Simulation-based approaches to investigate transmission dynamics and inform public health interventions

ABOUT THE SPEAKER
Dr. Andrea Torneri

ABSTRACT
The spread of an infectious disease is a complex biological process influenced by the interactions that characterise the modes and routes of transmission, the strength of the host immune response and the in-host pathogen kinetic. Epidemiological models are useful tools developed to describe and predict transmission dynamics, and to assess the potential impact of interventions. Each of these models is based on a trade-off between accuracy, transparency, and flexibility, relying on a set of assumptions made to represent how infections occur. However, the impact of specific mechanistic assumptions is not always straightforward to assess, and the lack of comprehensive datasets makes it challenging to disentangle the contributions of the different components characterising the transmission process. To gain insight into the drivers of transmission and their interplay, we developed a multiscale framework based on the theory of counting processes. Infection events are expressed here as the result of contact interactions thinned by time-varying probability values accounting for susceptibility and infectiousness levels. Based on this framework, we developed an individual-based model and conducted a series of simulation studies to investigate assumptions related to the transmission process and study the potential effectiveness of mitigation measures.