Real-time pandemic surveillance using hospital admissions and mobility data.
Proc Natl Acad Sci U S A
; 119(7)2022 02 15.
Article
in English
| MEDLINE | ID: covidwho-1671750
ABSTRACT
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.
Keywords
Full text:
Available
Collection:
International databases
Database:
MEDLINE
Main subject:
Pandemics
/
SARS-CoV-2
/
COVID-19
/
Hospitals
Type of study:
Observational study
/
Prognostic study
Limits:
Humans
Country/Region as subject:
North America
Language:
English
Year:
2022
Document Type:
Article
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