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Background <p>Identifying the spatial heterogeneity in malaria transmission is crucial for designing geographically targeted control interventions, especially in high-burden communities where hotspot identification and delineation can facilitate the decision-making process toward resource allocation to specific areas where they are most needed. This study is the first attempt to identify malaria hotspots by jointly modelling vector abundance and human malaria incidence, alongside key ecological drivers, providing new insights into entomological and epidemiological synergies for public health management.</p> Methods <p>We applied a Bayesian Framework for Joint Gaussian Spatial Processes to log-transformed <i>Anopheles gambiae</i> s.l. and <i>Anopheles funestus</i> counts, and malaria incidence in eight communes of southwest Benin. Entomological data were obtained from mosquito surveillance activities and routine malaria incidence data from the District Health Information System 2. Malaria hotspots were delineated from a joint risk surface derived from interpolated predictive surfaces of malaria incidence and vectors abundance. Co-regionalization analysis explored local spatial correlations between malaria incidence and each mosquito vector suitability.</p> Results <p>Joint risk modelling identified contiguous malaria hotspots located mainly on the western shores of Lake Ahémé, and&#xa0;in Atchannou, Sè, Avloh and Grand‑Popo districts. Four ecological factors emerged as consistent and key drivers for all three processes: wind speed, mid-infrared reflectance, leaf area index and land surface temperature. Contrary to common assumptions, <i>An. funestus</i> showed stronger spatial correlation with malaria incidence across 119.95&#xa0;km<sup>2</sup> compared to 89.90 km<sup>2</sup>&#xa0;of&#xa0;<i>An. gambiae</i> s.l.; and with 67.29&#xa0;km<sup>2</sup> showing synergistic effects of both species.</p> Conclusion <p>This study reveals high heterogeneity in the spatial association between malaria and its primary vector species, with <i>An. funestus</i> playing a potential prominent role than previously recognized. Our framework offers a useful insight of the distinct ecological preferences of each malaria vector species, and highlights the need for species-agnostic, and spatially targeted interventions informed by entomological and epidemiological data until universal vaccines become widely available.</p>

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Identification of malaria hotspots in southwestern Benin through spatial joint modelling of malaria incidence and vector abundance

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年4月4日

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  • Gabriel Michel Monteiro,
  • Rock Yves Aïkpon,
  • Codjo Dandonougbo,
  • Luigi Sedda,
  • Luc Salako Djogbenou

摘要

Background

Identifying the spatial heterogeneity in malaria transmission is crucial for designing geographically targeted control interventions, especially in high-burden communities where hotspot identification and delineation can facilitate the decision-making process toward resource allocation to specific areas where they are most needed. This study is the first attempt to identify malaria hotspots by jointly modelling vector abundance and human malaria incidence, alongside key ecological drivers, providing new insights into entomological and epidemiological synergies for public health management.

Methods

We applied a Bayesian Framework for Joint Gaussian Spatial Processes to log-transformed Anopheles gambiae s.l. and Anopheles funestus counts, and malaria incidence in eight communes of southwest Benin. Entomological data were obtained from mosquito surveillance activities and routine malaria incidence data from the District Health Information System 2. Malaria hotspots were delineated from a joint risk surface derived from interpolated predictive surfaces of malaria incidence and vectors abundance. Co-regionalization analysis explored local spatial correlations between malaria incidence and each mosquito vector suitability.

Results

Joint risk modelling identified contiguous malaria hotspots located mainly on the western shores of Lake Ahémé, and in Atchannou, Sè, Avloh and Grand‑Popo districts. Four ecological factors emerged as consistent and key drivers for all three processes: wind speed, mid-infrared reflectance, leaf area index and land surface temperature. Contrary to common assumptions, An. funestus showed stronger spatial correlation with malaria incidence across 119.95 km2 compared to 89.90 km2 of An. gambiae s.l.; and with 67.29 km2 showing synergistic effects of both species.

Conclusion

This study reveals high heterogeneity in the spatial association between malaria and its primary vector species, with An. funestus playing a potential prominent role than previously recognized. Our framework offers a useful insight of the distinct ecological preferences of each malaria vector species, and highlights the need for species-agnostic, and spatially targeted interventions informed by entomological and epidemiological data until universal vaccines become widely available.