The use of global connectivity estimates in real-time models of international infectious disease spread

Human mobility plays an important part in the spread of infectious diseases. Mathematical models can assess the risk that an emerging outbreak will spread internationally, providing decision-makers with information to support early surveillance and control measures. In this project I will aim to improve how well these models capture international mobility patterns by exploring the suitability of different measures of connectivity. I will compare flight passenger data (which is commonly used in international infectious disease spread models) with alternative transport passenger statistics and novel data, such as location data from mobile phones. I will use these data sources (both individually and in combination) in models to make predictions of the risks of imported and exported disease cases, and assess how well these different models can retrospectively predict the global spread of COVID-19. I will use these findings to develop a statistical model and tools that are ready in advance of future outbreaks to make rapid assessments of the risks that they will spread geographically. The project will lead to more realistic models of international infectious disease spread and thus improve the information that is available to support decision-makers in controlling future outbreaks before they become widespread.