.25127 to 0.37325 (id 68ed8d3d-fda0-438d-87ed-17c5d6d517d1) [42] Catalog: Summarize within (operation catalog, gp_summarize_within). Count and aggregate the features inside each polygon (or bin), proportionally for lines and polygons. ArcGIS: Summarize Within, Aggregate Points. QGIS: native:countpointsinpolygon, native:sumlinelengths. (id gp_summarize_within) [43] Plan: Community health worker visits per feature of Health sub-districts (the Start plan's candidate c_f0b17dbbc715). Count Community health worker visits inside each feature of Health sub-districts by purpose; the counts map as a choropleth (natural breaks, sequential colors). (id 502cfe77-e27d-4781-888d-59ecc8241e24:c_f0b17dbbc715) [44] Tool result: gp_summarize_within, step 0. Summarize within: 1 layer saved: Summarize within: Health sub-districts (18 features). (id a98160db-9f77-4a98-acfd-a879b8f97de9:0) [45] Rendering: Map: Counts: community health worker visits, by health sub-district. view longitude 32.51464 to 32.64754, latitude 0.26149 to 0.36336 (id 3b9b93aa-6cb9-420c-beb3-f36bcbea491e) [46] Tool result: gp_summarize_within, step 1. Summarize within: 1 layer saved: Summarize within: Health sub-districts (18 features). (id a98160db-9f77-4a98-acfd-a879b8f97de9:1) [47] Rendering: Map: Counts: vaccination sessions, by health sub-district. view longitude 32.51464 to 32.64754, latitude 0.26149 to 0.36336 (id 72daafcb-45d3-43a7-bc54-d22b2cc6e843) [48] Tool result: gp_summarize_within, step 2. Summarize within: 1 layer saved: Summarize within: Health sub-districts (18 features). (id a98160db-9f77-4a98-acfd-a879b8f97de9:2) [49] Rendering: Map: Counts: cases, by health sub-district. view longitude 32.51464 to 32.64754, latitude 0.26149 to 0.36336 (id 2135f78f-d2ac-433f-9d8a-6e3d45ba6e33) [50] Catalog: Period change (analysis catalog, gp_period_change). Counts or sums per period (week over week, month over month, any two periods, or before and after a date), per area, per H3 cell or in total, with the places of largest rise and fall and a significance note. ArcGIS: Summarize Percent Change (Crime Analysis), Change Point Detection. (id gp_period_change) [51] Plan: Community health worker visits: change per week per feature of Health sub-districts (the Start plan's candidate c_a2fcbe1fcdab). Community health worker visits carry dates in 'visited_at' over 26 weeks: the count per week, week over week, with the places of largest rise and fall per feature of Health sub-districts and whether the change is beyond chance. (id 502cfe77-e27d-4781-888d-59ecc8241e24:c_a2fcbe1fcdab) [52] Rendering: Map: Period change: community health worker visits, by health sub-district. view longitude 32.51235 to 32.64994, latitude 0.26121 to 0.36364 (id 1ba857b6-25f9-4a1e-9809-7dabd1dba73f) [53] Rendering: Chart: Community health worker visits: count per week. 27 rows (id 38a7f1b5-f586-4a93-acb8-4706829d7084) [54] Rendering: Chart: Community health worker visits: week of 2026-09-21 to week of 2026-09-28 by place. 28 rows (id cc02c36d-d7ba-4b30-b17f-b4d59c59a182) [55] Rendering: Chart: Period change: Community health worker visits (week of 2026-09-21 to week of 2026-09-28). 5 rows (id 5a1e5aac-b54b-49dd-82a1-6de54c6f280a) [56] Rendering: Map: Period change: vaccination sessions, by health sub-district. view longitude 32.51235 to 32.64994, latitude 0.26121 to 0.36364 (id 4021c7ee-ffb3-49da-9404-41221d96a7a2) [57] Rendering: Chart: Vaccination sessions: count per week. 39 rows (id f56fc17e-709a-4ed7-a297-d2fd6e33d067) [58] Rendering: Chart: Vaccination sessions: week of 2026-06-29 to week of 2026-07-06 by place. 20 rows (id 293fe134-01c3-4c24-b991-e34d182657c7) [59] Rendering: Chart: Period change: Vaccination sessions (week of 2026-06-29 to week of 2026-07-06). 5 rows (id a076831a-6ae4-47aa-8be1-9a5d36f509fa) [60] Rendering: Map: Period change: cases, by health sub-district. view longitude 32.51235 to 32.64994, latitude 0.26121 to 0.36364 (id c02c2da5-7128-4cbe-bc4c-1af8118de764) [61] Rendering: Chart: Cases: count per week. 53 rows (id a232e499-3702-4315-b7d3-da9d00e71897) [62] Rendering: Chart: Cases: week of 2026-05-18 to week of 2026-05-25 by place. 30 rows (id c86a0c4a-b1af-4354-b292-8496363ee353) [63] Rendering: Chart: Period change: Cases (week of 2026-05-18 to week of 2026-05-25). 6 rows (id 916d1401-e116-42e0-b939-a730c5fff396) [64] Catalog: Calculate density (operation catalog, gp_calculate_density). Kernel density per square kilometer on a grid. ArcGIS: Calculate Density, Kernel Density. QGIS: native:heatmapkerneldensityestimation. (id gp_calculate_density) [65] Plan: Kernel density of Community health worker visits (the Start plan's candidate c_224143430356). 1000 points in Community health worker visits: a density surface shows where they concentrate. Measured: Community health worker visits are clustered (nearest-neighbor ratio 0.95, z = -3.3), median spacing 124 m. (id 502cfe77-e27d-4781-888d-59ecc8241e24:c_224143430356) [66] Tool result: gp_calculate_density, step 0. Calculate density: 1 layer saved: Calculate density: Community health worker visits (2452 features). (id 12155ea1-761a-4dfb-bea9-c367f910d998:0) [67] Rendering: Map: Density: community health worker visits. view longitude 32.50938 to 32.65381, latitude 0.25717 to 0.36787 (id c4802079-26fd-4077-ab26-93b7925b6482) [68] Plan: Kernel density of Cases (the Start plan's candidate c_070749f5aead). 2290 points in Cases: a density surface shows where they concentrate. Measured: Cases are clustered (nearest-neighbor ratio 0.90, z = -9.4), median spacing 75.6 m. (id 502cfe77-e27d-4781-888d-59ecc8241e24:c_070749f5aead) [69] Tool result: gp_calculate_density, step 1. Calculate density: 1 layer saved: Calculate density: Cases (2508 features). (id 12155ea1-761a-4dfb-bea9-c367f910d998:1) [70] Rendering: Map: Density: cases. view longitude 32.50843 to 32.65285, latitude 0.25669 to 0.3674 (id 06af517c-f53b-4797-a57c-3a39a0f27a0d) [71] Plan: Kernel density of Vaccination sessions (the Start plan's candidate c_e0476445813b). 200 points in Vaccination sessions: a density surface shows where they concentrate. (id 502cfe77-e27d-4781-888d-59ecc8241e24:c_e0476445813b) [72] Tool result: gp_calculate_density, step 2. Calculate density: 1 layer saved: Calculate density: Vaccination sessions (616 features). (id 12155ea1-761a-4dfb-bea9-c367f910d998:2) [73] Rendering: Map: Density: vaccination sessions. view longitude 32.49029 to 32.67437, latitude 0.24081 to 0.3819 (id 8e8a277d-768e-4e3d-b59e-c64d53beeb67) [74] Catalog: Find point clusters (operation catalog, gp_find_point_clusters). Density-based clusters (DBSCAN, HDBSCAN or OPTICS, optionally space-time) with hulls and noise. ArcGIS: Find Point Clusters, Density-based Clustering. QGIS: native:dbscanclustering, native:stdbscanclustering. (id gp_find_point_clusters) [75] Plan: Cluster points of Community health worker visits (the Start plan's candidate c_b85a20a2f1a4). DBSCAN finds dense groups of Community health worker visits and separates noise. Measured: Community health worker visits are clustered (nearest-neighbor ratio 0.95, z = -3.3), median spacing 124 m. DBSCAN at 200 m (twice the measured spacing). (id 502cfe77-e27d-4781-888d-59ecc8241e24:c_b85a20a2f1a4) [76] Tool result: gp_find_point_clusters, step 0. Find point clusters: 1 layer saved: Find point clusters: Community health worker visits (1000 features). (id 77bbbd8a-81ae-4bc9-b0c3-23aaf5f1fa0f:0) [77] Rendering: Map: Clusters: community health worker visits. view longitude 32.51641 to 32.64657, latitude 0.26283 to 0.3626 (id 8bb4310d-659d-4524-a471-aaae6fdb36c0) [78] Plan: Cluster points of Cases (the Start plan's candidate c_c6803ff2f7da). DBSCAN finds dense groups of Cases and separates noise. Measured: Cases are clustered (nearest-neighbor ratio 0.90, z = -9.4), median spacing 75.6 m. DBSCAN at 200 m (twice the measured spacing). (id 502cfe77-e27d-4781-888d-59ecc8241e24:c_c6803ff2f7da) [79] Tool result: gp_find_point_clusters, step 1. Find point clusters: 2 layers saved: Find point clusters: Cases (2290 features); Find point clusters: cluster hulls (1 feature). (id 77bbbd8a-81ae-4bc9-b0c3-23aaf5f1fa0f:1) [80] Rendering: Map: Clusters: cases. view longitude 32.51557 to 32.64664, latitude 0.26229 to 0.36275 (id 48c33ecb-0323-4a65-a670-4a31e60946af) [81] Catalog: Find outliers (operation catalog, gp_outliers). Anselin local Moran’s I: HH, LL clusters and HL, LH outliers. ArcGIS: Find Outliers, Cluster and Outlier Analysis (Anselin Local Moran’s I). (id gp_outliers) [82] Plan: Local morans i of Health sub-districts (the Start plan's candidate c_39aa394b7122). Local Moran's I maps clusters and outliers of 'population'. Measured: Health sub-districts have 39 neighboring pairs (4.33 neighbors per area, 0 isolated), so neighbors can be compared. (id 502cfe77-e27d-4781-888d-59ecc8241e24:c_39aa394b7122) [83] Tool result: gp_outliers, step 0. Find outliers: 1 layer saved: Find outliers: Health sub-districts (18 features). (id 97423338-c177-4702-bebb-f1beb57bfaff:0) [84] Rendering: Map: Outliers: health sub-districts. view longitude 32.51464 to 32.64754, latitude 0.26149 to 0.36336 (id b07c4b22-dbfb-4024-b401-a4a40ee5d0c0) [85] Catalog: Spatial join (operation catalog, gp_spatial_join). Attach attributes (or aggregates) of matching features from another layer, by location, distance, time and attributes. ArcGIS: Join Features, Spatial Join. QGIS: native:joinattributesbylocation, native:joinbylocationsummary, native:joinbynearest. (id gp_spatial_join) [86] Plan: The run's goal. Describe the project Sample: Public health, Kampala: run the analyses that tell the most about its data, draw every result, and write a cited report. (id 12778c77-4c3f-4379-9aca-c9e85676282e:goal) [87] Tool result: gp_spatial_join, step 0. Spatial join: 1 layer saved: Change beside the counts per health sub-district (18 features). (id a1498fe0-e6b0-47ea-b178-eea9d9489b4e:0) [88] Layer: Summarize within: Health sub-districts. 18 features (id 9220b574-337c-44ac-8245-9bddea6ca9e7) [89] Layer: Change beside the counts per health sub-district. 