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State-level tracking of COVID-19 in the United States.

Authors :
Unwin, H. Juliette T.
Mishra, Swapnil
Bradley, Valerie C.
Gandy, Axel
Mellan, Thomas A.
Coupland, Helen
Ish-Horowicz, Jonathan
Vollmer, Michaela A. C.
Whittaker, Charles
Filippi, Sarah L.
Xi, Xiaoyue
Monod, Mélodie
Ratmann, Oliver
Hutchinson, Michael
Valka, Fabian
Zhu, Harrison
Hawryluk, Iwona
Milton, Philip
Ainslie, Kylie E. C.
Baguelin, Marc
Source :
Nature Communications; 12/3/2020, Vol. 11 Issue 1, pN.PAG-N.PAG, 1p
Publication Year :
2020

Abstract

As of 1st June 2020, the US Centres for Disease Control and Prevention reported 104,232 confirmed or probable COVID-19-related deaths in the US. This was more than twice the number of deaths reported in the next most severely impacted country. We jointly model the US epidemic at the state-level, using publicly available death data within a Bayesian hierarchical semi-mechanistic framework. For each state, we estimate the number of individuals that have been infected, the number of individuals that are currently infectious and the time-varying reproduction number (the average number of secondary infections caused by an infected person). We use changes in mobility to capture the impact that non-pharmaceutical interventions and other behaviour changes have on the rate of transmission of SARS-CoV-2. We estimate that R<subscript>t</subscript> was only below one in 23 states on 1st June. We also estimate that 3.7% [3.4%–4.0%] of the total population of the US had been infected, with wide variation between states, and approximately 0.01% of the population was infectious. We demonstrate good 3 week model forecasts of deaths with low error and good coverage of our credible intervals. High numbers of COVID-19-related deaths have been reported in the United States, but estimation of the true numbers of infections is challenging. Here, the authors estimate that on 1 June 2020, 3.7% of the US population was infected with SARS-CoV-2, and 0.01% was infectious, with wide variation by state. [ABSTRACT FROM AUTHOR]

Details

Language :
English
ISSN :
20411723
Volume :
11
Issue :
1
Database :
Complementary Index
Journal :
Nature Communications
Publication Type :
Academic Journal
Accession number :
147362288
Full Text :
https://doi.org/10.1038/s41467-020-19652-6