Mathematical Assessment of the Role of Temperature and Rainfall on Vector-Borne Diseases

Vector-borne diseases (VBDs), such as malaria, dengue and West Nile virus, cause significant public health burden in endemic areas. For instance, more than 55% (50%) of the worlds population live in areas at risk of dengue (malaria) with over 50 (300) million people infected and 20,000 (800,000) deaths annually. Owing to the significant burden inflicted by vector on human and animal health, VBDs have become the target of medical, veterinary and conservation research since the nineteenth century. Hence, understanding the population dynamics of VBDs, and the relationship between VBDs and the environment, is fundamental to the study of the epidemiology of VBDs. Vector abundance is a key determining factor that affects the persistence or resurgence of VBDs in populations (it also affects the risk index of VBDs in a given region). Hence, it is crucial to study the dynamics of VBDs, and devise effective and realistic methods for controlling VBDs in communities. Climate variables, such as temperature, humidity, rainfall and wind, significantly affect VBDs. Numerous mathematical models have been designed and used to assess the impact of climate change and seasonality on the transmission dynamics of VBDs, such as malaria, dengue, chikungunya and West Nile virus. However, most of the existing models of VBDs dynamics were built for a specific geographic context. The purpose of the current project is to qualitatively assess the impact of temperature and rainfall on the VBDs in a certain region. To achieve this objective, we will start with a new compartmental mathematical model, which incorporates variability in temperature and rainfall, will be designed and used to study the dynamics of the aquatic and adult stages of female mosquitoes in the given region (the resulting model, which takes the form of a non-autonomous deterministic system of nonlinear differential equations, can be applied to several mosquito species and different areas). Then we will extend this model for several diseases.