Datasets:

electricsheepafrica
/

africa-synth-epidemics-climate-sensitive-infections-senegal

id int64	month int64	year int64	season string	monthly_mean_temp_c float64	temp_anomaly_c float64	monthly_rainfall_mm float64	rainfall_anomaly_pct float64	relative_humidity_pct int64	enso_phase string	flooding_event int64	drought_month int64	altitude_m int64	urban int64	population_density string	age_group string	sex string	disease string	climate_attributable int64	severity string	hospitalised int64	died int64	itn_use int64	vector_control_active int64	wash_improved int64	surveillance_detected int64	early_warning_issued int64	outbreak_declared int64
1	9	2,021	wet	30.5	1.27	94.4	-32.6	66	neutral	0	0	49	0	medium	50_plus	female	diarrhoeal_disease	1	mild	0	0	0	1	0	0	0	0
2	5	2,023	wet	28.2	0.59	238.3	13.3	69	el_nino	0	0	62	0	high	5_14	male	malaria	1	mild	0	0	1	0	0	0	0	0
3	5	2,023	wet	29.2	0.86	289.7	21.8	67	el_nino	1	0	58	0	high	15_49	male	cholera	1	mild	0	0	0	0	0	0	0	0
4	7	2,022	wet	30.6	0.63	228.4	21.4	62	neutral	0	0	16	1	medium	5_14	female	malaria	0	moderate	1	0	1	1	1	0	0	0
5	3	2,021	wet	29.8	1.02	329.9	-2.5	59	neutral	1	0	6	1	high	50_plus	female	malaria	0	moderate	0	0	1	1	0	0	0	0
6	5	2,022	wet	27.1	0.08	276.5	-13.2	67	neutral	0	0	42	1	high	under5	female	malaria	0	mild	0	0	0	1	0	0	0	0
7	8	2,018	wet	24.9	0.29	298.2	21	88	la_nina	0	0	20	0	low	5_14	female	malaria	0	mild	0	0	0	1	0	1	0	0
8	4	2,022	wet	33	0.87	358.4	-12.5	50	el_nino	0	0	113	0	high	5_14	female	malaria	1	mild	0	0	1	1	0	0	0	0
9	9	2,023	wet	28.3	0.94	387.5	5.5	60	la_nina	1	0	96	1	high	5_14	male	diarrhoeal_disease	1	mild	0	0	0	0	1	1	0	0
10	12	2,021	dry	27	0.97	180.7	2.3	42	el_nino	0	0	48	0	medium	5_14	female	cholera	0	mild	0	0	0	0	1	1	0	0
11	8	2,023	wet	28.2	0.45	249.9	6.1	67	el_nino	0	0	52	0	high	15_49	female	diarrhoeal_disease	1	moderate	1	0	0	0	0	1	0	0
12	8	2,023	wet	34.1	0.54	191.1	-2.4	43	la_nina	0	0	113	1	high	15_49	male	malaria	1	mild	0	0	1	1	0	1	0	0
13	3	2,019	wet	28	0.67	290.9	-13.9	70	neutral	1	0	60	0	high	50_plus	male	malaria	1	mild	0	0	1	1	1	0	1	0
14	10	2,018	wet	29.8	-0.07	353.5	11.4	51	neutral	0	0	51	0	low	15_49	female	diarrhoeal_disease	0	mild	0	0	0	0	1	1	0	0
15	6	2,020	wet	24.8	1.23	240.4	-1.5	52	el_nino	0	0	89	0	medium	5_14	female	diarrhoeal_disease	0	moderate	0	0	0	1	0	1	0	0
16	11	2,019	dry	30.3	1.18	217.9	18.3	16	el_nino	0	0	14	0	medium	15_49	female	schistosomiasis	1	mild	0	0	0	0	1	0	0	0
17	6	2,021	wet	28.8	1.01	415.5	-51.6	57	el_nino	1	0	94	0	medium	15_49	female	malaria	0	moderate	1	0	1	1	1	0	0	0
18	5	2,022	wet	26.7	0.39	288.2	21.1	65	neutral	0	0	75	1	high	15_49	male	malaria	0	moderate	1	0	0	1	0	0	0	0
19	11	2,023	dry	23.9	-0.36	58.9	-11.8	40	el_nino	0	0	54	1	high	under5	male	malaria	0	mild	0	0	0	1	1	1	0	0
20	10	2,020	wet	22.8	0.46	337.4	-7.1	67	neutral	0	0	47	1	high	15_49	female	diarrhoeal_disease	0	mild	0	0	0	1	1	0	0	0
