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Activity Number: 130 - Advances in Resource Allocation for Epidemic Control: Estimation, Optimization, and Counterfactuals
Type: Topic Contributed
Date/Time: Monday, August 3, 2020 : 1:00 PM to 2:50 PM
Sponsor: Section on Statistics in Epidemiology
Abstract #312849
Title: The Cost of Uncertainty in Curing Epidemics
Author(s): Jessica Hoffmann*
Companies: University of Texas At Austin
Keywords: Epidemics; Uncertainty; Graph Algorithms

Motivated by the study of controlling (curing) epidemics under uncertainty in these covid-19-afflicted times, we consider the spread of an SI process on a known graph, where we have a limited budget to use to transition infected nodes back to the susceptible state. Recent work has demonstrated that under perfect and instantaneous information (which nodes are/are not infected), the budget required for curing a graph depends on a combinatorial property, the CutWidth. We show that this assumption is in fact necessary: even a minor degradation of perfect information, e.g., a diagnostic test that is 99% accurate, drastically alters the landscape. Infections that could previously be cured in sublinear time now may require exponential time, or orderwise larger budget to cure. The crux of the issue comes down to a tension not present in the full information case: if a node is suspected (but not certain) to be infected, do we risk wasting our budget to try to cure an uninfected node, or increase our certainty by longer observation, at the risk that the infection spreads further? Our results present fundamental, algorithm-independent bounds that tradeoff budget required vs. uncertainty.

Authors who are presenting talks have a * after their name.

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