Computational Epidemiology of Infectious Diseases
Our group is interested in infectious diseases and their interactions with the environment. Specifically, we analyse the dynamics of mosquito-borne diseases with respect to climate, and patterns of antibiotic resistance and their relation to antibiotic usage. We develop and apply mathematical models as well as machine learning and advanced statistical techniques to electronic medical records and other disease-related data. For example, we have analysed the relationship between the incidence of dengue virus in Brazil and West Nile Virus in Israel to weather; or, we have predicted antibiotic resistance patterns of hospitalized patients' infections using machine learning. Our major aim is to understand and predict the dynamics of infectious diseases to successfully mitigate their future emergence and spread.