18 features (id 329bcab2-5a7e-47ff-9e3a-70ab2bfb87e9) [90] Rendering: Map: Change beside the counts per health sub-district. view longitude 32.51464 to 32.64754, latitude 0.26149 to 0.36336 (id 1a975715-964d-4cea-a9d4-e1f36f7402f9) [91] Rendering: Chart: Change beside the counts per health sub-district. 6 rows (id d8232fd5-0620-44d0-a014-35f7ff7c1ccb) [92] Tool result: gp_spatial_join, step 0. Spatial join: 1 layer saved: Change of vaccination sessions where density is high (10 features). (id dcf7086a-4b87-4442-bf12-c3721d1867e7:0) [93] Layer: Calculate density: Community health worker visits. 2,452 features (id 3aeb4280-7dc5-445b-bbe5-0f612cf31e62) [94] Layer: Change of vaccination sessions where density is high. 10 features (id 9cf2f4e1-0c98-47a0-8eda-017184bee6f6) [95] Rendering: Map: Change of vaccination sessions where density is high. view longitude 32.51464 to 32.64754, latitude 0.26149 to 0.36336 (id 2e0eef15-a319-4b25-8746-f0ab62d6c9ad) [96] Rendering: Chart: Change of vaccination sessions where density is high. 5 rows (id 339db613-2509-4201-bb54-b9cf3018c42e) [97] Tool result: gp_summarize_within, step 0. Summarize within: 1 layer saved: Hot cells of cases per health sub-district (18 features). (id cbaa2d66-7e86-4062-a268-8ceebb34226f:0) [98] Layer: Hot spot analysis: Cases. 2,259 features (id 7dc9eb55-076a-4811-89ea-633c6eba1ad4) [99] Layer: Hot cells of cases per health sub-district. 18 features (id c90fa521-67fb-47d1-b4be-789822f3c247) [100] Rendering: Map: Hot cells of cases per health sub-district. view longitude 32.51464 to 32.64754, latitude 0.26149 to 0.36336 (id c1fd9863-16f7-454b-b2e4-46156c47d93d) [101] Rendering: Chart: Hot cells of cases per health sub-district. 6 rows (id d428d2a4-23ca-4584-9e0e-18e6da6305ec) [102] Catalog: Find nearest (operation catalog, gp_find_nearest). The k nearest features for each input, with geodesic distance and bearing. ArcGIS: Find Nearest, Near, Generate Near Table. QGIS: native:joinbynearest, native:nearestneighbouranalysis. (id gp_find_nearest) [103] Tool result: gp_find_nearest, step 0. Find nearest: 1 layer saved: Nearest outbreak response team to each cluster of cases (1 feature). (id bf6d2133-da41-4a20-84bf-87e492f1da5b:0) [104] Layer: Find point clusters: cluster hulls. 1 features (id 53d9a3e2-2347-4fc0-bb22-fd75844c6cc8) [105] Layer: Nearest outbreak response team to each cluster of cases. 1 features (id b7c1c4f9-ecec-44aa-bf5a-a330316e549f) [106] Rendering: Map: Nearest outbreak response team to each cluster of cases. view longitude 32.5846 to 32.60637, latitude 0.28861 to 0.3053 (id 561b2c9d-1c47-4350-a8f1-2ace1078c213) [107] Rendering: Chart: Nearest outbreak response team to each cluster of cases. 1 rows (id 3762f5c3-d6af-4f5c-847a-dcb0fcf4fbe1) [108] Catalog: Pipeline (tool description, gp_pipeline). Run up to 20 GIS operations in one engine call; a step's params may use "$id" (an earlier step's result) or "$id.key", and layer references. Every step's map outputs are saved as styled layers. Never calls Mapbox. (id gp_pipeline) [109] Plan: Community health worker visits per 10,000 people per feature of Health sub-districts (the Start plan's candidate c_9273635ebf24). Health sub-districts holds population in 'population': Community health worker visits per 10,000 people in each feature compares features of different size; mapped as a choropleth (natural breaks, sequential colors). (id 502cfe77-e27d-4781-888d-59ecc8241e24:c_9273635ebf24) [110] Tool result: gp_pipeline, step 0. Pipeline of 2 steps (classify_field > dissolve): 1 layer saved. (id 228e478a-4ba9-4d74-976e-e45efd5229c7:0) [111] Layer: Dense areas of community health worker visits. 5 features (id 3c9ccc18-20ed-47f2-ac66-234751a0679d) [112] Rendering: Chart: Dense areas of community health worker visits. 5 rows (id 42cf1971-6f22-441e-aa1a-a1326ec273d8) [113] Tool result: gp_summarize_within, step 0. Summarize within: 1 layer saved: Change of vaccination sessions per health sub-district (18 features). (id 17676f2a-994e-4660-9745-0a3db803f8a8:0) [114] Layer: Change of vaccination sessions per health sub-district. 18 features (id f4ad8c88-5019-4a59-a2a0-054c153f32be) [115] Rendering: Map: Change of vaccination sessions per health sub-district. view longitude 32.51464 to 32.64754, latitude 0.26149 to 0.36336 (id 5b65b748-4632-4299-9ce1-4f8eb9a0301e) [116] Rendering: Chart: Change of vaccination sessions per health sub-district. 6 rows (id 7d5f7fc8-f8d9-41e4-be37-e2cea1f883b7) [117] Plan: Outbreak response teams per 10,000 people per feature of Health sub-districts (the Start plan's candidate c_9a76ebe60c76). Health sub-districts holds population in 'population': Outbreak response teams per 10,000 people in each feature compares features of different size; mapped as a choropleth (natural breaks, sequential colors). (id 502cfe77-e27d-4781-888d-59ecc8241e24:c_9a76ebe60c76) [118] Tool result: gp_pipeline, step 0. Pipeline of 2 steps (buffer > summarize_within): 1 layer saved. (id 8ab3e530-5833-406a-81af-1fb4eae27c2f:0) [119] Layer: Outbreak response teams within 250 m of each cluster of cases. 1 features (id b7e12b85-5a4c-4d51-b141-8bb6248100da) [120] Rendering: Map: Outbreak response teams within 250 m of each cluster of cases. view longitude 32.58093 to 32.61005, latitude 0.28573 to 0.30805 (id 2a7e6b94-090d-4ceb-9543-bf0e2eaad33c) [121] Rendering: Chart: Outbreak response teams within 250 m of each cluster of cases. 1 rows (id 8d19bda1-77d2-4fd7-9777-e6693a0dd643) [122] Catalog: Merge layers (operation catalog, gp_merge_layers). Append several layers into one. ArcGIS: Merge Layers, Merge. QGIS: native:mergevectorlayers. (id gp_merge_layers) [123] Tool result: gp_merge_layers, step 0. Merge layers: 1 layer saved: Density of community health worker visits and cases together (4960 features). (id 1e12b18a-4e3f-4064-82dd-a2dfd4e27abc:0) [124] Layer: Calculate density: Cases. 2,508 features (id b48a2b54-e6f8-464b-bc6d-b3f70448006d) [125] Layer: Density of community health worker visits and cases together. 4,960 features (id 39d58f96-c415-42fe-bd1d-212d8c37be6e) [126] Rendering: Chart: Density of community health worker visits and cases together. 14 rows (id 2adbb49c-d53c-48e7-a48f-ad1f6f209cf5) [127] Rendering: Map: Health sub-districts by population (id 5ef70e51-0bfd-4073-a856-905f5169fec3) [128] Rendering: Chart: Cases over time (onset_date). 159 rows (id 2767bc98-56c4-42c3-b1d6-bd12a2987bec) [129] Rendering: Chart: Clinic stock by month over time (month). 36 rows (id f8cfe58c-5392-477f-95a8-16f1c3223752) [130] Rendering: Chart: Emerging hot spots: Community health worker visits. 8 rows (id d847a912-2ac6-45dd-9ff7-e69e54f5091b) [131] Rendering: Chart: Emerging hot spots: Vaccination sessions. 6 rows (id e41e6ccc-7233-4910-b880-fa8e491bc99b) [132] Rendering: Chart: Emerging hot spots: Cases. 8 rows (id 8d0dd7bd-fa8d-46d4-a605-510c07477368) [133] Rendering: Chart: Hot spot analysis: Community health worker visits. 21 rows (id 2f9efce8-7875-4dac-b005-38853d079029) [134] Rendering: Chart: Hot spot analysis: Cases. 53 rows (id fa55dd0e-666e-4751-b2b2-9e1f0b859733) [135] Rendering: Chart: Hot spot analysis: Vaccination sessions. 14 rows (id b1e62dd9-e001-4d7a-b5e6-ab1dc1f840cc) [136] Rendering: Chart: Summarize within: Health sub-districts. 6 rows (id 9f7e951b-937a-4c91-a27e-78f52dcc8881) [137] Rendering: Chart: Summarize within: Health sub-districts. 6 rows (id c229b3e5-c6e5-4952-a2f7-694aa2209b3f) [138] Rendering: Chart: Summarize within: Health sub-districts. 6 rows (id 5e8cdc54-af5b-4a64-8630-d8cc680b09a0) [139] Rendering: Chart: Calculate density: Community health worker visits. 13 rows (id fde12f1f-a5aa-4aeb-bf82-9263e56dca6f) [140] Rendering: Chart: Calculate density: Cases. 13 rows (id d3819a7e-df6e-410c-a08e-5dc99421b7b4) [141] Rendering: Chart: Calculate density: Vaccination sessions. 11 rows (id e67f7ebe-6edb-44cb-992b-568211369e6d) [142] Rendering: Chart: Find point clusters: Cases. 2 rows (id 9446f731-3ec5-4204-a55f-680b73096a9b) [143] Rendering: Chart: Find point clusters: cluster hulls. 1 rows (id 47f73bfc-cea9-49b5-a8c8-0daf229197af) [144] Rendering: Chart: Find outliers: Health sub-districts. 18 rows (id 80bc5037-fe96-47f6-9f2d-09b7e0b01cee) [145] Rendering: Chart: Find point clusters: Cases over time. 106 rows (id eda4bec9-eaa5-497f-855f-0e9893628d6b) [146] Rendering: Chart: Find point clusters: Community health worker visits over time. 108 rows (id b48c3ed5-295d-4fff-a7f2-8068c4ce4536) [147] Rendering: Map: Density of community health worker visits and cases together. view longitude 32.50837 to 32.6538, latitude 0.25641 to 0.36789 (id 88c9d95c-3e1b-4a51-8f51-669e638423ad) [148] Document: Epidemiological situation report. 1 page(s), 1 page(s) from the text layer (id cb6fd9c4-f91e-4a89-a40d-68bf28554744) [149] Document page: Epidemiological situation report, page 1. Malaria cases rose after both rainy seasons, three weeks after the rain peaks. (id cb6fd9c4-f91e-4a89-a40d-68bf28554744:1) [150] Dataset: Health sub-districts. vector, 18 rows (id 9bb4e797-2cc2-4974-b5ec-02ddc0a96670) [151] Dataset: Clinics. vector, 35 rows (id 668e992e-3b51-4493-9704-d33893bb3dfc) [152] Dataset: Water points. vector, 150 rows (id 899479cd-15fb-482f-8652-6656bfa10bfb) [153] Dataset: Outbreak response teams. vector, 6 rows (id af26ec39-903c-46e8-8314-2dea85af0098) [154] Dataset: Epidemiological situation report. document, ? rows (id 33a668e1-f7f4-454b-ac70-576cd982e98f) [155] Record: Sample: Public health, Kampala (id 2612067c-badd-4310-9678-8f580d5e3d32) [156] Layer: Emerging hot spots: Community health worker visits. 124 features (id 32cd91ad-60ea-4d7e-a96f-48417549e7e1) [157] Layer: Emerging hot spots: Vaccination sessions. 24 features (id 99a03e93-7113-48b6-b8ad-aa357667356c) [158] Layer: Emerging hot spots: Cases. 125 features (id fd743779-7b6c-495f-9c0d-b1ac9a8dafa4) [159] Layer: Hot spot analysis: Community health worker visits. 390 features (id e0bdc602-6847-43ba-83b2-d2832f74b413) [160] Layer: Hot spot analysis: Vaccination sessions. 99 features (id dc4def90-dea3-4ca0-88dd-5778b14b932b) [161] Layer: Summarize within: Health sub-districts. 