21	8	2,018	wet	26	1.04	258.2	-45.8	76	el_nino	1	0	17	0	high	15_49	female	malaria	0	mild	0	0	0	0	0	1	0	0
22	6	2,021	wet	33.8	0.83	284.4	26.9	54	neutral	0	0	59	0	low	15_49	male	malaria	1	mild	0	0	0	0	0	0	0	0
23	5	2,023	wet	31.3	0.23	270.6	64.9	67	neutral	0	0	67	0	medium	15_49	male	diarrhoeal_disease	0	mild	0	0	0	1	0	1	0	0
24	5	2,019	wet	30.9	-0.12	239.8	-49.8	47	la_nina	0	0	76	0	low	5_14	female	schistosomiasis	0	moderate	0	0	0	0	1	0	1	0
25	4	2,021	wet	30.3	0.46	350.8	40.7	53	neutral	0	0	44	0	medium	under5	female	malaria	0	moderate	1	0	0	1	1	1	0	0
26	7	2,021	wet	26.6	-0.04	195.6	6.2	62	neutral	0	0	45	1	high	15_49	male	diarrhoeal_disease	0	mild	0	0	0	0	0	0	0	0
27	10	2,020	wet	26.4	0.76	300.1	1.3	55	la_nina	1	0	45	0	medium	5_14	female	malaria	1	moderate	0	0	0	0	0	0	0	0
28	12	2,023	dry	24.7	1.16	233.8	-29.4	24	neutral	0	0	98	0	low	15_49	female	malaria	1	mild	0	0	1	0	0	0	0	0
29	10	2,019	wet	27	0.48	318.7	19.1	70	la_nina	1	0	18	0	high	15_49	female	diarrhoeal_disease	0	mild	0	0	0	0	0	1	0	0
30	8	2,018	wet	27.3	0.59	392.6	-26	36	la_nina	0	0	37	0	medium	5_14	female	malaria	1	mild	0	0	0	0	1	0	1	0
31	7	2,023	wet	25.4	-0.07	284.8	-9.8	87	el_nino	1	0	16	1	low	15_49	female	malaria	1	severe	0	0	1	0	1	1	0	0
32	7	2,022	wet	30.6	0.94	205.8	-32.9	75	el_nino	0	0	121	0	high	15_49	male	malaria	0	moderate	0	0	0	1	0	1	0	0
33	12	2,021	dry	26.8	1.28	0	-20.7	25	el_nino	0	1	22	0	medium	under5	male	malaria	1	moderate	1	0	1	0	1	1	0	0
34	1	2,021	dry	24.4	0.02	115.7	-8.8	22	el_nino	0	0	83	1	high	15_49	male	malaria	0	mild	0	0	0	1	1	1	0	0
35	1	2,019	dry	29.4	-0.01	0	25	49	neutral	0	0	52	0	medium	under5	female	diarrhoeal_disease	0	mild	0	0	0	0	0	0	0	0
36	10	2,020	wet	30.2	1.02	382.5	15.9	73	el_nino	1	0	0	1	medium	15_49	female	diarrhoeal_disease	1	moderate	1	0	0	1	0	1	0	0
37	12	2,018	dry	27.2	1.7	134.5	0.3	37	neutral	0	0	43	0	low	under5	female	malaria	0	severe	1	0	1	0	1	0	0	0
38	6	2,022	wet	31.8	1.35	258.1	-31.1	61	neutral	1	0	32	0	medium	under5	female	dengue	0	mild	0	0	0	1	1	0	0	0
39	4	2,021	wet	23.9	0.81	272.8	-2.9	79	neutral	0	0	96	1	high	50_plus	female	malaria	1	moderate	1	0	1	0	0	0	0	0
40	8	2,022	wet	26.8	1.28	237.4	-12.2	75	la_nina	0	0	58	1	medium	15_49	male	schistosomiasis	1	mild	0	0	0	0	1	1	0	0
41	7	2,021	wet	28.5	0.94	179.3	18.1	65	la_nina	0	0	0	1	medium	15_49	female	diarrhoeal_disease	0	severe	0	0	0	1	1	0	1	0
42	10	2,022	wet	27.2	0.6	273.5	-7.8	77	neutral	0	0	64	0	medium	15_49	female	malaria	0	mild	0	0	1	1	0	1	0	0
43	6	2,020	wet	31.1	1.38	399.3	13.2	81	el_nino	1	0	23	0	high	15_49	female	dengue	1	mild	0	0	0	1	0	0	0	0
44	3	2,022	wet	25.7	0.76	354.6	21.6	67	la_nina	0	0	86	0	medium	5_14	female	diarrhoeal_disease	0	mild	0	0	0	0	1	1	0	0
45	10	2,022	wet	28.6	1	415.5	3.3	52	neutral	1	0	9	1	medium	15_49	male	malaria	0	mild	0	0	1	0	0	0	0	0
46	6	2,023	wet	24.8	0.91	336.2	9.4	72	la_nina	0	0	40	0	medium	50_plus	male	diarrhoeal_disease	1	moderate	0	0	0	0	0	0	0	0
