Spatio-temporal dynamics of malaria vector niche overlaps in Africa

Malaria remains a significant public health challenge in sub-Saharan Africa, with transmission heightened by the dynamics of primary and secondary mosquitoes infected with Plasmodium parasites . Regions where both vector types co-exist face heightened likelihood of intensified malaria transmission. Hence, understanding vectors' ecological interactions, especially their niche overlaps in geographic or environmental space, is crucial for targeted malaria control and elimination strategies. We employed a dynamic cellular automata (CA) model to map niche overlaps among primary ( Anopheles gambiae complex, An. funestus group) and secondary ( An. pharoensis , An. coustani ) malaria vectors across African, using open-access environmental and vector occurrence datasets sourced from open-access geospatial portals, and spanning 1985 to 2021. Prior to modeling, we conducted exploratory data analysis (EDA) involving descriptive statistics, correlation and cluster analysis to glean insights into the relationships between the variables. Spearman correlation analysis revealed weak significant correlations (|r| < 0.3, p -value <0.001) between environmental variables and vectors occurrence, while environmental variables exhibited strong intercorrelations. Furthermore, An. gambiae complex prevailed at higher elevations with a minimum relative humidity of 22 %, while secondary vectors prevailed at lower elevations with humidity >38 % and temperatures above 20 °C. Our model, with accuracy exceeding 0.9 following validation, revealed expanding malaria vector niche overlaps across Africa, attributed to vectors expansion beyond their native regions. Such expanding vector niche overlaps predisposes numerous areas at risk of sustained and prolonged malaria transmission, underscoring the need for targeted malaria vector control interventions. Furthermore, dynamic modeling approaches, incorporating continuous data updates, captured ecological interactions accurately. • Understanding malaria vector overlaps is key to implementing integrated vector control measures. • Cellular automata approach relies on concept of neighborhood interactions to model the spread. • The modeling approach accurately captured the dynamic & complex nature of ecological phenomena. • Cellular automata approach leveraged on continuous data updates capturing ecological dynamics.