18 features (id def8ac79-eb53-495e-af55-828f67a5b5a2) [162] Layer: Summarize within: Health sub-districts. 18 features (id f4259241-f5e4-42fa-9481-0cf7434aa6d5) [163] Layer: Calculate density: Vaccination sessions. 616 features (id c49f160e-369f-44fa-b91b-296dc8ea18ee) [164] Layer: Find point clusters: Community health worker visits. 1,000 features (id 97d77c9e-c58d-4c5a-af88-3874d12ef032) [165] Layer: Find point clusters: Cases. 2,290 features (id f97a47bc-a875-42e7-891d-ba3d7809245f) [166] Layer: Find outliers: Health sub-districts. 18 features (id 2d5379dc-e698-4b04-b2db-6504a49d353d) ## Appendix: the data behind the charts ### Figure 1. Timeline: events per week by dataset [1] | period | series | value | | --- | --- | --- | | 2025-09-29 | Cases | 16 | | 2025-09-29 | Community health worker visits | 0 | | 2025-09-29 | Weekly cases by sub-district | 0 | | 2025-09-29 | Clinic stock by month | 0 | | 2025-09-29 | Vaccination sessions | 0 | | 2025-10-06 | Cases | 25 | | 2025-10-06 | Community health worker visits | 0 | | 2025-10-06 | Weekly cases by sub-district | 18 | | 2025-10-06 | Clinic stock by month | 0 | | 2025-10-06 | Vaccination sessions | 0 | | 2025-10-13 | Cases | 20 | | 2025-10-13 | Community health worker visits | 0 | | 2025-10-13 | Weekly cases by sub-district | 18 | | 2025-10-13 | Clinic stock by month | 0 | | 2025-10-13 | Vaccination sessions | 0 | | 2025-10-20 | Cases | 39 | | 2025-10-20 | Community health worker visits | 0 | | 2025-10-20 | Weekly cases by sub-district | 18 | | 2025-10-20 | Clinic stock by month | 0 | | 2025-10-20 | Vaccination sessions | 0 | | 2025-10-27 | Cases | 25 | | 2025-10-27 | Community health worker visits | 0 | | 2025-10-27 | Weekly cases by sub-district | 18 | | 2025-10-27 | Clinic stock by month | 0 | | 2025-10-27 | Vaccination sessions | 0 | | 2025-11-03 | Cases | 38 | | 2025-11-03 | Community health worker visits | 0 | | 2025-11-03 | Weekly cases by sub-district | 18 | | 2025-11-03 | Clinic stock by month | 0 | | 2025-11-03 | Vaccination sessions | 0 | | 2025-11-10 | Cases | 50 | | 2025-11-10 | Community health worker visits | 0 | | 2025-11-10 | Weekly cases by sub-district | 18 | | 2025-11-10 | Clinic stock by month | 0 | | 2025-11-10 | Vaccination sessions | 0 | | 2025-11-17 | Cases | 60 | | 2025-11-17 | Community health worker visits | 0 | | 2025-11-17 | Weekly cases by sub-district | 18 | | 2025-11-17 | Clinic stock by month | 0 | | 2025-11-17 | Vaccination sessions | 0 | | 2025-11-24 | Cases | 91 | | 2025-11-24 | Community health worker visits | 0 | | 2025-11-24 | Weekly cases by sub-district | 18 | | 2025-11-24 | Clinic stock by month | 0 | | 2025-11-24 | Vaccination sessions | 0 | | 2025-12-01 | Cases | 63 | | 2025-12-01 | Community health worker visits | 0 | | 2025-12-01 | Weekly cases by sub-district | 18 | | 2025-12-01 | Clinic stock by month | 0 | | 2025-12-01 | Vaccination sessions | 0 | | 2025-12-08 | Cases | 55 | | 2025-12-08 | Community health worker visits | 0 | | 2025-12-08 | Weekly cases by sub-district | 18 | | 2025-12-08 | Clinic stock by month | 0 | | 2025-12-08 | Vaccination sessions | 0 | | 2025-12-15 | Cases | 70 | | 2025-12-15 | Community health worker visits | 0 | | 2025-12-15 | Weekly cases by sub-district | 18 | | 2025-12-15 | Clinic stock by month | 0 | | 2025-12-15 | Vaccination sessions | 0 | | 2025-12-22 | Cases | 52 | | 2025-12-22 | Community health worker visits | 0 | | 2025-12-22 | Weekly cases by sub-district | 18 | | 2025-12-22 | Clinic stock by month | 0 | | 2025-12-22 | Vaccination sessions | 0 | | 2025-12-29 | Cases | 39 | | 2025-12-29 | Community health worker visits | 0 | | 2025-12-29 | Weekly cases by sub-district | 18 | | 2025-12-29 | Clinic stock by month | 35 | | 2025-12-29 | Vaccination sessions | 0 | | 2026-01-05 | Cases | 44 | | 2026-01-05 | Community health worker visits | 0 | | 2026-01-05 | Weekly cases by sub-district | 18 | | 2026-01-05 | Clinic stock by month | 0 | | 2026-01-05 | Vaccination sessions | 8 | | 2026-01-12 | Cases | 36 | | 2026-01-12 | Community health worker visits | 0 | | 2026-01-12 | Weekly cases by sub-district | 18 | | 2026-01-12 | Clinic stock by month | 0 | | 2026-01-12 | Vaccination sessions | 5 | | 2026-01-19 | Cases | 28 | | 2026-01-19 | Community health worker visits | 0 | | 2026-01-19 | Weekly cases by sub-district | 18 | | 2026-01-19 | Clinic stock by month | 0 | | 2026-01-19 | Vaccination sessions | 4 | | 2026-01-26 | Cases | 30 | | 2026-01-26 | Community health worker visits | 0 | | 2026-01-26 | Weekly cases by sub-district | 18 | | 2026-01-26 | Clinic stock by month | 35 | | 2026-01-26 | Vaccination sessions | 3 | | 2026-02-02 | Cases | 26 | | 2026-02-02 | Community health worker visits | 0 | | 2026-02-02 | Weekly cases by sub-district | 18 | | 2026-02-02 | Clinic stock by month | 0 | | 2026-02-02 | Vaccination sessions | 6 | | 2026-02-09 | Cases | 17 | | 2026-02-09 | Community health worker visits | 0 | | 2026-02-09 | Weekly cases by sub-district | 18 | | 2026-02-09 | Clinic stock by month | 0 | | 2026-02-09 | Vaccination sessions | 5 | | 2026-02-16 | Cases | 21 | | 2026-02-16 | Community health worker visits | 0 | | 2026-02-16 | Weekly cases by sub-district | 18 | | 2026-02-16 | Clinic stock by month | 0 | | 2026-02-16 | Vaccination sessions | 6 | | 2026-02-23 | Cases | 29 | | 2026-02-23 | Community health worker visits | 0 | | 2026-02-23 | Weekly cases by sub-district | 18 | | 2026-02-23 | Clinic stock by month | 35 | | 2026-02-23 | Vaccination sessions | 9 | | 2026-03-02 | Cases | 25 | | 2026-03-02 | Community health worker visits | 0 | | 2026-03-02 | Weekly cases by sub-district | 18 | | 2026-03-02 | Clinic stock by month | 0 | | 2026-03-02 | Vaccination sessions | 5 | | 2026-03-09 | Cases | 23 | | 2026-03-09 | Community health worker visits | 0 | | 2026-03-09 | Weekly cases by sub-district | 18 | | 2026-03-09 | Clinic stock by month | 0 | | 2026-03-09 | Vaccination sessions | 2 | | 2026-03-16 | Cases | 29 | | 2026-03-16 | Community health worker visits | 0 | | 2026-03-16 | Weekly cases by sub-district | 18 | | 2026-03-16 | Clinic stock by month | 0 | | 2026-03-16 | Vaccination sessions | 3 | | 2026-03-23 | Cases | 29 | | 2026-03-23 | Community health worker visits | 0 | | 2026-03-23 | Weekly cases by sub-district | 18 | | 2026-03-23 | Clinic stock by month | 0 | | 2026-03-23 | Vaccination sessions | 6 | | 2026-03-30 | Cases | 21 | | 2026-03-30 | Community health worker visits | 26 | | 2026-03-30 | Weekly cases by sub-district | 18 | | 2026-03-30 | Clinic stock by month | 35 | | 2026-03-30 | Vaccination sessions | 5 | | 2026-04-06 | Cases | 40 | | 2026-04-06 | Community health worker visits | 42 | | 2026-04-06 | Weekly cases by sub-district | 18 | | 2026-04-06 | Clinic stock by month | 0 | | 2026-04-06 | Vaccination sessions | 7 | | 2026-04-13 | Cases | 55 | | 2026-04-13 | Community health worker visits | 34 | | 2026-04-13 | Weekly cases by sub-district | 18 | | 2026-04-13 | Clinic stock by month | 0 | | 2026-04-13 | Vaccination sessions | 9 | | 2026-04-20 | Cases | 60 | | 2026-04-20 | Community health worker visits | 37 | | 2026-04-20 | Weekly cases by sub-district | 18 | | 2026-04-20 | Clinic stock by month | 0 | | 2026-04-20 | Vaccination sessions | 4 | | 2026-04-27 | Cases | 89 | | 2026-04-27 | Community health worker visits | 41 | | 2026-04-27 | Weekly cases by sub-district | 18 | | 2026-04-27 | Clinic stock by month | 35 | | 2026-04-27 | Vaccination sessions | 0 | | 2026-05-04 | Cases | 128 | | 2026-05-04 | Community health worker visits | 43 | | 2026-05-04 | Weekly cases by sub-district | 18 | | 2026-05-04 | Clinic stock by month | 0 | | 2026-05-04 | Vaccination sessions | 1 | | 2026-05-11 | Cases | 166 | | 2026-05-11 | Community health worker visits | 29 | | 2026-05-11 | Weekly cases by sub-district | 18 | | 2026-05-11 | Clinic stock by month | 0 | | 2026-05-11 | Vaccination sessions | 7 | | 2026-05-18 | Cases | 136 | | 2026-05-18 | Community health worker visits | 44 | | 2026-05-18 | Weekly cases by sub-district | 18 | | 2026-05-18 | Clinic stock by month | 0 | | 2026-05-18 | Vaccination sessions | 2 | | 2026-05-25 | Cases | 86 | | 2026-05-25 | Community health worker visits | 37 | | 2026-05-25 | Weekly cases by sub-district | 18 | | 2026-05-25 | Clinic stock by month | 0 | | 2026-05-25 | Vaccination sessions | 5 | | 2026-06-01 | Cases | 60 | | 2026-06-01 | Community health worker visits | 31 | | 2026-06-01 | Weekly cases by sub-district | 18 | | 2026-06-01 | Clinic stock by month | 35 | | 2026-06-01 | Vaccination sessions | 3 | | 2026-06-08 | Cases | 29 | | 2026-06-08 | Community health worker visits | 31 | | 2026-06-08 | Weekly cases by sub-district | 18 | | 2026-06-08 | Clinic stock by month | 0 | | 2026-06-08 | Vaccination sessions | 9 | | 2026-06-15 | Cases | 36 | | 2026-06-15 | Community health worker visits | 38 | | 2026-06-15 | Weekly cases by sub-district | 18 | | 2026-06-15 | Clinic stock by month | 0 | | 2026-06-15 | Vaccination sessions | 6 | | 2026-06-22 | Cases | 26 | | 2026-06-22 | Community health worker visits | 39 | | 2026-06-22 | Weekly cases by sub-district | 18 | | 2026-06-22 | Clinic stock by month | 0 | | 2026-06-22 | Vaccination sessions | 11 | | 2026-06-29 | Cases | 37 | | 2026-06-29 | Community health worker visits | 40 | | 2026-06-29 | Weekly cases by sub-district | 18 | | 2026-06-29 | Clinic stock by month | 35 | | 2026-06-29 | Vaccination sessions | 9 | | 2026-07-06 | Cases | 27 | | 2026-07-06 | Community health worker visits | 49 | | 2026-07-06 | Weekly cases by sub-district | 18 | | 2026-07-06 | Clinic stock by month | 0 | | 2026-07-06 | Vaccination sessions | 2 | | 2026-07-13 | Cases | 28 | | 2026-07-13 | Community health worker visits | 47 | | 2026-07-13 | Weekly cases by sub-district | 18 | | 2026-07-13 | Clinic stock by month | 0 | | 2026-07-13 | Vaccination sessions | 4 | | 2026-07-20 | Cases | 32 | | 2026-07-20 | Community health worker visits | 39 | | 2026-07-20 | Weekly cases by sub-district | 18 | | 2026-07-20 | Clinic stock by month | 0 | | 2026-07-20 | Vaccination sessions | 5 | | 2026-07-27 | Cases | 28 | | 2026-07-27 | Community health worker visits | 37 | | 2026-07-27 | Weekly cases by sub-district | 18 | | 2026-07-27 | Clinic stock by month | 35 | | 2026-07-27 | Vaccination sessions | 7 | | 2026-08-03 | Cases | 36 | | 2026-08-03 | Community health worker visits | 38 | | 2026-08-03 | Weekly cases by sub-district | 18 | | 2026-08-03 | Clinic stock by month | 0 | | 2026-08-03 | Vaccination sessions | 5 | | 2026-08-10 | Cases | 25 | | 2026-08-10 | Community health worker visits | 43 | | 2026-08-10 | Weekly cases by sub-district | 18 | | 2026-08-10 | Clinic stock by month | 0 | | 2026-08-10 | Vaccination sessions | 3 | | 2026-08-17 | Cases | 23 | | 2026-08-17 | Community health worker visits | 40 | | 2026-08-17 | Weekly cases by sub-district | 18 | | 2026-08-17 | Clinic stock by month | 0 | | 2026-08-17 | Vaccination sessions | 3 | | 2026-08-24 | Cases | 21 | | 2026-08-24 | Community health worker visits | 37 | | 2026-08-24 | Weekly cases by sub-district | 18 | | 2026-08-24 | Clinic stock by month | 0 | | 2026-08-24 | Vaccination sessions | 6 | | 2026-08-31 | Cases | 24 | | 2026-08-31 | Community health worker visits | 32 | | 2026-08-31 | Weekly cases by sub-district | 18 | | 2026-08-31 | Clinic stock by month | 35 | | 2026-08-31 | Vaccination sessions | 5 | | 2026-09-07 | Cases | 33 | | 2026-09-07 | Community health worker visits | 38 | | 2026-09-07 | Weekly cases by sub-district | 18 | | 2026-09-07 | Clinic stock by month | 0 | | 2026-09-07 | Vaccination sessions | 4 | | 2026-09-14 | Cases | 18 | | 2026-09-14 | Community health worker visits | 34 | | 2026-09-14 | Weekly cases by sub-district | 18 | | 2026-09-14 | Clinic stock by month | 0 | | 2026-09-14 | Vaccination sessions | 6 | | 2026-09-21 | Cases | 25 | | 2026-09-21 | Community health worker visits | 36 | | 2026-09-21 | Weekly cases by sub-district | 18 | | 2026-09-21 | Clinic stock by month | 0 | | 2026-09-21 | Vaccination sessions | 5 | | 2026-09-28 | Cases | 21 | | 2026-09-28 | Community health worker visits | 18 | | 2026-09-28 | Weekly cases by sub-district | 18 | | 2026-09-28 | Clinic stock by month | 0 | | 2026-09-28 | Vaccination sessions | 5 | ### Figure 2. Timeline lanes: events per week by dataset [7] | lane | period | period_end | value | | --- | --- | --- | --- | | Cases | 2025-09-29 | 2025-10-06 | 16 | | Cases | 2025-10-06 | 2025-10-13 | 25 | | Cases | 2025-10-13 | 2025-10-20 | 20 | | Cases | 2025-10-20 | 2025-10-27 | 39 | | Cases | 2025-10-27 | 2025-11-03 | 25 | | Cases | 2025-11-03 | 2025-11-10 | 38 | | Cases | 2025-11-10 | 2025-11-17 | 50 | | Cases | 2025-11-17 | 2025-11-24 | 60 | | Cases | 2025-11-24 | 2025-12-01 | 91 | | Cases | 2025-12-01 | 2025-12-08 | 63 | | Cases | 2025-12-08 | 2025-12-15 | 55 | | Cases | 2025-12-15 | 2025-12-22 | 70 | | Cases | 2025-12-22 | 2025-12-29 | 52 | | Cases | 2025-12-29 | 2026-01-05 | 39 | | Cases | 2026-01-05 | 2026-01-12 | 44 | | Cases | 2026-01-12 | 2026-01-19 | 36 | | Cases | 2026-01-19 | 2026-01-26 | 28 | | Cases | 2026-01-26 | 2026-02-02 | 30 | | Cases | 2026-02-02 | 2026-02-09 | 26 | | Cases | 2026-02-09 | 2026-02-16 | 17 | | Cases | 2026-02-16 | 2026-02-23 | 21 | | Cases | 2026-02-23 | 2026-03-02 | 29 | | Cases | 2026-03-02 | 2026-03-09 | 25 | | Cases | 2026-03-09 | 2026-03-16 | 23 | | Cases | 2026-03-16 | 2026-03-23 | 29 | | Cases | 2026-03-23 | 2026-03-30 | 29 | | Cases | 2026-03-30 | 2026-04-06 | 21 | | Cases | 2026-04-06 | 2026-04-13 | 40 | | Cases | 2026-04-13 | 2026-04-20 | 55 | | Cases | 2026-04-20 | 2026-04-27 | 60 | | Cases | 2026-04-27 | 2026-05-04 | 89 | | Cases | 2026-05-04 | 2026-05-11 | 128 | | Cases | 2026-05-11 | 2026-05-18 | 166 | | Cases | 2026-05-18 | 2026-05-25 | 136 | | Cases | 2026-05-25 | 2026-06-01 | 86 | | Cases | 2026-06-01 | 2026-06-08 | 60 | | Cases | 2026-06-08 | 2026-06-15 | 29 | | Cases | 2026-06-15 | 2026-06-22 | 36 | | Cases | 2026-06-22 | 2026-06-29 | 26 | | Cases | 2026-06-29 | 2026-07-06 | 37 | | Cases | 2026-07-06 | 2026-07-13 | 27 | | Cases | 2026-07-13 | 2026-07-20 | 28 | | Cases | 2026-07-20 | 2026-07-27 | 32 | | Cases | 2026-07-27 | 2026-08-03 | 28 | | Cases | 2026-08-03 | 2026-08-10 | 36 | | Cases | 2026-08-10 | 2026-08-17 | 25 | | Cases | 2026-08-17 | 2026-08-24 | 23 | | Cases | 2026-08-24 | 2026-08-31 | 21 | | Cases | 2026-08-31 | 2026-09-07 | 24 | | Cases | 2026-09-07 | 2026-09-14 | 33 | | Cases | 2026-09-14 | 2026-09-21 | 18 | | Cases | 2026-09-21 | 2026-09-28 | 25 | | Cases | 2026-09-28 | 2026-10-05 | 21 | | Community health worker visits | 2025-09-29 | 2025-10-06 | 0 | | Community health worker visits | 2025-10-06 | 2025-10-13 | 0 | | Community health worker visits | 2025-10-13 | 2025-10-20 | 0 | | Community health worker visits | 2025-10-20 | 2025-10-27 | 0 | | Community health worker visits | 2025-10-27 | 2025-11-03 | 0 | | Community health worker visits | 2025-11-03 | 2025-11-10 | 0 | | Community health worker visits | 2025-11-10 | 2025-11-17 | 0 | | Community health worker visits | 2025-11-17 | 2025-11-24 | 0 | | Community health worker visits | 2025-11-24 | 2025-12-01 | 0 | | Community health worker visits | 2025-12-01 | 2025-12-08 | 0 | | Community health worker visits | 2025-12-08 | 2025-12-15 | 0 | | Community health worker visits | 2025-12-15 | 2025-12-22 | 0 | | Community health worker visits | 2025-12-22 | 2025-12-29 | 0 | | Community health worker visits | 2025-12-29 | 2026-01-05 | 0 | | Community health worker visits | 2026-01-05 | 2026-01-12 | 0 | | Community health worker visits | 2026-01-12 | 2026-01-19 | 0 | | Community health worker visits | 2026-01-19 | 2026-01-26 | 0 | | Community health worker visits | 2026-01-26 | 2026-02-02 | 0 | | Community health worker visits | 2026-02-02 | 2026-02-09 | 0 | | Community health worker visits | 2026-02-09 | 2026-02-16 | 0 | | Community health worker visits | 2026-02-16 | 2026-02-23 | 0 | | Community health worker visits | 2026-02-23 | 2026-03-02 | 0 | | Community health worker visits | 2026-03-02 | 2026-03-09 | 0 | | Community health worker visits | 2026-03-09 | 2026-03-16 | 0 | | Community health worker visits | 2026-03-16 | 2026-03-23 | 0 | | Community health worker visits | 2026-03-23 | 2026-03-30 | 0 | | Community health worker visits | 2026-03-30 | 2026-04-06 | 26 | | Community health worker visits | 2026-04-06 | 2026-04-13 | 42 | | Community health worker visits | 2026-04-13 | 2026-04-20 | 34 | | Community health worker visits | 2026-04-20 | 2026-04-27 | 37 | | Community health worker visits | 2026-04-27 | 2026-05-04 | 41 | | Community health worker visits | 2026-05-04 | 2026-05-11 | 43 | | Community health worker visits | 2026-05-11 | 2026-05-18 | 29 | | Community health worker visits | 2026-05-18 | 2026-05-25 | 44 | | Community health worker visits | 2026-05-25 | 2026-06-01 | 37 | | Community health worker visits | 2026-06-01 | 2026-06-08 | 31 | | Community health worker visits | 2026-06-08 | 2026-06-15 | 31 | | Community health worker visits | 2026-06-15 | 2026-06-22 | 38 | | Community health worker visits | 2026-06-22 | 2026-06-29 | 39 | | Community health worker visits | 2026-06-29 | 2026-07-06 | 40 | | Community health worker visits | 2026-07-06 | 2026-07-13 | 49 | | Community health worker visits | 2026-07-13 | 2026-07-20 | 47 | | Community health worker visits | 2026-07-20 | 2026-07-27 | 39 | | Community health worker visits | 2026-07-27 | 2026-08-03 | 37 | | Community health worker visits | 2026-08-03 | 2026-08-10 | 38 | | Community health worker visits | 2026-08-10 | 2026-08-17 | 43 | | Community health worker visits | 2026-08-17 | 2026-08-24 | 40 | | Community health worker visits | 2026-08-24 | 2026-08-31 | 37 | | Community health worker visits | 2026-08-31 | 2026-09-07 | 32 | | Community health worker visits | 2026-09-07 | 2026-09-14 | 38 | | Community health worker visits | 2026-09-14 | 2026-09-21 | 34 | | Community health worker visits | 2026-09-21 | 2026-09-28 | 36 | | Community health worker visits | 2026-09-28 | 2026-10-05 | 18 | | Weekly cases by sub-district | 2025-09-29 | 2025-10-06 | 0 | | Weekly cases by sub-district | 2025-10-06 | 2025-10-13 | 18 | | Weekly cases by sub-district | 2025-10-13 | 2025-10-20 | 18 | | Weekly cases by sub-district | 2025-10-20 | 2025-10-27 | 18 | | Weekly cases by sub-district | 2025-10-27 | 2025-11-03 | 18 | | Weekly cases by sub-district | 2025-11-03 | 2025-11-10 | 18 | | Weekly cases by sub-district | 2025-11-10 | 2025-11-17 | 18 | | Weekly cases by sub-district | 2025-11-17 | 2025-11-24 | 18 | | Weekly cases by sub-district | 2025-11-24 | 2025-12-01 | 18 | | Weekly cases by sub-district | 2025-12-01 | 2025-12-08 | 18 | | Weekly cases by sub-district | 2025-12-08 | 2025-12-15 | 18 | | Weekly cases by sub-district | 2025-12-15 | 2025-12-22 | 18 | | Weekly cases by sub-district | 2025-12-22 | 2025-12-29 | 18 | | Weekly cases by sub-district | 2025-12-29 | 2026-01-05 | 18 | | Weekly cases by sub-district | 2026-01-05 | 2026-01-12 | 18 | | Weekly cases by sub-district | 2026-01-12 | 2026-01-19 | 18 | | Weekly cases by sub-district | 2026-01-19 | 2026-01-26 | 18 | | Weekly cases by sub-district | 2026-01-26 | 2026-02-02 | 18 | | Weekly cases by sub-district | 2026-02-02 | 2026-02-09 | 18 | | Weekly cases by sub-district | 2026-02-09 | 2026-02-16 | 18 | | Weekly cases by sub-district | 2026-02-16 | 2026-02-23 | 18 | | Weekly cases by sub-district | 2026-02-23 | 2026-03-02 | 18 | | Weekly cases by sub-district | 2026-03-02 | 2026-03-09 | 18 | | Weekly cases by sub-district | 2026-03-09 | 2026-03-16 | 18 | | Weekly cases by sub-district | 2026-03-16 | 2026-03-23 | 18 | | Weekly cases by sub-district | 2026-03-23 | 2026-03-30 | 18 | | Weekly cases by sub-district | 2026-03-30 | 2026-04-06 | 18 | | Weekly cases by sub-district | 2026-04-06 | 2026-04-13 | 18 | | Weekly cases by sub-district | 2026-04-13 | 2026-04-20 | 18 | | Weekly cases by sub-district | 2026-04-20 | 2026-04-27 | 18 | | Weekly cases by sub-district | 2026-04-27 | 2026-05-04 | 18 | | Weekly cases by sub-district | 2026-05-04 | 2026-05-11 | 18 | | Weekly cases by sub-district | 2026-05-11 | 2026-05-18 | 18 | | Weekly cases by sub-district | 2026-05-18 | 2026-05-25 | 18 | | Weekly cases by sub-district | 2026-05-25 | 2026-06-01 | 18 | | Weekly