Mapping malaria hotspots through spatial and spatio-temporal analysis in Sierra Leone, 2021-2024

Sierra Leone, using data from the District Health Information System 2 (DHIS2) database.

This study examined the spatial and spatio-temporal distribution of malaria incidence, and the relationship between malaria incidence and rainfall in surrounding areas in Sierra Leone from 2021 to 2024.

A cross-sectional geospatial study was conducted using malaria case data, mean rainfall data, population estimates, and chiefdom-level geographic coordinates. Spatial clustering was evaluated using Moran's I, significant district-level clusters were identified through space-time Poisson models (α = 0.05; 999 permutations), and the relationship between malaria incidence and rainfall in surrounding areas was assessed using bivariate Moran's I, implemented in Python.

Between 2021 and 2024, 7.4 million malaria cases were reported. Chiefdom-level incidence ranged from 21.2 to >750 per 1,000 population. Significant spatial clustering was observed (Moran's I > 0; P < 0.01). Persistent high-high clusters (P < 0.05) and low-low clusters (P < 0.05) were identified across the country. Space-time analysis identified both high-risk (relative risk [RR] = 1.2-1.9; P < 0.01) and low-risk districts (RR = 0.5-0.9; P < 0.01). There was no significant association between malaria incidence and rainfall in surrounding areas at the national level.

Malaria transmission remains spatially and temporally heterogeneous, with persistent hotspots that require tailored subnational interventions to accelerate progress towards elimination.