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Surveillance versus contact-tracing on configuration model graphs

Banff International Research Station for Mathematical Innovation and Discovery

The main object of study in this paper is an epidemic process on a large network in the presence of various public health interventions. As an example, we consider a simple Susceptible-Infected (SI)-type epidemic process on a Configuration Model (CM) random graph with public health interventions in the form of active random surveillance and contact-tracing. While infected individuals attempt to infect their neighbours, they themselves are at risk of removal due to random surveillance and contact-tracing. We allow the random graph to be constructed dynamically as an outcome of the spread of infection and removal due to contact-tracing. We study the large graph limit of these two competing processes (infection and contact-tracing) as the number of vertices grows to infinity. From the public health perspective, the large graph limit can be utilized to determine the optimal rates for surveillance and contact-tracing given a fixed budget constraint by formulating a suitable optimal control problem. Joint work with Soheil Eshghi, Eben Kenah, Forrest W. Crawford and Grzegorz A. Rempaà a.

Mathematics; Probability theory and stochastic processes; Statistics

video/mp4

Author affiliation: The Ohio State University

2019-11-17

Attribution-NonCommercial-NoDerivatives 4.0 International

http://hdl.handle.net/2429/72289

Unreviewed

http://creativecommons.org/licenses/by-nc-nd/4.0/