Modelling the Effects of Climate Change on Mosquito-Borne Diseases

Climate change is having a profound effect on biotic and abiotic drivers of disease spread. However, predicting how individual diseases respond to climate change is challenging to predict and requires new predictive frameworks. For mosquito-borne diseases, where mosquito life-stages occupy different niches, drivers may affect components of the pathosystem in complex and opposing ways. For example, changes in the patterns of seasonal rainfall and temperature, can affect variable life-history traits such as development, survival, fecundity and disease transmission. These traits may combine to give rise to sharp changes of species abundance and transmission dynamics, especially under climate change extremes, which impacts on our ability to predict mosquito population dynamics and the likelihood of disease outbreaks and subsequent impact of mitigation measures.
The aim of this project is to investigate the role of climate change in driving seasonal variability in mosquito populations and their diseases. The student will develop and analyse a system of state-dependent delayed differential equations in which environmental drivers affect life-history traits. The model will be analysed by extensive mathematical and numerical simulation techniques using a suitable programming language. The student will extend the mosquito model to include disease transmission to investigate how climate change will shape the likelihood of disease outbreaks in future climates. In addition, the student will validate the models against new and existing trait and spatial-temporal datasets provided by the VectorBite Research Coordination Network.