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arXiv · 2109.06025

Planning a Return to Normal after the COVID-19 Pandemic: Identifying Safe Contact Levels via Online Optimization

Gianluca Bianchin·Emiliano Dall'Anese·Jorge I. Poveda·David Jacobson·Elizabeth J. Carlton·Andrea G. Buchwald
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Abstract

Since the early months of 2020, non-pharmaceutical interventions (NPIs) -- implemented at varying levels of severity and based on widely-divergent perspectives of risk tolerance -- have been the primary means to control SARS-CoV-2 transmission. We seek to identify how risk tolerance and vaccination rates impact the rate at which a population can return to pre-pandemic contact behavior. To this end, we develop a novel feedback control method for data-driven decision-making to identify optimal levels of NPIs across geographical regions in order to guarantee that hospitalizations will not exceed a given risk tolerance. Results are shown for the state of Colorado, and they suggest that: coordination in decision-making across regions is essential to maintain the daily number of hospitalizations below the desired limits; increasing risk tolerance can decrease the number of days required to discontinue NPIs, at the cost of an increased number of deaths; and if vaccination uptake is less than 70\%, at most levels of risk tolerance, return to pre-pandemic contact behaviors before the early months of 2022 may newly jeopardize the healthcare system.

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Gianluca Bianchin, Emiliano Dall'Anese, Jorge I. Poveda, David Jacobson, Elizabeth J. Carlton, Andrea G. Buchwald. 2021-09-13. Planning a Return to Normal after the COVID-19 Pandemic: Identifying Safe Contact Levels via Online Optimization. https://arxiv.org/abs/2109.06025

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