Stochastic And Climate-Driven Modeling Of Malaria Transmission In Rwanda: A Vector-Human Interaction Approach

Abstract

Malaria transmission depends heavily on climate conditions such as temperature and rainfall, which influence mosquito development and parasite growth. In this study, we build a stochastic model to better understand how these environmental factors affect the spread of malaria over time. The model includes temperature- and moisture-sensitive biological parameters and uses daily climate data from Rwanda. Using numerical simulations based on the Milstein scheme, we explore how mosquito population dynamics and malaria in- fections respond to seasonal changes. Results show that transmission slows down during dry periods, with lower survival and slower mosquito cycles. In some cases, transmission can fade out completely. Mathemati- cally, we verify that the model satisfies H¨ormander’s condition, supporting the existence of a smooth density and ensuring the system’s well-posedness. Spatial maps further highlight how local climate affects malaria risk across regions. This model offers a flexible framework for climate-informed malaria control planning, especially in the face of environmental variability and climate change.