47	6	2,021	wet	28.6	0.97	330.3	-9.4	74	el_nino	1	0	93	0	high	5_14	female	malaria	1	mild	0	0	0	0	1	0	0	0
48	12	2,022	dry	27.5	0.8	148.1	29.7	37	neutral	0	0	64	0	high	15_49	male	schistosomiasis	0	mild	0	0	0	0	0	1	0	0
49	11	2,023	dry	25	1.02	0	-5.1	36	la_nina	0	0	94	0	low	5_14	female	malaria	1	moderate	0	0	1	1	0	0	1	0
50	9	2,019	wet	28	0	187.8	5.7	80	neutral	0	0	32	0	low	under5	female	malaria	0	severe	0	0	0	1	0	0	0	0
51	8	2,022	wet	27	1.41	412.1	12.4	76	la_nina	0	0	68	1	high	5_14	female	diarrhoeal_disease	0	mild	0	0	0	0	0	0	0	0
52	3	2,023	wet	31.4	0.62	301.7	-24.7	70	el_nino	0	0	42	1	high	15_49	male	malaria	0	moderate	1	0	1	0	0	1	0	0
53	7	2,023	wet	32.9	-0.36	89.9	-13.6	65	el_nino	0	0	64	1	medium	15_49	male	schistosomiasis	1	moderate	1	0	0	0	0	0	0	0
54	4	2,022	wet	28	0.05	181.3	-21.5	94	neutral	0	0	37	1	high	15_49	male	malaria	0	moderate	0	0	1	1	0	1	0	0
55	8	2,018	wet	28.6	0.11	241.5	9.1	72	neutral	0	0	95	0	high	under5	male	dengue	0	mild	0	0	0	1	1	1	0	0
56	3	2,019	wet	27	0.46	205.9	-22	92	el_nino	0	0	76	1	high	50_plus	male	malaria	1	moderate	1	0	0	0	0	0	1	0
57	6	2,020	wet	27.2	1.08	321	-13.7	51	la_nina	0	0	0	0	medium	15_49	male	malaria	1	mild	0	0	0	0	0	0	0	0
58	11	2,023	dry	27	0.02	141.1	37.9	25	neutral	0	0	28	1	medium	5_14	male	malaria	0	moderate	1	0	0	0	1	0	0	0
59	8	2,022	wet	32.4	1.52	236.3	9.3	57	el_nino	0	0	32	0	medium	under5	female	diarrhoeal_disease	1	mild	0	0	0	0	1	0	0	0
60	7	2,020	wet	27.5	0.6	78.4	6.4	75	neutral	0	0	30	1	high	50_plus	male	malaria	0	moderate	1	0	0	0	1	0	1	0
61	10	2,023	wet	29.6	1.08	435.3	-5.1	69	la_nina	0	0	27	0	medium	15_49	male	malaria	1	mild	0	0	1	1	1	0	0	0
62	10	2,023	wet	29.8	1.13	514	-5.3	55	la_nina	1	0	34	0	high	under5	female	malaria	0	mild	0	0	0	1	1	0	0	0
63	7	2,023	wet	27.6	0.67	236	22.9	44	la_nina	0	0	38	1	high	under5	female	diarrhoeal_disease	0	severe	0	0	0	1	0	0	0	0
64	11	2,023	dry	30.5	-0.63	25.4	-4.1	13	la_nina	0	0	87	1	high	15_49	female	dengue	0	mild	0	0	0	1	0	1	0	0
65	12	2,023	dry	30.4	0.64	35.6	-12.3	28	neutral	0	0	57	0	medium	5_14	female	dengue	0	severe	0	0	0	1	0	0	0	0
66	7	2,019	wet	32.1	0.89	188	20.6	76	neutral	0	0	72	0	low	under5	female	dengue	1	mild	0	0	0	0	0	0	1	0
67	8	2,020	wet	28.2	0.95	479.3	37.9	61	la_nina	1	0	65	0	high	15_49	female	diarrhoeal_disease	0	mild	0	0	0	0	0	0	0	0
68	10	2,021	wet	25.4	0.12	141.6	-44.2	66	el_nino	0	0	49	0	high	5_14	male	schistosomiasis	1	mild	0	0	0	0	0	0	0	0
69	3	2,021	wet	25.2	0.96	286.1	-15.4	64	neutral	0	0	39	0	high	15_49	female	malaria	0	mild	0	0	1	0	1	1	0	0
70	5	2,020	wet	28.8	1.07	350.6	-49.9	44	neutral	0	0	89	0	high	50_plus	female	diarrhoeal_disease	0	moderate	0	0	0	1	0	0	0	0
71	7	2,022	wet	25.7	1.8	183.7	18.7	44	neutral	0	0	95	0	medium	15_49	male	malaria	0	mild	0	0	1	1	1	1	0	0
72	6	2,023	wet	27	0.44	339.4	20.2	63	neutral	1	0	25	0	low	5_14	female	malaria	1	mild	0	0	0	0	1	0	0	0
73	1	2,022	dry	21.9	0.97	161.1	-3.8	41	el_nino	0	0	46	0	medium	5_14	male	malaria	1	severe	0	0	0	0	0	0	0	0
