State-level tracking of COVID-19 in the United States
File(s)s41467-020-19652-6.pdf (1.35 MB)
Published version
Author(s)
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 Issued
2020-12-03
Date Acceptance
2020-10-15
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/
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/
License URL
Sponsor
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)
Identifier
https://www.nature.com/articles/s41467-020-19652-6
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
Subjects
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
Date Publish Online
2020-12-03