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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 SBF004/1080 RES- -62388 MR/R015600/1 213494/Z/18/Z 215359/Z/19/Z MR/K010174/1B NIHR200908 PO14/00561 EP/V520354/1 |
Keywords: | Science & Technology Multidisciplinary Sciences Science & Technology - Other Topics Bayes Theorem COVID-19 Humans Models, Statistical Pandemics United States Virus Diseases Humans Virus Diseases Models, Statistical Bayes Theorem United States Pandemics COVID-19 |
Publication Status: | Published |
Online Publication Date: | 2020-12-03 |
Appears in Collections: | Mathematics Department of Infectious Diseases Statistics 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