74	9	2,023	wet	31.4	1.43	142	-7.8	81	el_nino	0	0	55	0	high	5_14	male	malaria	1	severe	0	0	0	0	1	1	0	0
75	10	2,020	wet	29.1	1.37	59	39.8	69	neutral	0	0	13	0	high	under5	female	schistosomiasis	0	mild	0	0	0	0	0	0	0	0
76	4	2,019	wet	24.9	0.83	259.3	29.2	62	neutral	0	0	88	0	medium	15_49	male	diarrhoeal_disease	0	moderate	0	0	0	0	0	0	0	0
77	2	2,021	dry	27.4	0.94	0	-2.2	15	neutral	0	0	43	1	high	15_49	female	malaria	1	moderate	1	0	1	1	0	1	0	0
78	12	2,018	dry	23.5	0.11	6.1	-31.2	39	neutral	0	1	0	1	high	15_49	female	diarrhoeal_disease	1	mild	0	0	0	0	1	0	0	0
79	5	2,022	wet	28.3	0.29	281.4	-14.6	58	el_nino	1	0	81	1	medium	50_plus	male	malaria	0	moderate	0	0	0	0	0	0	0	0
80	5	2,021	wet	28.1	0.39	280	-9.7	82	neutral	1	0	70	0	low	15_49	male	malaria	0	severe	1	0	1	0	0	0	0	0
81	3	2,022	wet	26.7	0.48	174.9	-26.3	63	el_nino	0	0	75	0	high	5_14	female	malaria	0	mild	0	0	0	0	0	0	0	0
82	9	2,018	wet	28.8	-0.42	181.5	17.2	79	neutral	0	0	2	1	medium	under5	male	malaria	0	moderate	0	0	0	1	0	1	0	0
83	5	2,022	wet	27.6	0.85	97.1	11.9	64	neutral	0	0	78	0	low	5_14	male	malaria	1	severe	0	0	0	0	0	1	0	0
84	10	2,020	wet	30.8	1.58	191.2	-5.7	73	el_nino	0	0	16	0	medium	15_49	female	malaria	0	mild	0	0	1	1	0	0	0	0
85	9	2,022	wet	27.6	1.13	212.3	5.8	61	el_nino	0	0	64	0	low	15_49	male	malaria	1	mild	0	0	0	0	1	1	0	0
86	7	2,019	wet	30.8	0.52	214.5	-29.9	52	la_nina	0	0	71	0	medium	5_14	male	malaria	0	mild	0	0	0	0	0	0	0	0
87	2	2,021	dry	24.6	-0.28	30	-14.5	16	la_nina	0	0	36	0	high	5_14	female	malaria	0	severe	0	1	0	0	0	0	0	0
88	2	2,023	dry	30.4	-0.72	0	25.6	31	neutral	0	1	80	0	medium	15_49	male	schistosomiasis	0	mild	0	0	0	1	0	0	0	0
89	4	2,023	wet	28.5	0.22	209	-20	49	el_nino	0	0	13	0	low	under5	female	malaria	1	moderate	0	0	1	0	0	0	0	0
90	12	2,022	dry	25.9	0.88	0	-6.8	32	neutral	0	1	57	1	high	15_49	female	malaria	1	moderate	1	0	1	1	1	0	0	0
91	10	2,021	wet	28	0.41	243.9	5	55	la_nina	0	0	9	0	high	under5	female	diarrhoeal_disease	0	mild	0	0	0	0	0	0	1	0
92	6	2,022	wet	29.2	0.1	371	-2.4	51	neutral	1	0	12	0	medium	under5	male	schistosomiasis	1	mild	0	0	0	0	0	0	0	0
93	4	2,021	wet	25.3	0.06	320.9	-65	67	neutral	0	0	35	1	low	5_14	male	dengue	0	mild	0	0	0	1	1	1	0	0
94	6	2,021	wet	26.1	1.13	314.8	2.6	51	el_nino	0	0	84	1	high	under5	female	diarrhoeal_disease	0	moderate	0	0	0	1	1	0	0	0
95	3	2,022	wet	28.5	1.07	252.4	-8.6	48	el_nino	1	0	51	1	high	5_14	male	malaria	1	mild	0	0	0	0	0	0	0	0
96	5	2,023	wet	29.1	1.11	281.9	20.5	66	neutral	1	0	25	1	high	15_49	male	cholera	1	severe	0	0	0	0	1	1	0	1
97	6	2,020	wet	29.8	0.67	275.6	2.7	67	la_nina	1	0	51	0	low	15_49	male	malaria	1	mild	0	0	1	0	0	0	0	0
98	3	2,021	wet	30.7	0.96	383.3	-2.4	61	el_nino	0	0	28	1	high	15_49	male	malaria	1	moderate	1	0	0	1	1	0	0	0
99	6	2,019	wet	26.2	1.45	327	15.1	79	el_nino	0	0	27	1	high	15_49	male	malaria	0	mild	0	0	1	1	0	1	0	0
100	2	2,023	dry	23	0.81	43	-11.7	59	neutral	0	0	71	1	high	5_14	male	malaria	1	moderate	1	0	0	0	1	0	0	0

