Modelling spatial processes of infectious diseases

Human movement plays a key role in the spread of infectious diseases, leading to spatial heterogeneities in disease transmission. An understanding of the causes of these heterogeneities is important in the design, application, and evaluation of public health interventions. In this thesis, we developed a range of statistical models to elucidate spatial dependencies of infection patterns in different populations, and embed existing mobility models within a principled statistical framework. We applied a spatio-temporal generalized linear mixed model to include both climate and non-climate effects on malaria incidence in Malawi while implicitly accounting for spatial dependency and the role of human movement. We further developed methods for real-time assessment of an epidemic by adding spatial information in the calculation of reproductive numbers to account for spatial heterogeneities. A detailed review of mobility models and their use in infectious disease modelling was performed to identify current gaps and opportunities in the field. Finally, a model describing the rate at which human social contact is made in different locations was developed to identify individual-level differences in mobility. The implications for understanding epidemic process and informing control are discussed. With increasing availability of fine-scale mobility data, studying and understanding mobility patterns and their relationship with infectious disease spread will play a key role in developing efficient surveillance and control of emerging and re-emerging diseases.