cases by sub-district | 2026-06-01 | 2026-06-08 | 18 | | Weekly cases by sub-district | 2026-06-08 | 2026-06-15 | 18 | | Weekly cases by sub-district | 2026-06-15 | 2026-06-22 | 18 | | Weekly cases by sub-district | 2026-06-22 | 2026-06-29 | 18 | | Weekly cases by sub-district | 2026-06-29 | 2026-07-06 | 18 | | Weekly cases by sub-district | 2026-07-06 | 2026-07-13 | 18 | | Weekly cases by sub-district | 2026-07-13 | 2026-07-20 | 18 | | Weekly cases by sub-district | 2026-07-20 | 2026-07-27 | 18 | | Weekly cases by sub-district | 2026-07-27 | 2026-08-03 | 18 | | Weekly cases by sub-district | 2026-08-03 | 2026-08-10 | 18 | | Weekly cases by sub-district | 2026-08-10 | 2026-08-17 | 18 | | Weekly cases by sub-district | 2026-08-17 | 2026-08-24 | 18 | | Weekly cases by sub-district | 2026-08-24 | 2026-08-31 | 18 | | Weekly cases by sub-district | 2026-08-31 | 2026-09-07 | 18 | | Weekly cases by sub-district | 2026-09-07 | 2026-09-14 | 18 | | Weekly cases by sub-district | 2026-09-14 | 2026-09-21 | 18 | | Weekly cases by sub-district | 2026-09-21 | 2026-09-28 | 18 | | Weekly cases by sub-district | 2026-09-28 | 2026-10-05 | 18 | | Clinic stock by month | 2025-09-29 | 2025-10-06 | 0 | | Clinic stock by month | 2025-10-06 | 2025-10-13 | 0 | | Clinic stock by month | 2025-10-13 | 2025-10-20 | 0 | | Clinic stock by month | 2025-10-20 | 2025-10-27 | 0 | | Clinic stock by month | 2025-10-27 | 2025-11-03 | 0 | | Clinic stock by month | 2025-11-03 | 2025-11-10 | 0 | | Clinic stock by month | 2025-11-10 | 2025-11-17 | 0 | | Clinic stock by month | 2025-11-17 | 2025-11-24 | 0 | | Clinic stock by month | 2025-11-24 | 2025-12-01 | 0 | | Clinic stock by month | 2025-12-01 | 2025-12-08 | 0 | | Clinic stock by month | 2025-12-08 | 2025-12-15 | 0 | | Clinic stock by month | 2025-12-15 | 2025-12-22 | 0 | | Clinic stock by month | 2025-12-22 | 2025-12-29 | 0 | | Clinic stock by month | 2025-12-29 | 2026-01-05 | 35 | | Clinic stock by month | 2026-01-05 | 2026-01-12 | 0 | | Clinic stock by month | 2026-01-12 | 2026-01-19 | 0 | | Clinic stock by month | 2026-01-19 | 2026-01-26 | 0 | | Clinic stock by month | 2026-01-26 | 2026-02-02 | 35 | | Clinic stock by month | 2026-02-02 | 2026-02-09 | 0 | | Clinic stock by month | 2026-02-09 | 2026-02-16 | 0 | | Clinic stock by month | 2026-02-16 | 2026-02-23 | 0 | | Clinic stock by month | 2026-02-23 | 2026-03-02 | 35 | | Clinic stock by month | 2026-03-02 | 2026-03-09 | 0 | | Clinic stock by month | 2026-03-09 | 2026-03-16 | 0 | | Clinic stock by month | 2026-03-16 | 2026-03-23 | 0 | | Clinic stock by month | 2026-03-23 | 2026-03-30 | 0 | | Clinic stock by month | 2026-03-30 | 2026-04-06 | 35 | | Clinic stock by month | 2026-04-06 | 2026-04-13 | 0 | | Clinic stock by month | 2026-04-13 | 2026-04-20 | 0 | | Clinic stock by month | 2026-04-20 | 2026-04-27 | 0 | | Clinic stock by month | 2026-04-27 | 2026-05-04 | 35 | | Clinic stock by month | 2026-05-04 | 2026-05-11 | 0 | | Clinic stock by month | 2026-05-11 | 2026-05-18 | 0 | | Clinic stock by month | 2026-05-18 | 2026-05-25 | 0 | | Clinic stock by month | 2026-05-25 | 2026-06-01 | 0 | | Clinic stock by month | 2026-06-01 | 2026-06-08 | 35 | | Clinic stock by month | 2026-06-08 | 2026-06-15 | 0 | | Clinic stock by month | 2026-06-15 | 2026-06-22 | 0 | | Clinic stock by month | 2026-06-22 | 2026-06-29 | 0 | | Clinic stock by month | 2026-06-29 | 2026-07-06 | 35 | | Clinic stock by month | 2026-07-06 | 2026-07-13 | 0 | | Clinic stock by month | 2026-07-13 | 2026-07-20 | 0 | | Clinic stock by month | 2026-07-20 | 2026-07-27 | 0 | | Clinic stock by month | 2026-07-27 | 2026-08-03 | 35 | | Clinic stock by month | 2026-08-03 | 2026-08-10 | 0 | | Clinic stock by month | 2026-08-10 | 2026-08-17 | 0 | | Clinic stock by month | 2026-08-17 | 2026-08-24 | 0 | | Clinic stock by month | 2026-08-24 | 2026-08-31 | 0 | | Clinic stock by month | 2026-08-31 | 2026-09-07 | 35 | | Clinic stock by month | 2026-09-07 | 2026-09-14 | 0 | | Clinic stock by month | 2026-09-14 | 2026-09-21 | 0 | | Clinic stock by month | 2026-09-21 | 2026-09-28 | 0 | | Clinic stock by month | 2026-09-28 | 2026-10-05 | 0 | | Vaccination sessions | 2025-09-29 | 2025-10-06 | 0 | | Vaccination sessions | 2025-10-06 | 2025-10-13 | 0 | | Vaccination sessions | 2025-10-13 | 2025-10-20 | 0 | | Vaccination sessions | 2025-10-20 | 2025-10-27 | 0 | | Vaccination sessions | 2025-10-27 | 2025-11-03 | 0 | | Vaccination sessions | 2025-11-03 | 2025-11-10 | 0 | | Vaccination sessions | 2025-11-10 | 2025-11-17 | 0 | | Vaccination sessions | 2025-11-17 | 2025-11-24 | 0 | | Vaccination sessions | 2025-11-24 | 2025-12-01 | 0 | | Vaccination sessions | 2025-12-01 | 2025-12-08 | 0 | | Vaccination sessions | 2025-12-08 | 2025-12-15 | 0 | | Vaccination sessions | 2025-12-15 | 2025-12-22 | 0 | | Vaccination sessions | 2025-12-22 | 2025-12-29 | 0 | | Vaccination sessions | 2025-12-29 | 2026-01-05 | 0 | | Vaccination sessions | 2026-01-05 | 2026-01-12 | 8 | | Vaccination sessions | 2026-01-12 | 2026-01-19 | 5 | | Vaccination sessions | 2026-01-19 | 2026-01-26 | 4 | | Vaccination sessions | 2026-01-26 | 2026-02-02 | 3 | | Vaccination sessions | 2026-02-02 | 2026-02-09 | 6 | | Vaccination sessions | 2026-02-09 | 2026-02-16 | 5 | | Vaccination sessions | 2026-02-16 | 2026-02-23 | 6 | | Vaccination sessions | 2026-02-23 | 2026-03-02 | 9 | | Vaccination sessions | 2026-03-02 | 2026-03-09 | 5 | | Vaccination sessions | 2026-03-09 | 2026-03-16 | 2 | | Vaccination sessions | 2026-03-16 | 2026-03-23 | 3 | | Vaccination sessions | 2026-03-23 | 2026-03-30 | 6 | | Vaccination sessions | 2026-03-30 | 2026-04-06 | 5 | | Vaccination sessions | 2026-04-06 | 2026-04-13 | 7 | | Vaccination sessions | 2026-04-13 | 2026-04-20 | 9 | | Vaccination sessions | 2026-04-20 | 2026-04-27 | 4 | | Vaccination sessions | 2026-04-27 | 2026-05-04 | 0 | | Vaccination sessions | 2026-05-04 | 2026-05-11 | 1 | | Vaccination sessions | 2026-05-11 | 2026-05-18 | 7 | | Vaccination sessions | 2026-05-18 | 2026-05-25 | 2 | | Vaccination sessions | 2026-05-25 | 2026-06-01 | 5 | | Vaccination sessions | 2026-06-01 | 2026-06-08 | 3 | | Vaccination sessions | 2026-06-08 | 2026-06-15 | 9 | | Vaccination sessions | 2026-06-15 | 2026-06-22 | 6 | | Vaccination sessions | 2026-06-22 | 2026-06-29 | 11 | | Vaccination sessions | 2026-06-29 | 2026-07-06 | 9 | | Vaccination sessions | 2026-07-06 | 2026-07-13 | 2 | | Vaccination sessions | 2026-07-13 | 2026-07-20 | 4 | | Vaccination sessions | 2026-07-20 | 2026-07-27 | 5 | | Vaccination sessions | 2026-07-27 | 2026-08-03 | 7 | | Vaccination sessions | 2026-08-03 | 2026-08-10 | 5 | | Vaccination sessions | 2026-08-10 | 2026-08-17 | 3 | | Vaccination sessions | 2026-08-17 | 2026-08-24 | 3 | | Vaccination sessions | 2026-08-24 | 2026-08-31 | 6 | | Vaccination sessions | 2026-08-31 | 2026-09-07 | 5 | | Vaccination sessions | 2026-09-07 | 2026-09-14 | 4 | | Vaccination sessions | 2026-09-14 | 2026-09-21 | 6 | | Vaccination sessions | 2026-09-21 | 2026-09-28 | 5 | | Vaccination sessions | 2026-09-28 | 2026-10-05 | 5 | ### Figure 3. Timeline: change from one week to the next [8] | period | period_end | value | previous | change | | --- | --- | --- | --- | --- | | 2025-10-06 | 2025-10-13 | 43 | 16 | 27 | | 2025-10-13 | 2025-10-20 | 38 | 43 | -5 | | 2025-10-20 | 2025-10-27 | 57 | 38 | 19 | | 2025-10-27 | 2025-11-03 | 43 | 57 | -14 | | 2025-11-03 | 2025-11-10 | 56 | 43 | 13 | | 2025-11-10 | 2025-11-17 | 68 | 56 | 12 | | 2025-11-17 | 2025-11-24 | 78 | 68 | 10 | | 2025-11-24 | 2025-12-01 | 109 | 78 | 31 | | 2025-12-01 | 2025-12-08 | 81 | 109 | -28 | | 2025-12-08 | 2025-12-15 | 73 | 81 | -8 | | 2025-12-15 | 2025-12-22 | 88 | 73 | 15 | | 2025-12-22 | 2025-12-29 | 70 | 88 | -18 | | 2025-12-29 | 2026-01-05 | 92 | 70 | 22 | | 2026-01-05 | 2026-01-12 | 70 | 92 | -22 | | 2026-01-12 | 2026-01-19 | 59 | 70 | -11 | | 2026-01-19 | 2026-01-26 | 50 | 59 | -9 | | 2026-01-26 | 2026-02-02 | 86 | 50 | 36 | | 2026-02-02 | 2026-02-09 | 50 | 86 | -36 | | 2026-02-09 | 2026-02-16 | 40 | 50 | -10 | | 2026-02-16 | 2026-02-23 | 45 | 40 | 5 | | 2026-02-23 | 2026-03-02 | 91 | 45 | 46 | | 2026-03-02 | 2026-03-09 | 48 | 91 | -43 | | 2026-03-09 | 2026-03-16 | 43 | 48 | -5 | | 2026-03-16 | 2026-03-23 | 50 | 43 | 7 | | 2026-03-23 | 2026-03-30 | 53 | 50 | 3 | | 2026-03-30 | 2026-04-06 | 105 | 53 | 52 | | 2026-04-06 | 2026-04-13 | 107 | 105 | 2 | | 2026-04-13 | 2026-04-20 | 116 | 107 | 9 | | 2026-04-20 | 2026-04-27 | 119 | 116 | 3 | | 2026-04-27 | 2026-05-04 | 183 | 119 | 64 | | 2026-05-04 | 2026-05-11 | 190 | 183 | 7 | | 2026-05-11 | 2026-05-18 | 220 | 190 | 30 | | 2026-05-18 | 2026-05-25 | 200 | 220 | -20 | | 2026-05-25 | 2026-06-01 | 146 | 200 | -54 | | 2026-06-01 | 2026-06-08 | 147 | 146 | 1 | | 2026-06-08 | 2026-06-15 | 87 | 147 | -60 | | 2026-06-15 | 2026-06-22 | 98 | 87 | 11 | | 2026-06-22 | 2026-06-29 | 94 | 98 | -4 | | 2026-06-29 | 2026-07-06 | 139 | 94 | 45 | | 2026-07-06 | 2026-07-13 | 96 | 139 | -43 | | 2026-07-13 | 2026-07-20 | 97 | 96 | 1 | | 2026-07-20 | 2026-07-27 | 94 | 97 | -3 | | 2026-07-27 | 2026-08-03 | 125 | 94 | 31 | | 2026-08-03 | 2026-08-10 | 97 | 125 | -28 | | 2026-08-10 | 2026-08-17 | 89 | 97 | -8 | | 2026-08-17 | 2026-08-24 | 84 | 89 | -5 | | 2026-08-24 | 2026-08-31 | 82 | 84 | -2 | | 2026-08-31 | 2026-09-07 | 114 | 82 | 32 | | 2026-09-07 | 2026-09-14 | 93 | 114 | -21 | | 2026-09-14 | 2026-09-21 | 76 | 93 | -17 | | 2026-09-21 | 2026-09-28 | 84 | 76 | 8 | | 2026-09-28 | 2026-10-05 | 62 | 84 | -22 | ### Figure 14. Community health worker visits: count per week [53] | period | value | | --- | --- | | 2026-03-30 | 26 | | 2026-04-06 | 42 | | 2026-04-13 | 34 | | 2026-04-20 | 37 | | 2026-04-27 | 41 | | 2026-05-04 | 43 | | 2026-05-11 | 29 | | 2026-05-18 | 44 | | 2026-05-25 | 37 | | 2026-06-01 | 31 | | 2026-06-08 | 31 | | 2026-06-15 | 38 | | 2026-06-22 | 39 | | 2026-06-29 | 40 | | 2026-07-06 | 49 | | 2026-07-13 | 47 | | 2026-07-20 | 39 | | 2026-07-27 | 37 | | 2026-08-03 | 38 | | 2026-08-10 | 43 | | 2026-08-17 | 40 | | 2026-08-24 | 37 | | 2026-08-31 | 32 | | 2026-09-07 | 38 | | 2026-09-14 | 34 | | 2026-09-21 | 36 | | 2026-09-28 | 18 | ### Figure 15. Community