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⚠️ Synthetic dataset — Parameterized from published SSA literature, not real observations. Not suitable for empirical analysis or policy inference.

Climate-Sensitive Infectious Diseases in Sub-Saharan Africa

Abstract

A synthetic, literature-grounded dataset modelling the relationship between climatic variables and infectious disease burden across three distinct epidemiological and climatic zones in sub-Saharan Africa (SSA). Each record represents a disease notification event linked to monthly temperature, rainfall, ENSO phase, flooding status, and temperature anomalies, enabling analysis of how climate variability drives shifts in malaria, dengue, cholera, diarrhoeal disease, schistosomiasis, and meningitis.

Climate change is reshaping the infectious disease landscape of Africa. A 2022 scoping review of 32 systematic reviews found strong evidence that climate variability is altering malaria transmission in African highlands and dengue emergence in tropical zones, with temperature and rainfall as the dominant drivers (Caminade et al., 2022). Meanwhile, cholera outbreaks in Africa are increasingly linked to El Niño events, heavy rainfall, and flooding — with the El Niño Southern Oscillation shifting the geography of cholera across the continent (Moore et al., 2017). A 2024 BMJ review confirmed that vector-borne, waterborne, and foodborne diseases are all temperature-sensitive, with LMICs bearing a disproportionate burden (Lv et al., 2024).

