Spatial distribution of malaria and its associated factors across the districts of central Ethiopia region, 2025: a Bayesian conditional autoregressive model
- 1Central Ethiopia Public Health Institute, Worabe, Ethiopia
- 2School of Public Health, College of Medicine and Health Sciences, Wachemo University, Hossana, Ethiopia
- 3Central Ethiopia Regional Health Bureau, Worabe, Ethiopia
Abstract
Introduction: To achieve national strategic objectives it is essential to comprehend the distribution of malaria, map high-risk locations, and determine the coverage of vector control strategies. Despite this, there hasn't been any study done in this area. Therefore, this study was aimed to assess spatial distribution and associated factors of malaria across the districts of central Ethiopia region.
Methods: A retrospective ecological study was conducted in central Ethiopia region based on the number of malaria cases from July 8, 2022 to July 7, 2025. Data sources such as weekly malaria surveillance data, NASA Langley Research Center POWER Project website, and Google earth pro website were considered. Spatial autocorrelation was measured using Global Moran’s I. Negative binomial regression model was fitted.
Results: A total of 842968 confirmed cases were reported from July 8, 2022-July 7, 2025. The trend of regional API was increasing with mean regional API of 43.07 cases/1000 population at risk. Districts with high incidence were clustered in North West, South west, South East and Eastern parts of the region. Hotspot districts were identified at Eastern, northern and southern parts of the region. There was a spatial autocorrelation among the districts of the region (Global Moran’s I=0.21). Altitude was significantly associated with the incidence of malaria (IRR=0.99).
Conclusion: More than half of the districts had high malaria risk. There was spatial variation in malaria incidence across the districts. Spatial clustering was observed across the districts of the region. Increasing the coverage of integrated malaria prevention and control strategies is warranted. Further studies are warranted to identify the factors of malaria at high risk areas considering additional environmental factors.
Supplementary Information: The online version contains supplementary material available at https://doi.org/10.1186/s12936-026-06144-z.
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BibTeX
@article{tirore2026spatial,
title = {{Spatial distribution of malaria and its associated factors across the districts of central Ethiopia region, 2025: a Bayesian conditional autoregressive model}},
author = {Lire Lemma Tirore and Samuel Darge and Mamush Hussen Shikur and Legese Petros and Ashenafi Petros and Temesgen Kelaye and Samuel Hailu Weldemariam and Terefe Markos and Fiseha Lemango Gurmamo and Woldesenbet Shewalem and Zeleke Dutamo Agde},
journal = {Malaria Journal},
year = {2026},
month = oct,
doi = {10.1186/s12936-026-06144-z},
url = {https://doi.org/10.1186/s12936-026-06144-z},
note = {CC BY-NC-ND 4.0},
}
RIS
TY - JOUR
TI - Spatial distribution of malaria and its associated factors across the districts of central Ethiopia region, 2025: a Bayesian conditional autoregressive model
AU - Lire Lemma Tirore
AU - Samuel Darge
AU - Mamush Hussen Shikur
AU - Legese Petros
AU - Ashenafi Petros
AU - Temesgen Kelaye
AU - Samuel Hailu Weldemariam
AU - Terefe Markos
AU - Fiseha Lemango Gurmamo
AU - Woldesenbet Shewalem
AU - Zeleke Dutamo Agde
PY - 2026
DA - 2026/10/22
JO - Malaria Journal
DO - 10.1186/s12936-026-06144-z
UR - https://doi.org/10.1186/s12936-026-06144-z
AB - Introduction: To achieve national strategic objectives it is essential to comprehend the distribution of malaria, map high-risk locations, and determine the coverage of vector control strategies. Despite this, there hasn't been any study done in this area. Therefore, this study was aimed to assess spatial distribution and associated factors of malaria across the districts of central Ethiopia region. Methods: A retrospective ecological study was conducted in central Ethiopia region based on the number of malaria cases from July 8, 2022 to July 7, 2025. Data sources such as weekly malaria surveillance data, NASA Langley Research Center POWER Project website, and Google earth pro website were considered. Spatial autocorrelation was measured using Global Moran’s I. Negative binomial regression model was fitted. Results: A total of 842968 confirmed cases were reported from July 8, 2022-July 7, 2025. The trend of regional API was increasing with mean regional API of 43.07 cases/1000 population at risk. Districts with high incidence were clustered in North West, South west, South East and Eastern parts of the region. Hotspot districts were identified at Eastern, northern and southern parts of the region. There was a spatial autocorrelation among the districts of the region (Global Moran’s I=0.21). Altitude was significantly associated with the incidence of malaria (IRR=0.99). Conclusion: More than half of the districts had high malaria risk. There was spatial variation in malaria incidence across the districts. Spatial clustering was observed across the districts of the region. Increasing the coverage of integrated malaria prevention and control strategies is warranted. Further studies are warranted to identify the factors of malaria at high risk areas considering additional environmental factors. Supplementary Information: The online version contains supplementary material available at https://doi.org/10.1186/s12936-026-06144-z.
SN - 1475-2875
ER -