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

H. Juliette T. Unwin*, Swapnil Mishra, Valerie C. Bradley, Axel Gandy, Thomas A. Mellan, Helen Coupland, Jonathan Ish-Horowicz, Michaela A.C. Vollmer, Charles Whittaker, Sarah L. Filippi, Xiaoyue Xi, Mélodie Monod, Oliver Ratmann, Michael Hutchinson, Fabian Valka, Harrison Zhu, Iwona Hawryluk, Philip Milton, Kylie E.C. Ainslie, Marc BaguelinAdhiratha Boonyasiri, Nick F. Brazeau, Lorenzo Cattarino, Zulma Cucunuba, Gina Cuomo-Dannenburg, Ilaria Dorigatti, Oliver D. Eales, Jeffrey W. Eaton, Sabine L. van Elsland, Richard G. FitzJohn, Katy A.M. Gaythorpe, William Green, Wes Hinsley, Benjamin Jeffrey, Edward Knock, Daniel J. Laydon, John Lees, Gemma Nedjati-Gilani, Pierre Nouvellet, Lucy Okell, Kris V. Parag, Igor Siveroni, Hayley A. Thompson, Patrick Walker, Caroline E. Walters, Oliver J. Watson, Lilith K. Whittles, Azra C. Ghani, Neil M. Ferguson, Steven Riley, Christl A. Donnelly, Samir Bhatt, Seth Flaxman

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

15 Citations (Scopus)

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 Rt 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.

Original languageEnglish
Article number6189
JournalNature Communications
Volume11
Issue number1
DOIs
Publication statusPublished - Dec 2020
Externally publishedYes

Bibliographical note

Funding Information:
We would like to thank Amazon AWS and Microsoft AI for health for computational credits and we would like to thank the Stan development team for their ongoing assistance. We would also like to thank David Joerg and Jacob Steinhardt for their comments through Open Review. This research was partly funded by the Imperial College COVID-19 Research Fund and was supported by Centre funding from the UK Medical Research Council under a concordat with the UK Department for International Development, the NIHR Health Protection Research Unit in Modelling Methodology and Community Jameel. H.J.T.U. is funded by Imperial College London through an Imperial College Research Fellowship grant. S.B. acknowledges the NIHR BRC Imperial College NHS Trust Infection and COVID themes, the Academy of Medical Sciences Springboard award and the Bill and Melinda Gates Foundation.

Publisher Copyright:
© 2020, The Author(s).

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