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.
Date of Acceptance: 15-Oct-2020
URI: http://hdl.handle.net/10044/1/83296
ISSN: 2041-1723
Publisher: Nature Research
Journal / Book Title: Nature Communications
Copyright Statement: This paper is embargoed until publication. Once published it will be available fully open access.
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
Funder's Grant Number: RDA02
SBF004/1080
CRR00280
MR/R015600/1
213494/Z/18/Z
215359/Z/19/Z
Publication Status: Accepted
Embargo Date: publication subject to indefinite embargo
Appears in Collections:Mathematics
Department of Infectious Diseases
Statistics
Faculty of Medicine
Faculty of Natural Sciences
School of Public Health
Imperial College London COVID-19