Dynamic networks for improved epidemiological modelling and better pandemic preparedness.

Infectious diseases like COVID-19 or mpox spread through our interactions with other people; however, we still don't fully understand how the timing and structure of these interactions shape outbreaks. My research explores how the patterns of people's interactions and their social contacts affect diseases spread through a population. I will use a gamified mobile-app developed with Epidemica, a framework for digital epidemiology, to track real-time, close- proximity interactions of the mobile phones. By undertaking naturalistic experiments of people playing this mobile-phone game during which a disease spreads, we will map how people interact in different environments. This will allow us to see how interactions change over space and time, for example, how many people you interact with at different parts of the day or in different seasons, which will help gain better understanding of how diseases might travel through a population across contexts. This project will create a suite of social-connections networks, develop tools to analyse these and make them usable across infectious disease models. This will facilitate real-life networks inclusion in models which would help design more targeted interventions, supporting public health teams to decide when and where actions like targeted-testing, vaccination, or closures will have the most impact.