Real-time pandemic surveillance using hospital admissions and mobility data.
COVID-19
epidemiological data
forecasting
healthcare usage
Journal
Proceedings of the National Academy of Sciences of the United States of America
ISSN: 1091-6490
Titre abrégé: Proc Natl Acad Sci U S A
Pays: United States
ID NLM: 7505876
Informations de publication
Date de publication:
15 02 2022
15 02 2022
Historique:
accepted:
04
01
2022
entrez:
2
2
2022
pubmed:
3
2
2022
medline:
9
2
2022
Statut:
ppublish
Résumé
Forecasting the burden of COVID-19 has been impeded by limitations in data, with case reporting biased by testing practices, death counts lagging far behind infections, and hospital census reflecting time-varying patient access, admission criteria, and demographics. Here, we show that hospital admissions coupled with mobility data can reliably predict severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) transmission rates and healthcare demand. Using a forecasting model that has guided mitigation policies in Austin, TX, we estimate that the local reproduction number had an initial 7-d average of 5.8 (95% credible interval [CrI]: 3.6 to 7.9) and reached a low of 0.65 (95% CrI: 0.52 to 0.77) after the summer 2020 surge. Estimated case detection rates ranged from 17.2% (95% CrI: 11.8 to 22.1%) at the outset to a high of 70% (95% CrI: 64 to 80%) in January 2021, and infection prevalence remained above 0.1% between April 2020 and March 1, 2021, peaking at 0.8% (0.7-0.9%) in early January 2021. As precautionary behaviors increased safety in public spaces, the relationship between mobility and transmission weakened. We estimate that mobility-associated transmission was 62% (95% CrI: 52 to 68%) lower in February 2021 compared to March 2020. In a retrospective comparison, the 95% CrIs of our 1, 2, and 3 wk ahead forecasts contained 93.6%, 89.9%, and 87.7% of reported data, respectively. Developed by a task force including scientists, public health officials, policy makers, and hospital executives, this model can reliably project COVID-19 healthcare needs in US cities.
Identifiants
pubmed: 35105729
pii: 2111870119
doi: 10.1073/pnas.2111870119
pmc: PMC8851544
pii:
doi:
Types de publication
Journal Article
Research Support, N.I.H., Extramural
Research Support, U.S. Gov't, P.H.S.
Langues
eng
Sous-ensembles de citation
IM
Subventions
Organisme : NIAID NIH HHS
ID : R01 AI151176
Pays : United States
Organisme : NCIRD CDC HHS
ID : U01 IP001136
Pays : United States
Organisme : ACL HHS
ID : U01IP001136
Pays : United States
Commentaires et corrections
Type : CommentIn
Informations de copyright
Copyright © 2022 the Author(s). Published by PNAS.
Déclaration de conflit d'intérêts
The authors declare no competing interest.
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