RAPID: The effect of contact network structure on the spread of COVID-19: balancing disease mitigation and socioeconomic well-being


What makes COVID-19 spread rapidly in some places, yet slowly in others? How should society lessen social distancing while limiting an increase in infections? To answer these questions, this Rapid Response Research (RAPID) project seeks to understand how patterns of interpersonal interaction (?structure?) in social contact networks affect disease spread in a population. The researchers will simulate a disease spreading through a variety of social contact networks, and use machine learning to relate each network?s structure to the number and timing of new infections. By limiting structures related to increased disease, societies may be able to reopen other parts of their economies while still curbing overall disease spread. The researchers will produce an interactive web application for the public and decision-makers to visualize trade-offs between reducing disease and maintaining social cohesion. This research will support the professional development of an early career scientist. This research aims to determine the inherent risk of SARS-CoV-2 spread based on contact network structure. The researchers will use machine learning to 1) identify network structures that influence disease spread and 2) predict disease spread on empirical contact networks. Important network structures will serve as targets for simulated disease mitigation interventions (e.g. reducing structures that increase levels of disease or increasing structures that reduce disease levels). Finally, the researchers will investigate whether future outbreaks of COVID-19 or other diseases could be alleviated through optimizing social contact networks ahead of time. The outcomes of this research will inform and facilitate quick, efficient interventions to reduce the social and economic costs of COVID-19. This research will develop a general framework for relating disease to network structure. Thus, results can be generalized beyond the current pandemic, serving to further our understanding of potential future waves of COVID-19, as well as other directly-transmitted diseases in humans, livestock, and wildlife.


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