Public health action · 2026
Mapping malaria hotspots through spatial and spatio-temporal analysis in Sierra Leone, 2021-2024.
СтатьяНазвание и аннотация: Перевод в очереди
Обработка статьи · этапы, ошибки и управление
Загрузка состояния…
Ошибки и ограничения
История · было → стало
Состояние обновляется каждые 5 секунд, пока блок открыт. Процент показывается только там, где известен объём работы. Пауза останавливает продолжение этапа; текущий запрос к источнику может завершиться.
Falama A M, Omoniwa O, Kanu M S, Lahai W K, Kamara A R Y, Ameh G, Kamara I F, Fofanah B D, Kenneh S, Kamara M N, Sesay M A, Sesay N, Kanu F, Alwani A A, Thekkur P, Zachariah R, Kanu M S, Lakoh S
Аннотация
SETTING: Sierra Leone, using data from the District Health Information System 2 (DHIS2) database.
OBJECTIVE: 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.
METHOD: 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.
RESULTS: 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.
CONCLUSION: Malaria transmission remains spatially and temporally heterogeneous, with persistent hotspots that require tailored subnational interventions to accelerate progress towards elimination.
OBJECTIVE: 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.
METHOD: 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.
RESULTS: 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.
CONCLUSION: Malaria transmission remains spatially and temporally heterogeneous, with persistent hotspots that require tailored subnational interventions to accelerate progress towards elimination.