Three scenarios capture the SSA climate-disease gradient:

Highland Warming (Kenya highlands, Ethiopian highlands, Rwanda): Historically low malaria transmission now increasing as warming pushes the Plasmodium falciparum transmission zone to higher altitudes (mean altitude ~1,800 m, temperature anomaly +1.2 °C above baseline). Dengue is emerging. Moderate ITN coverage (55%) and surveillance capacity (40%).
Coastal Flood-Prone (Coastal Kenya, Mozambique, Lagos, Accra): Holoendemic malaria (200/1,000), high dengue risk in urban areas (25/1,000), and frequent cholera outbreaks driven by flooding and El Niño (30% ENSO probability). Poor WASH access (40%) amplifies waterborne disease risk.
Sahel Seasonal (Niger, Burkina Faso, Northern Nigeria, Chad): Intense seasonal malaria following the rains (150/1,000), high meningitis burden during dry dusty season (20/1,000 — meningitis belt), largest temperature anomaly (+1.5 °C), and lowest surveillance and early-warning capacity (20% and 10% respectively).

Dataset Structure

Each scenario contains 10,000 records (30,000 total). Key columns include:

Time & Seasonality

month, year, season (wet/dry)

Climate Covariates

monthly_mean_temp_c — Monthly mean temperature (°C)
temp_anomaly_c — Temperature departure from 1990-2020 baseline (°C)
monthly_rainfall_mm — Monthly precipitation (mm)
rainfall_anomaly_pct — Rainfall departure from normal (%)
relative_humidity_pct — Relative humidity (%)
enso_phase — El Niño / La Niña / Neutral
flooding_event — Binary: major flooding in that month
drought_month — Binary: severe rainfall deficit

Location

altitude_m — Elevation (metres above sea level)
urban — Binary: urban vs. rural setting
population_density — Low / medium / high

Demographics

age_group — under5 / 5-14 / 15-49 / 50+
sex

Disease & Outcomes

disease — One of: malaria, dengue, cholera, diarrhoeal_disease, schistosomiasis, meningitis
climate_attributable — Binary: case plausibly attributable to climate anomaly
severity — mild / moderate / severe
hospitalised, died
outbreak_declared — Binary: part of a declared outbreak

Interventions & Surveillance

itn_use — Insecticide-treated net use (malaria cases)
vector_control_active — Active vector control in area
wash_improved — Access to improved WASH
surveillance_detected — Case detected by surveillance system
early_warning_issued — Climate-health early warning was active

Parameterization Evidence

Parameter	Value Used	Source	Year
Strong evidence for climate impacts on malaria in African highlands	Highland scenario framing	Caminade et al. Malar J 21:6	2022
Temperature and rainfall are dominant climatic drivers	Climate covariates	Caminade et al.	2022
Vector-borne, waterborne, foodborne diseases all temperature-sensitive	Disease selection and risk models	Lv et al. BMJ 387:e079421	2024
LMICs bear disproportionate burden	Scenario design	Lv et al.	2024
El Niño → increased rainfall in East Africa → cholera outbreaks	ENSO-cholera linkage	Moore et al. PNAS 114(17):4436-4441	2017
1.4-4.0M cholera cases/yr; 21,000-143,000 deaths; endemic in 47 countries	Cholera incidence & CFR	J Epidemiol Glob Health (2025)	2025
Heavy rainfall, rising SST, El Niño are key cholera drivers in Africa	Flood-cholera risk model	J Epidemiol Glob Health	2025
Optimal P. falciparum transmission at 25-33 °C	Malaria temperature window	Multiple PMC malaria-climate studies	Various
Rainfall creates Anopheles breeding sites with 1-2 month lag	Rainfall-malaria lag	Multiple PMC studies	Various
Highland epidemics when warming exceeds historical norms (>1,500 m)	Highland altitude threshold	Caminade et al.; PMC studies	Various
Dengue/chikungunya emerging in SSA as Aedes range expands	Dengue scenario framing	Lancet Planet Health 4:e416-e423	2020
Meningitis belt: dry season, low humidity, dust drive epidemics	Meningitis-climate model	Azongo et al. Environ Int 91:133-149	2016
ITN coverage ~40-60% across SSA countries	ITN parameter	WHO World Malaria Report	2023

Validation Summary

The 8-panel validation report (validation_report.png) confirms:

