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Infectious Disease Spread on Context-Specific Interaction Networks: Influenza and COVID-19


The spread of infectious disease is strongly influenced by human interaction. In order to understand this connection and develop effective strategies against future outbreaks, it is important to construct models which capture the intricacies of real-world social encounters. In this work, we generate networks which maintain some features of the partial interaction networks that were recorded in an existing diary-based survey at the University of Warwick. To preserve realistic structure in our artificial networks, we use a context-specific approach. In particular, we propose different algorithms for producing larger home, work, and social networks. Our networks capture much of the interaction structure in the original diary-based survey and offer a way to account for the interactions of survey participants with non-participants. Simulating a discrete susceptible-infected-recovered (SIR) model on the full network produces epidemic behavior which shares characteristics with previous influenza seasons. Our approach allows us to explore how disease transmission and dynamic responses to infection differ depending on interaction context. We find that, while social interactions may be reduced first after influenza infection, limiting work and school encounters is significantly more effective in controlling the overall severity of the epidemic. Combining our algorithms for extending the diary-based data with household- and business-size data in the U.S., we construct a large, context-specific network to model Providence, RI. We simulate a COVID-19 outbreak on the network and explore the impact of various infection control measures. We quantify the lowered interaction rate caused by the lockdown in Providence, RI at the start of the COVID-19 pandemic and discover that large-business closures have the greatest impact on disease spread. To the contrary, closing small businesses does not significantly lower the total number of infections in the community. Our results further indicate that contact tracing is not a feasible method for limiting SARS-CoV-2 transmission.
Thesis (Ph. D.)--Brown University, 2021


Mallory, Kristina, "Infectious Disease Spread on Context-Specific Interaction Networks: Influenza and COVID-19" (2021). Applied Mathematics Theses and Dissertations. Brown Digital Repository. Brown University Library.