Estimated Incidence of Coronavirus Disease 2019 (COVID-19) Illness and Hospitalization—United States, February–September 2020
Abstract Background In the United States, laboratory-confirmed coronavirus disease 2019 (COVID-19) is nationally notifiable. However, reported case counts are recognized to be less than the true number of cases because detection and reporting are incomplete and can vary by disease severity, geograph...
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Published in | Clinical infectious diseases Vol. 72; no. 12; pp. e1010 - e1017 |
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Main Authors | , , , , , , , , |
Format | Journal Article |
Language | English |
Published |
15.06.2021
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Online Access | Get full text |
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Summary: | Abstract
Background
In the United States, laboratory-confirmed coronavirus disease 2019 (COVID-19) is nationally notifiable. However, reported case counts are recognized to be less than the true number of cases because detection and reporting are incomplete and can vary by disease severity, geography, and over time.
Methods
To estimate the cumulative incidence of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infections, symptomatic illnesses, and hospitalizations, we adapted a simple probabilistic multiplier model. Laboratory-confirmed case counts that were reported nationally were adjusted for sources of underdetection based on testing practices in inpatient and outpatient settings and assay sensitivity.
Results
We estimated that through the end of September, 1 of every 2.5 (95% uncertainty interval [UI]: 2.0–3.1) hospitalized infections and 1 of every 7.1 (95% UI: 5.8–9.0) nonhospitalized illnesses may have been nationally reported. Applying these multipliers to reported SARS-CoV-2 cases along with data on the prevalence of asymptomatic infection from published systematic reviews, we estimate that 2.4 million hospitalizations, 44.8 million symptomatic illnesses, and 52.9 million total infections may have occurred in the US population from 27 February–30 September 2020.
Conclusions
These preliminary estimates help demonstrate the societal and healthcare burdens of the COVID-19 pandemic and can help inform resource allocation and mitigation planning. |
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Bibliography: | ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 23 |
ISSN: | 1058-4838 1537-6591 |
DOI: | 10.1093/cid/ciaa1780 |