Disease distribution: Malaria dominates all scenarios (47-56%); meningitis highest in Sahel (8.0%); dengue highest in coastal/urban settings (10.1%); cholera highest in coastal flood-prone areas (3.5%).
Climate space: Clear separation — highlands cooler and wetter; Sahel hot and dry; coastal warm and very wet.
Seasonality: Cases peak during wet season months (March-October) across all scenarios, consistent with vector breeding and waterborne transmission.
Climate drivers: Climate-attributable fraction ranges from 50% (coastal) to 59% (Sahel). El Niño and flooding events appropriately distributed.
Severity and mortality: Sahel has highest CFR (3.5%) reflecting lower healthcare access; meningitis has highest disease-specific CFR consistent with literature.
Disease-specific CFR: Meningitis CFR highest across all scenarios; cholera CFR elevated in flood-prone settings.
Intervention coverage: ITN use, vector control, and WASH access show expected gradient — lowest in Sahel, highest in highlands.
Correlations: Temperature and rainfall positively correlated with flooding; flooding correlated with hospitalisation and mortality.

Usage

from datasets import load_dataset

# Load the coastal flood-prone scenario (default)
ds = load_dataset("electricsheepafrica/climate-sensitive-infections", name="coastal_flood_prone")
df = ds["train"].to_pandas()

# Analyse disease burden by ENSO phase
print(df.groupby("enso_phase")["disease"].value_counts(normalize=True).unstack().round(3))

# Filter climate-attributable cases
climate_cases = df[df["climate_attributable"] == 1]
print(f"Climate-attributable: {len(climate_cases)} ({len(climate_cases)/len(df)*100:.1f}%)")

# Cholera during flooding events
flood_cholera = df[(df["flooding_event"] == 1) & (df["disease"] == "cholera")]
print(f"Cholera cases during floods: {len(flood_cholera)}")

Intended Uses

Training climate-disease early warning models for SSA
Analysing ENSO-driven cholera outbreak risk patterns
Modelling the impact of highland warming on malaria transmission zones
Evaluating the effectiveness of ITNs, vector control, and WASH interventions under climate change
Teaching climate-health epidemiology and planetary health concepts
Benchmarking climate-attribution methods for infectious disease

Limitations

Synthetic data: Generated from literature-derived parameter distributions, not real surveillance records. Disease co-occurrence and spatial autocorrelation are simplified.
Climate simplification: Monthly aggregates miss daily extremes and lagged effects. Real ENSO-disease relationships involve complex teleconnections not fully modelled.
Single-disease records: Each record represents one disease, whereas co-infections (e.g., malaria + diarrhoea) are common in SSA but not captured.
No spatial resolution: Records are not geolocated; within-scenario variation is modelled probabilistically.
Intervention effectiveness: ITN and WASH impact are modelled as simple risk reductions, not as dose-response relationships with coverage thresholds.
Emerging pathogens: The dataset focuses on established climate-sensitive diseases; novel pathogens and spillover events are not modelled.

References

Caminade C, et al. (2022). Charting the evidence for climate change impacts on the global spread of malaria and dengue and adaptive responses: a scoping review of reviews. Malar J 21:6. DOI: 10.1186/s12936-021-04050-w
Lv H, et al. (2024). The impact of increasing temperatures due to climate change on infectious diseases. BMJ 387:e079421. DOI: 10.1136/bmj-2024-079421
Moore SM, et al. (2017). El Niño and the shifting geography of cholera in Africa. PNAS 114(17):4436-4441. DOI: 10.1073/pnas.1617218114
Cholera in Africa: A Climate Change Crisis (2025). J Epidemiol Glob Health. DOI: 10.1007/s44197-025-00386-x
Lancet Planetary Health (2020). Climate change could shift disease burden from malaria to arboviruses in Africa. Lancet Planet Health 4:e416-e423. DOI: 10.1016/S2542-5196(20)30178-9
Azongo DK, et al. (2016). Temperature-related morbidity and mortality in Sub-Saharan Africa: A systematic review. Environ Int 91:133-149. DOI: 10.1016/j.envint.2016.02.027

Citation

If you use this dataset, please cite:

@dataset{electricsheepafrica_climate_sensitive_infections_2025,
  title={Climate-Sensitive Infectious Diseases in Sub-Saharan Africa},
  author={Electric Sheep Africa},
  year={2025},
  publisher={HuggingFace},
  url={https://huggingface.co/datasets/electricsheepafrica/climate-sensitive-infections}
}

License

CC-BY-4.0

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