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

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Title: State-level tracking of COVID-19 in the United States
Authors: Unwin, H
Mishra, S
Bradley, V
Gandy, A
Mellan, T
Coupland, H
Ish-Horowicz, J
Vollmer, M
Whittaker, C
Filippi, S
Xi, X
Monod, M
Ratmann, O
Hutchinson, M
Valka, F
Zhu, H
Hawryluk, I
Milton, P
Ainslie, K
Baguelin, M
Boonyasiri, A
Brazeau, N
Cattarino, L
Cucunuba, Z
Cuomo-Dannenburg, G
Dorigatti, I
Eales, O
Eaton, J
Van Elsland, S
Fitzjohn, R
Gaythorpe, K
Green, W
Hinsley, W
Jeffrey, B
Knock, E
Laydon, D
Lees, J
Nedjati-Gilani, G
Nouvellet, P
Okell, L
Parag, K
Siveroni, I
Thompson, H
Walker, P
Walters, C
Watson, O
Whittles, L
Ghani, A
Ferguson, N
Riley, S
Donnelly, C
Bhatt, S
Flaxman, S
Item Type: Journal Article
Abstract: As of 1st June 2020, the US Centers 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 deathdata 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 therate of transmission of SARS-CoV-2. We estimate thatRtwas 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.
Issue Date: 3-Dec-2020
Date of Acceptance: 15-Oct-2020
URI: http://hdl.handle.net/10044/1/83296
DOI: 10.1038/s41467-020-19652-6
ISSN: 2041-1723
Publisher: Nature Research
Start Page: 1
End Page: 9
Journal / Book Title: Nature Communications
Volume: 11
Issue: 6189
Copyright Statement: © The Author(s) 2020. This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/
Sponsor/Funder: Imperial College Healthcare NHS Trust- BRC Funding
The Academy of Medical Sciences
Bill & Melinda Gates Foundation
Medical Research Council (MRC)
Wellcome Trust
Wellcome Trust
Medical Research Council (MRC)
National Institute for Health Research
Medicines for Malaria Venture
Medical Research Council (MRC)
Funder's Grant Number: RDA02
RES- -62388
Keywords: Science & Technology
Multidisciplinary Sciences
Science & Technology - Other Topics
Bayes Theorem
Models, Statistical
United States
Virus Diseases
Virus Diseases
Models, Statistical
Bayes Theorem
United States
Publication Status: Published
Online Publication Date: 2020-12-03
Appears in Collections:Mathematics
Department of Infectious Diseases
Faculty of Medicine
Imperial College London COVID-19
School of Public Health
Faculty of Natural Sciences

This item is licensed under a Creative Commons License Creative Commons