Yuval is utilising advances in mathematical models and computational modelling techniques to learn more about malaria. Malaria continues to be one of the leading causes of global childhood mortality, accounting for approximately half a million deaths a year in children under 5. Modelling supports improved understanding of malaria dynamics, impacts of intervention methods, and assessment of progress.

Two categories of models are individual-based models, which simulate the spread of disease by modelling individuals within a population separately under a set of biologically driven rules, and geostatistical models, which represent disease patterns on a larger scale geographically and through time based on empirical observations. Yuval’s PhD aims to create a meaningful integration between these two often disparate approaches, in order to elucidate patterns of the disease, assess intervention impacts, and predict prevalence. These new computational approaches for integrating the individual-based model and the geostatistical model will improve our understanding of malaria and create a framework applicable to other diseases.