Predicting the Environmental Suitability for Anopheles Stephensi Under the Current Conditions in Ghana Publisher Pubmed



Ismail RBY^{1, 2} ; Bozorgomid F¹ ; Osei JHN² ; Pibansa S² ; Frempong KK² ; Ofei MK² ; Boakye HA² ; Ansahowusu J² ; Akorful SCA² ; Tawiahmensah CNL² ; Abudu M² ; Asafuadjaye A² ; Appawu MA² ; Boakye DA² Show All Authors Show Affiliations
Source: Scientific Reports Published:2024

Abstract

Vector-borne diseases emergence, particularly malaria, present a significant public health challenge worldwide. Anophelines are predominant malaria vectors, with varied distribution, and influenced by environment and climate. This study, in Ghana, modelled environmental suitability for Anopheles stephensi, a potential vector that may threaten advances in malaria and vector control. Understanding this vector’s distribution and dynamics ensures effective malaria and vector control programmes implementation. We explored the MaxEnt ecological modelling method to forecast An. stephensi’s potential hotspots and niches. We analysed environmental and climatic variables to predict spatial distribution and ecological niches of An. stephensi with a spatial resolution of approximately 5 km2. Analysing geospatial and species occurrence data, we identified optimal environmental conditions and important factors for its presence. The model’s most important variables guided hotspot prediction across several ecological zones aside from urban and peri-urban regions. Considering the vector’s complex bionomics, these areas provide varying and adaptable conditions for the vector to colonise and establish. This is shown by the AUC = 0.943 prediction accuracy of the model, which is considered excellent. Based on our predictions, this vector species would thrive in the Greater Accra, Ashanti Central, Upper East, Northern, and North East regions. Forecasting its environmental suitability by ecological niche modelling supports proactive surveillance and focused malaria management strategies. Public health officials can act to reduce the risk of malaria transmission by identifying areas where mosquitoes may breed, which will ultimately improve health outcomes and disease control. © 2024, The Author(s).