Using Maximum Entropy Modeling to Identify Malaria Vector Habitat in Panama

Abstract

Despite attempts to eradicate local malaria transmission in Panama by 2020, malaria remains endemic in some regions in Panama with cases surging since 2019. While approximately only 730 cases were reported in 2018, over 14,000 were identified in 2024 alone. While this augmented case incidence temporally aligns with increased international migration through the Darien Gap, the emergence of the highly efficient South American malaria vector, An. darlingi, may also contribute to recent case resurgence. Historically published malaria vector observations are concentrated in southern regions of Panama. Given the costly nature of entomologic field collection, identifying prospective malaria vector habitats can help inform collection efforts in under-sampled regions. Moreover, if field validated, identifying these habitats for An. darlingi and other common malaria vectors (An. albimanus, An. pseudopunctipennis, An. punctimacula) in Panama could help inform future vector control and malaria elimination efforts. This exploratory analysis assesses the feasibility of using historical malaria vector observations and 5-kilometer (km) resolution Land Data Assimilation System (LDAS) climate data to perform Maximum Entropy (MaxEnt) modeling and provides initial MaxEnt habitat suitability predictions. While the discussed model requires further iteration to address possible model overfitting and explore the impact of spatial autocorrelation among presence observations, the exploratory results indicate nation-wide habitat suitability with species-specific distributions for An. albimanus, An. punctimacula, and An. darlingi in both dry and wet seasons. An. pseudopunctipennis’ species distribution appears inconclusive.