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Analysis of COVID-19 Epidemics on a Timescale of Burden

31 Pages Posted: 19 Jul 2021 Publication Status: Review Complete

See all articles by Alex Washburne

Alex Washburne

Selva Analytics LLC

Justin Silverman

Pennsylvania State University - Department of Statistics

José Lourenço

University of Oxford - Department of Zoology

Nathaniel Hupert

Departments of Population Health Sciences and of Medicine, Weill Cornell Medicine, and Cornell Institute for Disease and Disaster Preparedness

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Abstract

The 2020 COVID-19 pandemic produced thousands of well-quantified epidemics in counties, states, and countries around the world. Comparing the dynamics and outcomes of these nested epidemics could improve our understanding of the impact of non-pharmaceutical interventions (NPIs) and help public health officials, political leaders, and other managers with real-time risk assessment across multiple geographic levels. Cross-outbreak comparisons are challenging due to their variable dates of introduction of the SARS-CoV-2 virus, rates of transmission, case detection rates, and asynchronous and diverse management interventions.

Here, we present a new graphical method for comparing ongoing COVID-19 epidemics by using disease burden as a natural timescale for comparison. Trajectories of growth rates of cases over the timescale of lagged deaths per-capita produces coherent visual comparisons of epidemics that are otherwise incoherent and asynchronous in the timescale of calendar dates or incomparable using non-stationary measures of burden such as cases. Applied to US COVID-19 outbreaks, this approach intuitively depicts multiple findings including early lockdowns reducing transmission at fewer deaths per-capita early in the epidemic, reopenings coinciding clearly with resurgent summer epidemics, and peaks in unmitigated epidemics that, while separated in time and place, converge to a population fatality rate lower than current, commonly used estimates based on infection fatality rates and herd immunity thresholds.

Our method uses early and minimally mitigated epidemics, like that in NYC in March-April 2020 and Sweden in later 2020, to define what we call “epidemic resistance lines” (ERLs) or hypothesized upper bounds of epidemic speed and burden. ERLs from less-mitigated epidemics allow cross-outbreak benchmarking, for example of resurgent summer epidemics in the US. In particular, the unmitigated NYC epidemic resistance line appears to bound the growth rates of 3,000 US counties and funnel growth rates across counties to their peaks where growth rates equal zero in the fall and winter of 2020 before vaccines could be widely administered. Corroboration of upper-bounds on epidemic trajectories allowed early predictions of mortality burden for unmitigated COVID-19 epidemics in these populations, predictions that were more accurate for counties in states without mask-wearing mandates. We discuss how this method could be used for future epidemics, including seasonal epidemics caused by influenza or ongoing epidemics caused by new SARS-CoV-2 variants.

Suggested Citation

Washburne, Alex and Silverman, Justin and Lourenço, José and Hupert, Nathaniel, Analysis of COVID-19 Epidemics on a Timescale of Burden. Available at SSRN: https://ssrn.com/abstract=3889703 or http://dx.doi.org/10.2139/ssrn.3889703
This version of the paper has not been formally peer reviewed.

Alex Washburne (Contact Author)

Selva Analytics LLC ( email )

Bozeman, MT
United States

Justin Silverman

Pennsylvania State University - Department of Statistics ( email )

United States

José Lourenço

University of Oxford - Department of Zoology ( email )

New Radcliffe House
Radcliffe Observatory Quarter
Oxford, OX13 5QL
United Kingdom

Nathaniel Hupert

Departments of Population Health Sciences and of Medicine, Weill Cornell Medicine, and Cornell Institute for Disease and Disaster Preparedness ( email )

1300 York Avenue
New York, NY 10065
United States

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