health worker visits: week of 2026-09-21 to week of 2026-09-28 by place [54] | group | period | value | | --- | --- | --- | | Canal Side sub-district | week of 2026-09-21 | 5 | | Canal Side sub-district | week of 2026-09-28 | 1 | | Fernhill sub-district | week of 2026-09-21 | 6 | | Fernhill sub-district | week of 2026-09-28 | 2 | | Eastfield sub-district | week of 2026-09-21 | 5 | | Eastfield sub-district | week of 2026-09-28 | 3 | | Foundry Row sub-district | week of 2026-09-21 | 2 | | Foundry Row sub-district | week of 2026-09-28 | 0 | | Glenwood sub-district | week of 2026-09-21 | 2 | | Glenwood sub-district | week of 2026-09-28 | 0 | | Parkside sub-district | week of 2026-09-21 | 3 | | Parkside sub-district | week of 2026-09-28 | 1 | | Riverside sub-district | week of 2026-09-21 | 1 | | Riverside sub-district | week of 2026-09-28 | 3 | | Cedar Vale sub-district | week of 2026-09-21 | 1 | | Cedar Vale sub-district | week of 2026-09-28 | 0 | | Hawthorn Rise sub-district | week of 2026-09-21 | 1 | | Hawthorn Rise sub-district | week of 2026-09-28 | 0 | | Millbrook sub-district | week of 2026-09-21 | 2 | | Millbrook sub-district | week of 2026-09-28 | 1 | | Oakwood sub-district | week of 2026-09-21 | 2 | | Oakwood sub-district | week of 2026-09-28 | 3 | | Station Quarter sub-district | week of 2026-09-21 | 2 | | Station Quarter sub-district | week of 2026-09-28 | 1 | | Westbrook sub-district | week of 2026-09-21 | 1 | | Westbrook sub-district | week of 2026-09-28 | 0 | | Market Hill sub-district | week of 2026-09-21 | 3 | | Market Hill sub-district | week of 2026-09-28 | 3 | ### Figure 16. Period change: Community health worker visits (week of 2026-09-21 to week of 2026-09-28) [55] | bin_start | bin_end | count | | --- | --- | --- | | -1.633 | -1.106 | 4 | | -1.106 | -0.5798 | 5 | | -0.5798 | -0.0532 | 2 | | -0.0532 | 0.4734 | 2 | | 0.4734 | 1 | 1 | ### Figure 18. Vaccination sessions: count per week [57] | period | value | | --- | --- | | 2026-01-05 | 8 | | 2026-01-12 | 5 | | 2026-01-19 | 4 | | 2026-01-26 | 3 | | 2026-02-02 | 6 | | 2026-02-09 | 5 | | 2026-02-16 | 6 | | 2026-02-23 | 9 | | 2026-03-02 | 5 | | 2026-03-09 | 2 | | 2026-03-16 | 3 | | 2026-03-23 | 6 | | 2026-03-30 | 5 | | 2026-04-06 | 7 | | 2026-04-13 | 9 | | 2026-04-20 | 4 | | 2026-04-27 | 0 | | 2026-05-04 | 1 | | 2026-05-11 | 7 | | 2026-05-18 | 2 | | 2026-05-25 | 5 | | 2026-06-01 | 3 | | 2026-06-08 | 9 | | 2026-06-15 | 6 | | 2026-06-22 | 11 | | 2026-06-29 | 9 | | 2026-07-06 | 2 | | 2026-07-13 | 4 | | 2026-07-20 | 5 | | 2026-07-27 | 7 | | 2026-08-03 | 5 | | 2026-08-10 | 3 | | 2026-08-17 | 3 | | 2026-08-24 | 6 | | 2026-08-31 | 5 | | 2026-09-07 | 4 | | 2026-09-14 | 6 | | 2026-09-21 | 5 | | 2026-09-28 | 5 | ### Figure 19. Vaccination sessions: week of 2026-06-29 to week of 2026-07-06 by place [58] | group | period | value | | --- | --- | --- | | Fernhill sub-district | week of 2026-06-29 | 2 | | Fernhill sub-district | week of 2026-07-06 | 0 | | Brickfield sub-district | week of 2026-06-29 | 1 | | Brickfield sub-district | week of 2026-07-06 | 0 | | Canal Side sub-district | week of 2026-06-29 | 0 | | Canal Side sub-district | week of 2026-07-06 | 1 | | Foundry Row sub-district | week of 2026-06-29 | 0 | | Foundry Row sub-district | week of 2026-07-06 | 1 | | Glenwood sub-district | week of 2026-06-29 | 1 | | Glenwood sub-district | week of 2026-07-06 | 0 | | Ivy Bank sub-district | week of 2026-06-29 | 1 | | Ivy Bank sub-district | week of 2026-07-06 | 0 | | Market Hill sub-district | week of 2026-06-29 | 1 | | Market Hill sub-district | week of 2026-07-06 | 0 | | Oakwood sub-district | week of 2026-06-29 | 1 | | Oakwood sub-district | week of 2026-07-06 | 0 | | Parkside sub-district | week of 2026-06-29 | 1 | | Parkside sub-district | week of 2026-07-06 | 0 | | Station Quarter sub-district | week of 2026-06-29 | 1 | | Station Quarter sub-district | week of 2026-07-06 | 0 | ### Figure 20. Period change: Vaccination sessions (week of 2026-06-29 to week of 2026-07-06) [59] | bin_start | bin_end | count | | --- | --- | --- | | -1.414 | -0.9314 | 8 | | -0.9314 | -0.4485 | 0 | | -0.4485 | 0.03432 | 0 | | 0.03432 | 0.5172 | 0 | | 0.5172 | 1 | 2 | ### Figure 22. Cases: count per week [61] | period | value | | --- | --- | | 2025-09-29 | 16 | | 2025-10-06 | 25 | | 2025-10-13 | 20 | | 2025-10-20 | 39 | | 2025-10-27 | 25 | | 2025-11-03 | 38 | | 2025-11-10 | 50 | | 2025-11-17 | 60 | | 2025-11-24 | 91 | | 2025-12-01 | 63 | | 2025-12-08 | 55 | | 2025-12-15 | 70 | | 2025-12-22 | 52 | | 2025-12-29 | 39 | | 2026-01-05 | 44 | | 2026-01-12 | 36 | | 2026-01-19 | 28 | | 2026-01-26 | 30 | | 2026-02-02 | 26 | | 2026-02-09 | 17 | | 2026-02-16 | 21 | | 2026-02-23 | 29 | | 2026-03-02 | 25 | | 2026-03-09 | 23 | | 2026-03-16 | 29 | | 2026-03-23 | 29 | | 2026-03-30 | 21 | | 2026-04-06 | 40 | | 2026-04-13 | 55 | | 2026-04-20 | 60 | | 2026-04-27 | 89 | | 2026-05-04 | 128 | | 2026-05-11 | 166 | | 2026-05-18 | 136 | | 2026-05-25 | 86 | | 2026-06-01 | 60 | | 2026-06-08 | 29 | | 2026-06-15 | 36 | | 2026-06-22 | 26 | | 2026-06-29 | 37 | | 2026-07-06 | 27 | | 2026-07-13 | 28 | | 2026-07-20 | 32 | | 2026-07-27 | 28 | | 2026-08-03 | 36 | | 2026-08-10 | 25 | | 2026-08-17 | 23 | | 2026-08-24 | 21 | | 2026-08-31 | 24 | | 2026-09-07 | 33 | | 2026-09-14 | 18 | | 2026-09-21 | 25 | | 2026-09-28 | 21 | ### Figure 23. Cases: week of 2026-05-18 to week of 2026-05-25 by place [62] | group | period | value | | --- | --- | --- | | Parkside sub-district | week of 2026-05-18 | 77 | | Parkside sub-district | week of 2026-05-25 | 39 | | Fernhill sub-district | week of 2026-05-18 | 12 | | Fernhill sub-district | week of 2026-05-25 | 6 | | Oakwood sub-district | week of 2026-05-18 | 6 | | Oakwood sub-district | week of 2026-05-25 | 2 | | Foundry Row sub-district | week of 2026-05-18 | 5 | | Foundry Row sub-district | week of 2026-05-25 | 8 | | Hawthorn Rise sub-district | week of 2026-05-18 | 3 | | Hawthorn Rise sub-district | week of 2026-05-25 | 0 | | Riverside sub-district | week of 2026-05-18 | 3 | | Riverside sub-district | week of 2026-05-25 | 0 | | Station Quarter sub-district | week of 2026-05-18 | 4 | | Station Quarter sub-district | week of 2026-05-25 | 1 | | Eastfield sub-district | week of 2026-05-18 | 6 | | Eastfield sub-district | week of 2026-05-25 | 8 | | Market Hill sub-district | week of 2026-05-18 | 4 | | Market Hill sub-district | week of 2026-05-25 | 6 | | Ashby Green sub-district | week of 2026-05-18 | 1 | | Ashby Green sub-district | week of 2026-05-25 | 2 | | Brickfield sub-district | week of 2026-05-18 | 1 | | Brickfield sub-district | week of 2026-05-25 | 0 | | Elmstead sub-district | week of 2026-05-18 | 1 | | Elmstead sub-district | week of 2026-05-25 | 2 | | Ivy Bank sub-district | week of 2026-05-18 | 1 | | Ivy Bank sub-district | week of 2026-05-25 | 0 | | Millbrook sub-district | week of 2026-05-18 | 2 | | Millbrook sub-district | week of 2026-05-25 | 3 | | Westbrook sub-district | week of 2026-05-18 | 3 | | Westbrook sub-district | week of 2026-05-25 | 2 | ### Figure 24. Period change: Cases (week of 2026-05-18 to week of 2026-05-25) [63] | bin_start | bin_end | count | | --- | --- | --- | | -3.528 | -2.801 | 1 | | -2.801 | -2.075 | 0 | | -2.075 | -1.348 | 4 | | -1.348 | -0.6213 | 3 | | -0.6213 | 0.1054 | 4 | | 0.1054 | 0.8321 | 6 | ### Figure 32. Change beside the counts per health sub-district [91] | bin_start | bin_end | count | | --- | --- | --- | | -3.528 | -2.801 | 1 | | -2.801 | -2.075 | 0 | | -2.075 | -1.348 | 4 | | -1.348 | -0.6213 | 3 | | -0.6213 | 0.1054 | 4 | | 0.1054 | 0.8321 | 6 | ### Figure 34. Change of vaccination sessions where density is high [96] | bin_start | bin_end | count | | --- | --- | --- | | -1.414 | -0.9314 | 8 | | -0.9314 | -0.4485 | 0 | | -0.4485 | 0.03432 | 0 | | 0.03432 | 0.5172 | 0 | | 0.5172 | 1 | 2 | ### Figure 36. Hot cells of cases per health sub-district [101] | bin_start | bin_end | count | | --- | --- | --- | | 73 | 120.5 | 9 | | 120.5 | 168 | 5 | | 168 | 215.5 | 2 | | 215.5 | 263 | 1 | | 263 | 310.5 | 0 | | 310.5 | 358 | 1 | ### Figure 38. Nearest outbreak response team to each cluster of cases [107] | rank | name | | --- | --- | | 1 | Rapid response team 1 | ### Figure 39. Dense areas of community health worker visits [112] | bin_start | bin_end | count | | --- | --- | --- | | 0 | 0.8 | 1 | | 0.8 | 1.6 | 1 | | 1.6 | 2.4 | 1 | | 2.4 | 3.2 | 1 | | 3.2 | 4 | 1 | ### Figure 41. Change of vaccination sessions per health sub-district [116] | bin_start | bin_end | count | | --- | --- | --- | | 0 | 0.1667 | 8 | | 0.1667 | 0.3333 | 0 | | 0.3333 | 0.5 | 0 | | 0.5 | 0.6667 | 0 | | 0.6667 | 0.8333 | 0 | | 0.8333 | 1 | 10 | ### Figure 43. Outbreak response teams within 250 m of each cluster of cases [121] | cluster | features | fraction | share | | --- | --- | --- | --- | | 0 | 1 | 1 | 100.0% | ### Figure 44. Density of community health worker visits and cases together [126] | bin_start | bin_end | count | | --- | --- | --- | | 0 | 3.786 | 295 | | 3.786 | 7.571 | 385 | | 7.571 | 11.36 | 389 | | 11.36 | 15.14 | 384 | | 15.14 | 18.93 | 284 | | 18.93 | 22.71 | 378 | | 22.71 | 26.5 | 380 | | 26.5 | 30.29 | 383 | | 30.29 | 34.07 | 381 | | 34.07 | 37.86 | 279 | | 37.86 | 41.64 | 378 | | 41.64 | 45.43 | 382 | | 45.43 | 49.21 | 375 | | 49.21 | 53 | 287 | ### Figure 46. Cases over time (onset_date) [128] | period | series | value | | --- | --- | --- | | 2025-09-29 | malaria | 13 | | 2025-09-29 | cholera | 0 | | 2025-09-29 | typhoid | 3 | | 2025-10-06 | malaria | 23 | | 2025-10-06 | cholera | 0 | | 2025-10-06 | typhoid | 2 | | 2025-10-13 | malaria | 19 | | 2025-10-13 | cholera | 0 | | 2025-10-13 | typhoid | 1 | | 2025-10-20 | malaria | 35 | | 2025-10-20 | cholera | 0 | | 2025-10-20 | typhoid | 4 | | 2025-10-27 | malaria | 20 | | 2025-10-27 | cholera | 0 | | 2025-10-27 | typhoid | 5 | | 2025-11-03 | malaria | 34 | | 2025-11-03 | cholera | 0 | | 2025-11-03 | typhoid | 4 | | 2025-11-10 | malaria | 46 | | 2025-11-10 | cholera | 0 | | 2025-11-10 | typhoid | 4 | | 2025-11-17 | malaria | 59 | | 2025-11-17 | cholera | 0 | | 2025-11-17 | typhoid | 1 | | 2025-11-24 | malaria | 87 | | 2025-11-24 | cholera | 0 | | 2025-11-24 | typhoid | 4 | | 2025-12-01 | malaria | 57 | | 2025-12-01 | cholera | 0 | | 2025-12-01 | typhoid | 6 | | 2025-12-08 | malaria | 50 | | 2025-12-08 | cholera | 0 | | 2025-12-08 | typhoid | 5 | | 2025-12-15 | malaria | 66 | | 2025-12-15 | cholera | 0 | | 2025-12-15 | typhoid | 4 | | 2025-12-22 | malaria | 46 | | 2025-12-22 | cholera | 0 | | 2025-12-22 | typhoid | 6 | | 2025-12-29 | malaria | 33 | | 2025-12-29 | cholera | 0 | | 2025-12-29 | typhoid | 6 | | 2026-01-05 | malaria | 38 | | 2026-01-05 | cholera | 0 | | 2026-01-05 | typhoid | 6 | | 2026-01-12 | malaria | 32 | | 2026-01-12 | cholera | 0 | | 2026-01-12 | typhoid | 4 | | 2026-01-19 | malaria | 23 | | 2026-01-19 | cholera | 0 | | 2026-01-19 | typhoid | 5 | | 2026-01-26 | malaria | 24 | | 2026-01-26 | cholera | 0 | | 2026-01-26 | typhoid | 6 | | 2026-02-02 | malaria | 23 | | 2026-02-02 | cholera | 0 | | 2026-02-02 | typhoid | 3 | | 2026-02-09 | malaria | 11 | | 2026-02-09 | cholera | 0 | | 2026-02-09 | typhoid | 6 | | 2026-02-16 | malaria | 18 | | 2026-02-16 | cholera | 0 | | 2026-02-16 | typhoid | 3 | | 2026-02-23 | malaria | 24 | | 2026-02-23 | cholera | 0 | | 2026-02-23 | typhoid | 5 | | 2026-03-02 | malaria | 19 | | 2026-03-02 | cholera | 0 | | 2026-03-02 | typhoid | 6 | | 2026-03-09 | malaria | 17 | | 2026-03-09 | cholera | 0 | | 2026-03-09 | typhoid | 6 | | 2026-03-16 | malaria | 25 | | 2026-03-16 | cholera | 0 | | 2026-03-16 | typhoid | 4 | | 2026-03-23 | malaria | 25 | | 2026-03-23 | cholera | 0 | | 2026-03-23 | typhoid | 4 | | 2026-03-30 | malaria | 18 | | 2026-03-30 | cholera | 0 | | 2026-03-30 | typhoid | 3 | | 2026-04-06 | malaria | 36 | | 2026-04-06 | cholera | 0 | | 2026-04-06 | typhoid | 4 | | 2026-04-13 | malaria | 44 | | 2026-04-13 | cholera | 0 | | 2026-04-13 | typhoid | 11 | | 2026-04-20 | malaria | 51 | | 2026-04-20 | cholera | 1 | | 2026-04-20 | typhoid | 8 | | 2026-04-27 | malaria | 58 | | 2026-04-27 | cholera | 25 | | 2026-04-27 | typhoid | 6 | | 2026-05-04 | malaria | 60 | | 2026-05-04 | cholera | 63 | | 2026-05-04 | typhoid | 5 | | 2026-05-11 | malaria | 55 | | 2026-05-11 | cholera | 109 | | 2026-05-11 | typhoid | 2 | | 2026-05-18 | malaria | 52 | | 2026-05-18 | cholera | 77 | | 2026-05-18 | typhoid | 7 | | 2026-05-25 | malaria | 45 | | 2026-05-25 | cholera | 37 | | 2026-05-25 | typhoid | 4 | | 2026-06-01 | malaria | 43 | | 2026-06-01 | cholera | 14 | | 2026-06-01 | typhoid | 3 | | 2026-06-08 | malaria | 21 | | 2026-06-08 | cholera | 4 | | 2026-06-08 | typhoid | 4 | | 2026-06-15 | malaria | 28 | | 2026-06-15 | cholera | 0 | | 2026-06-15 | typhoid | 8 | | 2026-06-22 | malaria | 20 | | 2026-06-22 | cholera | 0 | | 2026-06-22 | typhoid | 6 | | 2026-06-29 | malaria | 24 | | 2026-06-29 | cholera | 0 | | 2026-06-29 | typhoid | 13 | | 2026-07-06 | malaria | 22 | | 2026-07-06 | cholera | 0 | | 2026-07-06 | typhoid | 5 | | 2026-07-13 | malaria | 23 | | 2026-07-13 | cholera | 0 | | 2026-07-13 | typhoid | 5 | | 2026-07-20 | malaria | 28 | | 2026-07-20 | cholera | 0 | | 2026-07-20 | typhoid | 4 | | 2026-07-27 | malaria | 21 | | 2026-07-27 | cholera | 0 | | 2026-07-27 | typhoid | 7 | | 2026-08-03 | malaria | 31 | | 2026-08-03 | cholera | 0 | | 2026-08-03 | typhoid | 5 | | 2026-08-10 | malaria | 22 | | 2026-08-10 | cholera | 0 | | 2026-08-10 | typhoid | 3 | | 2026-08-17 | malaria | 18 | | 2026-08-17 | cholera | 0 | | 2026-08-17 | typhoid | 5 | | 2026-08-24 | malaria | 15 | | 2026-08-24 | cholera | 0 | | 2026-08-24 | typhoid | 6 | | 2026-08-31 | malaria | 21 | | 2026-08-31 | cholera | 0 | | 2026-08-31 | typhoid | 3 | | 2026-09-07 | malaria | 25 | | 2026-09-07 | cholera | 0 | | 2026-09-07 | typhoid | 8 | | 2026-09-14 | malaria | 15 | | 2026-09-14 | cholera | 0 | | 2026-09-14 | typhoid | 3 | | 2026-09-21 | malaria | 19 | | 2026-09-21 | cholera | 0 | | 2026-09-21 | typhoid | 6 | | 2026-09-28 | malaria | 18 | | 2026-09-28 | cholera | 0 | | 2026-09-28 | typhoid | 3 | ### Figure 47. Clinic stock by month over time (month) [129] | period | value | | --- | --- | | 2025-12-29 | 35 | | 2026-01-05 | 0 | | 2026-01-12 | 0 | | 2026-01-19 | 0 | | 2026-01-26 | 35 | | 2026-02-02 | 0 | | 2026-02-09 | 0 | | 2026-02-16 | 0 | | 2026-02-23 | 35 | | 2026-03-02 | 0 | | 2026-03-09 | 0 | | 2026-03-16 | 0 | | 2026-03-23 | 0 | | 2026-03-30 | 35 | | 2026-04-06 | 0 | | 2026-04-13 | 0 | | 2026-04-20 | 0 | | 2026-04-27 | 35 | | 2026-05-04 | 0 | | 2026-05-11 | 0 | | 2026-05-18 | 0 | | 2026-05-25 | 0 | | 2026-06-01 | 35 | | 2026-06-08 | 0 | | 2026-06-15 | 0 | | 2026-06-22 | 0 | | 2026-06-29 | 35 | | 2026-07-06 | 0 | | 2026-07-13 | 0 | | 2026-07-20 | 0 | | 2026-07-27 | 35 | | 2026-08-03 | 0 | | 2026-08-10 | 0 | | 2026-08-17 | 0 | | 2026-08-24 | 0 | | 2026-08-31 | 35 | ### Figure 48. Emerging hot spots: Community health worker visits [130] | bin_start | bin_end | count | | --- | --- | --- | | -1.156 | -0.4926 | 60 | | -0.4926 | 0.1708 | 26 | | 0.1708 | 0.8341 | 18 | | 0.8341 | 1.498 | 10 | | 1.498 | 2.161 | 4 | | 2.161 | 2.824 | 5 | | 2.824 | 3.488 | 0 | | 3.488 | 4.151 | 1 | ### Figure 49. Emerging hot spots: Vaccination sessions [131] | bin_start | bin_end | count | | --- | --- | --- | | -0.832 | -0.3452 | 3 | | -0.3452 | 0.1417 | 0 | | 0.1417 | 0.6285 | 11 | | 0.6285 | 1.115 | 6 | | 1.115 | 1.602 | 0 | | 1.602 | 2.089 | 4 | ### Figure 50. Emerging hot spots: Cases [132] | bin_start | bin_end | count | | --- | --- | --- | | -0.628 | -0.4474 | 32 | | -0.4474 | -0.2667 | 48 | | -0.2667 | -0.08612 | 0 | | -0.08612 | 0.0945 | 0 | | 0.0945 | 0.2751 | 23 | | 0.2751 | 0.4557 | 0 | | 0.4557 | 0.6364 | 15 | | 0.6364 | 0.817 | 7 | ### Figure 51. Hot spot analysis: Community health worker visits [133] | bin_start | bin_end | band | band_label | count | | --- | --- | --- | --- | --- | | -3.5 | -3 | cold_99 | Cold spot, 99% confidence | 1 | | -3 | -2.5 | cold_95 | Cold spot, 95% confidence | 3 | | -3 | -2.5 | cold_99 | Cold spot, 99% confidence | 6 | | -2.5 | -2 | cold_95 | Cold spot, 95% confidence | 7 | | -2 | -1.5 | cold_90 | Cold spot, 90% confidence | 14 | | -2 | -1.5 | cold_95 | Cold spot, 95% confidence | 4 | | -2 | -1.5 | not_significant | Not significant | 12 | | -1.5 | -1 | not_significant | Not significant | 31 | | -1 | -0.5 | not_significant | Not significant | 48 | | -0.5 | 0 | not_significant | Not significant | 59 | | 0 | 0.5 | not_significant | Not significant | 57 | | 0.5 | 1 | not_significant | Not significant | 52 | | 1 | 1.5 | not_significant | Not significant | 40 | | 1.5 | 2 | hot_90 | Hot spot, 90% confidence | 15 | | 1.5 | 2 | not_significant | Not significant | 3 | | 2 | 2.5 | hot_95 | Hot spot, 95% confidence | 15 | | 2.5 | 3 | hot_99 | Hot spot, 99% confidence | 7 | | 3 | 3.5 | hot_99 | Hot spot, 99% confidence | 8 | | 3.5 | 4 | hot_99 | Hot spot, 99% confidence | 5 | | 4 | 4.5 | hot_99 | Hot spot, 99% confidence | 2 | | 4.5 | 5 | hot_99 | Hot spot, 99% confidence | 1 | ### Figure 52. Hot spot analysis: Cases [134] | bin_start | bin_end | band | band_label | count | | --- | --- | --- | --- | --- | | -3 | -2.5 | cold_99 | Cold spot, 99% confidence | 1 | | -2.5 | -2 | cold_95 | Cold spot, 95% confidence | 31 | | -2 | -1.5 | cold_90 | Cold spot, 90% confidence | 88 | | -2 | -1.5 | cold_95 | Cold spot, 95% confidence | 17 | | -2 | -1.5 | not_significant | Not significant | 17 | | -1.5 | -1 | not_significant | Not significant | 401 | | -1 | -0.5 | not_significant | Not significant | 509 | | -0.5 | 0 | not_significant | Not significant | 472 | | 0 | 0.5 | not_significant | Not significant | 351 | | 0.5 | 1 | not_significant | Not significant | 177 | | 1 | 1.5 | not_significant | Not significant | 75 | | 1.5 | 2 | hot_90 | Hot spot, 90% confidence | 12 | | 1.5 | 2 | not_significant | Not significant | 11 | | 2 | 2.5 | hot_95 | Hot spot, 95% confidence | 7 | | 2.5 | 3 | hot_95 | Hot spot, 95% confidence | 2 | | 2.5 | 3 | hot_99 | Hot spot, 99% confidence | 2 | | 3 | 3.5 | hot_99 | Hot spot, 99% confidence | 2 | | 3.5 | 4 | hot_99 | Hot spot, 99% confidence | 9 | | 4 | 4.5 | hot_99 | Hot spot, 99% confidence | 8 | | 4.5 | 5 | hot_99 | Hot spot, 99% confidence | 2 | | 5 | 5.5 | hot_99 | Hot spot, 99% confidence | 3 | | 5.5 | 6 | hot_99 | Hot spot, 99% confidence | 4 | | 6 | 6.5 | hot_99 | Hot spot, 99% confidence | 3 | | 6.5 | 7 | hot_99 | Hot spot, 99% confidence | 2 | | 7 | 7.5 | hot_99 | Hot spot, 99% confidence | 3 | | 7.5 | 8 | hot_99 | Hot spot, 99% confidence | 1 | | 8 | 8.5 | hot_99 | Hot spot, 99% confidence | 4 | | 8.5 | 9 | hot_99 | Hot spot, 99% confidence | 2 | | 9 | 9.5 | hot_99 | Hot spot, 99% confidence | 4 | | 9.5 | 10 | hot_99 | Hot spot, 99% confidence | 2 | | 10 | 10.5 | hot_99 | Hot spot, 99% confidence | 3 | | 10.5 | 11 | hot_99 | Hot spot, 99% confidence | 2 | | 11 | 11.5 | hot_99 | Hot spot, 99% confidence | 1 | | 12 | 12.5 | hot_99 | Hot spot, 99% confidence | 2 | | 13 | 13.5 | hot_99 | Hot spot, 99% confidence | 1 | | 13.5 | 14 | hot_99 | Hot spot, 99% confidence | 1 | | 14 | 14.5 | hot_99 | Hot spot, 99% confidence | 3 | | 14.5 | 15 | hot_99 | Hot spot, 99% confidence | 1 | | 15 | 15.5 | hot_99 | Hot spot, 99% confidence | 1 | | 15.5 | 16 | hot_99 | Hot spot, 99% confidence | 1 | | 16 | 16.5 | hot_99 | Hot spot, 99% confidence | 2 | | 17 | 17.5 | hot_99 | Hot spot, 99% confidence | 2 | | 18 | 18.5 | hot_99 | Hot spot, 99% confidence | 2 | | 18.5 | 19 | hot_99 | Hot spot, 99% confidence | 1 | | 19 | 19.5 | hot_99 | Hot spot, 99% confidence | 1 | | 19.5 | 20 | hot_99 | Hot spot, 99% confidence | 1 | | 20 | 20.5 | hot_99 | Hot spot, 99% confidence | 2 | | 22.5 | 23 | hot_99 | Hot spot, 99% confidence | 4 | | 23 | 23.5 | hot_99 | Hot spot, 99% confidence | 1 | | 24 | 24.5 | hot_99 | Hot spot, 99% confidence | 2 | | 24.5 | 25 | hot_99 | Hot spot, 99% confidence | 1 | | 25.5 | 26 | hot_99 | Hot spot, 99% confidence | 1 | | 26 | 26.5 | hot_99 | Hot spot, 99% confidence | 1 | ### Figure 53. Hot spot analysis: Vaccination sessions [135] | bin_start | bin_end | band | band_label | count | | --- | --- | --- | --- | --- | | -2.5 | -2 | cold_95 | Cold spot, 95% confidence | 1 | | -2 | -1.5 | cold_90 | Cold spot, 90% confidence | 4 | | -1.5 | -1 | not_significant | Not significant | 15 | | -1 | -0.5 | not_significant | Not significant | 22 | | -0.5 | 0 | not_significant | Not significant | 20 | | 0 | 0.5 | not_significant | Not significant | 8 | | 0.5 | 1 | not_significant | Not significant | 8 | | 1 | 1.5 | not_significant | Not significant | 3 | | 1.5 | 2 | hot_90 | Hot spot, 90% confidence | 6 | | 2.5 | 3 | hot_99 | Hot spot, 99% confidence | 2 | | 3 | 3.5 | hot_99 | Hot spot, 99% confidence | 4 | | 3.5 | 4 | hot_99 | Hot spot, 99% confidence | 3 | | 5 | 5.5 | hot_99 | Hot spot, 99% confidence | 2 | | 5.5 | 6 | hot_99 | Hot spot, 99% confidence | 1 | ### Figure 54. Summarize within: Health sub-districts [136] | bin_start | bin_end | count | | --- | --- | --- | | 14 | 36.67 | 7 | | 36.67 | 59.33 | 3 | | 59.33 | 82 | 6 | | 82 | 104.7 | 1 | | 104.7 | 127.3 | 0 | | 127.3 | 150 | 1 | ### Figure 55. Summarize within: Health sub-districts [137] | bin_start | bin_end | count | | --- | --- | --- | | 3 | 7.833 | 8 | | 7.833 | 12.67 | 5 | | 12.67 | 17.5 | 1 | | 17.5 | 22.33 | 2 | | 22.33 | 27.17 | 1 | | 27.17 | 32 | 1 | ### Figure 56. Summarize within: Health sub-districts [138] | bin_start | bin_end | count | | --- | --- | --- | | 33 | 113 | 10 | | 113 | 193 | 6 | | 193 | 273 | 0 | | 273 | 353 | 1 | | 353 | 433 | 0 | | 433 | 513 | 1 | ### Figure 57. Calculate density: Community health worker visits [139] | bin_start | bin_end | count | | --- | --- | --- | | 0 | 4.077 | 198 | | 4.077 | 8.154 | 193 | | 8.154 | 12.23 | 192 | | 12.23 | 16.31 | 191 | | 16.31 | 20.38 | 188 | | 20.38 | 24.46 | 186 | | 24.46 | 28.54 | 190 | | 28.54 | 32.62 | 193 | | 32.62 | 36.69 | 187 | | 36.69 | 40.77 | 189 | | 40.77 | 44.85 | 189 | | 44.85 | 48.92 | 187 | | 48.92 | 53 | 169 | ### Figure 58. Calculate density: Cases [140] | bin_start | bin_end | count | | --- | --- | --- | | 0 | 4.077 | 193 | | 4.077 | 8.154 | 194 | | 8.154 | 12.23 | 196 | | 12.23 | 16.31 | 190 | | 16.31 | 20.38 | 192 | | 20.38 | 24.46 | 189 | | 24.46 | 28.54 | 194 | | 28.54 | 32.62 | 192 | | 32.62 | 36.69 | 187 | | 36.69 | 40.77 | 187 | | 40.77 | 44.85 | 192 | | 44.85 | 48.92 | 190 | | 48.92 | 53 | 212 | ### Figure 59. Calculate density: Vaccination sessions [141] | bin_start | bin_end | count | | --- | --- | --- | | 0 | 2.455 | 49 | | 2.455 | 4.909 | 47 | | 4.909 | 7.364 | 71 | | 7.364 | 9.818 | 46 | | 9.818 | 12.27 | 72 | | 12.27 | 14.73 | 49 | | 14.73 | 17.18 | 73 | | 17.18 | 19.64 | 48 | | 19.64 | 22.09 | 75 | | 22.09 | 24.55 | 46 | | 24.55 | 27 | 40 | ### Figure 60. Find point clusters: Cases [142] | cluster | features | fraction | share | | --- | --- | --- | --- | | Noise | 1,961 | 0.8563 | 85.6% | | 0 | 329 | 0.1437 | 14.4% | ### Figure 61. Find point clusters: cluster hulls [143] | cluster | features | fraction | share | | --- | --- | --- | --- | | 0 | 1 | 1 | 100.0% | ### Figure 62. Find outliers: Health sub-districts [144] | value | lag | type | | --- | --- | --- | | 1.279 | -0.6096 | NS | | -0.322 | -0.1926 | NS | | 0.3843 | -0.2891 | NS | | -0.4745 | -0.8491 | NS | | -0.3255 | 1.301 | NS | | -0.1949 | 0.5765 | NS | | 1.301 | -0.2602 | NS | | -0.8491 | -0.7215 | NS | | -0.7074 | -0.9158 | NS | | -0.2891 | 0.3834 | NS | | 0.7505 | -0.2687 | NS | | 0.0154 | -0.3876 | NS | | -0.9826 | -0.6102 | NS | | -0.5515 | -0.4775 | NS | | 3.066 | -0.8691 | NS | | -0.6096 | 1.279 | NS | | -0.8691 | 3.066 | NS | | -0.6209 | -0.7671 | NS | ### Figure 63. Find point clusters: Cases over time [145] | period | series | value | | --- | --- | --- | | 2025-09-29 | F | 8 | | 2025-09-29 | M | 8 | | 2025-10-06 | F | 8 | | 2025-10-06 | M | 17 | | 2025-10-13 | F | 16 | | 2025-10-13 | M | 4 | | 2025-10-20 | F | 19 | | 2025-10-20 | M | 20 | | 2025-10-27 | F | 13 | | 2025-10-27 | M | 12 | | 2025-11-03 | F | 17 | | 2025-11-03 | M | 21 | | 2025-11-10 | F | 23 | | 2025-11-10 | M | 27 | | 2025-11-17 | F | 37 | | 2025-11-17 | M | 23 | | 2025-11-24 | F | 43 | | 2025-11-24 | M | 48 | | 2025-12-01 | F | 25 | | 2025-12-01 | M | 38 | | 2025-12-08 | F | 30 | | 2025-12-08 | M | 25 | | 2025-12-15 | F | 35 | | 2025-12-15 | M | 35 | | 2025-12-22 | F | 23 | | 2025-12-22 | M | 29 | | 2025-12-29 | F | 25 | | 2025-12-29 | M | 14 | | 2026-01-05 | F | 25 | | 2026-01-05 | M | 19 | | 2026-01-12 | F | 16 | | 2026-01-12 | M | 20 | | 2026-01-19 | F | 12 | | 2026-01-19 | M | 16 | | 2026-01-26 | F | 14 | | 2026-01-26 | M | 16 | | 2026-02-02 | F | 11 | | 2026-02-02 | M | 15 | | 2026-02-09 | F | 8 | | 2026-02-09 | M | 9 | | 2026-02-16 | F | 13 | | 2026-02-16 | M | 8 | | 2026-02-23 | F | 15 | | 2026-02-23 | M | 14 | | 2026-03-02 | F | 13 | | 2026-03-02 | M | 12 | | 2026-03-09 | F | 15 | | 2026-03-09 | M | 8 | | 2026-03-16 | F | 17 | | 2026-03-16 | M | 12 | | 2026-03-23 | F | 12 | | 2026-03-23 | M | 17 | | 2026-03-30 | F | 9 | | 2026-03-30 | M | 12 | | 2026-04-06 | F | 26 | | 2026-04-06 | M | 14 | | 2026-04-13 | F | 26 | | 2026-04-13 | M | 29 | | 2026-04-20 | F | 36 | | 2026-04-20 | M | 24 | | 2026-04-27 | F | 50 | | 2026-04-27 | M | 39 | | 2026-05-04 | F | 65 | | 2026-05-04 | M | 63 | | 2026-05-11 | F | 86 | | 2026-05-11 | M | 80 | | 2026-05-18 | F | 75 | | 2026-05-18 | M | 61 | | 2026-05-25 | F | 41 | | 2026-05-25 | M | 45 | | 2026-06-01 | F | 29 | | 2026-06-01 | M | 31 | | 2026-06-08 | F | 13 | | 2026-06-08 | M | 16 | | 2026-06-15 | F | 19 | | 2026-06-15 | M | 17 | | 2026-06-22 | F | 11 | | 2026-06-22 | M | 15 | | 2026-06-29 | F | 25 | | 2026-06-29 | M | 12 | | 2026-07-06 | F | 14 | | 2026-07-06 | M | 13 | | 2026-07-13 | F | 9 | | 2026-07-13 | M | 19 | | 2026-07-20 | F | 19 | | 2026-07-20 | M | 13 | | 2026-07-27 | F | 11 | | 2026-07-27 | M | 17 | | 2026-08-03 | F | 15 | | 2026-08-03 | M | 21 | | 2026-08-10 | F | 12 | | 2026-08-10 | M | 13 | | 2026-08-17 | F | 10 | | 2026-08-17 | M | 13 | | 2026-08-24 | F | 5 | | 2026-08-24 | M | 16 | | 2026-08-31 | F | 14 | | 2026-08-31 | M | 10 | | 2026-09-07 | F | 21 | | 2026-09-07 | M | 12 | | 2026-09-14 | F | 6 | | 2026-09-14 | M | 12 | | 2026-09-21 | F | 9 | | 2026-09-21 | M | 16 | | 2026-09-28 | F | 5 | | 2026-09-28 | M | 16 | ### Figure 64. Find point clusters: Community health worker visits over time [146] | period | series | value | | --- | --- | --- | | 2026-03-30 | bed net check | 6 | | 2026-03-30 | referral | 6 | | 2026-03-30 | case follow-up | 8 | | 2026-03-30 | health education | 6 | | 2026-04-06 | bed net check | 11 | | 2026-04-06 | referral | 9 | | 2026-04-06 | case follow-up | 10 | | 2026-04-06 | health education | 12 | | 2026-04-13 | bed net check | 6 | | 2026-04-13 | referral | 8 | | 2026-04-13 | case follow-up | 11 | | 2026-04-13 | health education | 9 | | 2026-04-20 | bed net check | 7 | | 2026-04-20 | referral | 9 | | 2026-04-20 | case follow-up | 10 | | 2026-04-20 | health education | 11 | | 2026-04-27 | bed net check | 11 | | 2026-04-27 | referral | 12 | | 2026-04-27 | case follow-up | 9 | | 2026-04-27 | health education | 9 | | 2026-05-04 | bed net check | 16 | | 2026-05-04 | referral | 11 | | 2026-05-04 | case follow-up | 7 | | 2026-05-04 | health education | 9 | | 2026-05-11 | bed net check | 7 | | 2026-05-11 | referral | 9 | | 2026-05-11 | case follow-up | 6 | | 2026-05-11 | health education | 7 | | 2026-05-18 | bed net check | 7 | | 2026-05-18 | referral | 13 | | 2026-05-18 | case follow-up | 8 | | 2026-05-18 | health education | 16 | | 2026-05-25 | bed net check | 7 | | 2026-05-25 | referral | 10 | | 2026-05-25 | case follow-up | 14 | | 2026-05-25 | health education | 6 | | 2026-06-01 | bed net check | 8 | | 2026-06-01 | referral | 6 | | 2026-06-01 | case follow-up | 11 | | 2026-06-01 | health education | 6 | | 2026-06-08 | bed net check | 8 | | 2026-06-08 | referral | 9 | | 2026-06-08 | case follow-up | 7 | | 2026-06-08 | health education | 7 | | 2026-06-15 | bed net check | 10 | | 2026-06-15 | referral | 10 | | 2026-06-15 | case follow-up | 10 | | 2026-06-15 | health education | 8 | | 2026-06-22 | bed net check | 13 | | 2026-06-22 | referral | 11 | | 2026-06-22 | case follow-up | 4 | | 2026-06-22 | health education | 11 | | 2026-06-29 | bed net check | 6 | | 2026-06-29 | referral | 14 | | 2026-06-29 | case follow-up | 9 | | 2026-06-29 | health education | 11 | | 2026-07-06 | bed net check | 14 | | 2026-07-06 | referral | 12 | | 2026-07-06 | case follow-up | 15 | | 2026-07-06 | health education | 8 | | 2026-07-13 | bed net check | 15 | | 2026-07-13 | referral | 9 | | 2026-07-13 | case follow-up | 9 | | 2026-07-13 | health education | 14 | | 2026-07-20 | bed net check | 9 | | 2026-07-20 | referral | 11 | | 2026-07-20 | case follow-up | 4 | | 2026-07-20 | health education | 15 | | 2026-07-27 | bed net check | 16 | | 2026-07-27 | referral | 9 | | 2026-07-27 | case follow-up | 4 | | 2026-07-27 | health education | 8 | | 2026-08-03 | bed net check | 11 | | 2026-08-03 | referral | 10 | | 2026-08-03 | case follow-up | 7 | | 2026-08-03 | health education | 10 | | 2026-08-10 | bed net check | 9 | | 2026-08-10 | referral | 13 | | 2026-08-10 | case follow-up | 14 | | 2026-08-10 | health education | 7 | | 2026-08-17 | bed net check | 7 | | 2026-08-17 | referral | 12 | | 2026-08-17 | case follow-up | 9 | | 2026-08-17 | health education | 12 | | 2026-08-24 | bed net check | 8 | | 2026-08-24 | referral | 8 | | 2026-08-24 | case follow-up | 13 | | 2026-08-24 | health education | 8 | | 2026-08-31 | bed net check | 10 | | 2026-08-31 | referral | 6 | | 2026-08-31 | case follow-up | 9 | | 2026-08-31 | health education | 7 | | 2026-09-07 | bed net check | 10 | | 2026-09-07 | referral | 10 | | 2026-09-07 | case follow-up | 7 | | 2026-09-07 | health education | 11 | | 2026-09-14 | bed net check | 11 | | 2026-09-14 | referral | 7 | | 2026-09-14 | case follow-up | 10 | | 2026-09-14 | health education | 6 | | 2026-09-21 | bed net check | 9 | | 2026-09-21 | referral | 7 | | 2026-09-21 | case follow-up | 14 | | 2026-09-21 | health education | 6 | | 2026-09-28 | bed net check | 5 | | 2026-09-28 | referral | 5 | | 2026-09-28 | case follow-up | 5 | | 2026-09-28 | health education | 3 | *GeoBit Large Spatial Model. Findings are labeled Evidenced (computed by a tool), Inferred (a reading or reasoning) or Unverified (no evidence yet).*