Estimating individual risks of COVID-19-associated hospitalization and death using publicly available data.
We describe a method to estimate individual risks of hospitalization and death attributable to non-household and household transmission of SARS-CoV-2 using available public data on confirmed-case incidence data along with estimates of the clinical fraction, timing of transmission, isolation adherenc...
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2020-01-01
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Online Access: | https://doi.org/10.1371/journal.pone.0243026 |
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doaj-2eaeecffa2fd4e4290639870cdb841f82021-03-04T12:49:42ZengPublic Library of Science (PLoS)PLoS ONE1932-62032020-01-011512e024302610.1371/journal.pone.0243026Estimating individual risks of COVID-19-associated hospitalization and death using publicly available data.Rajiv BhatiaJeffrey KlausnerWe describe a method to estimate individual risks of hospitalization and death attributable to non-household and household transmission of SARS-CoV-2 using available public data on confirmed-case incidence data along with estimates of the clinical fraction, timing of transmission, isolation adherence, secondary infection risks, contact rates, and case-hospitalization and case-fatality ratios. Using the method, we estimate that risks for a 90-day period at the median daily summertime U.S. county confirmed COVID-19 case incidence of 10.8 per 100,000 and pre-pandemic contact rates range from 0.4 to 8.9 per 100,000 for the four deciles of age between 20 and 60 years. The corresponding 90-day period risk of hospitalization ranges from 13.7 to 69.2 per 100,000. Assuming a non-household secondary infection risk of 4% and pre-pandemic contact rates, the share of transmissions attributable to household settings ranges from 73% to 78%. These estimates are sensitive to the parameter assumptions; nevertheless, they are comparable to the COVID-19 hospitalization and fatality rates observed over the time period. We conclude that individual risk of hospitalization and death from SARS-CoV-2 infection is calculable from publicly available data sources. Access to publicly reported infection incidence data by setting and other exposure characteristics along with setting specific estimates of secondary infection risk would allow for more precise individual risk estimation.https://doi.org/10.1371/journal.pone.0243026 |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Rajiv Bhatia Jeffrey Klausner |
spellingShingle |
Rajiv Bhatia Jeffrey Klausner Estimating individual risks of COVID-19-associated hospitalization and death using publicly available data. PLoS ONE |
author_facet |
Rajiv Bhatia Jeffrey Klausner |
author_sort |
Rajiv Bhatia |
title |
Estimating individual risks of COVID-19-associated hospitalization and death using publicly available data. |
title_short |
Estimating individual risks of COVID-19-associated hospitalization and death using publicly available data. |
title_full |
Estimating individual risks of COVID-19-associated hospitalization and death using publicly available data. |
title_fullStr |
Estimating individual risks of COVID-19-associated hospitalization and death using publicly available data. |
title_full_unstemmed |
Estimating individual risks of COVID-19-associated hospitalization and death using publicly available data. |
title_sort |
estimating individual risks of covid-19-associated hospitalization and death using publicly available data. |
publisher |
Public Library of Science (PLoS) |
series |
PLoS ONE |
issn |
1932-6203 |
publishDate |
2020-01-01 |
description |
We describe a method to estimate individual risks of hospitalization and death attributable to non-household and household transmission of SARS-CoV-2 using available public data on confirmed-case incidence data along with estimates of the clinical fraction, timing of transmission, isolation adherence, secondary infection risks, contact rates, and case-hospitalization and case-fatality ratios. Using the method, we estimate that risks for a 90-day period at the median daily summertime U.S. county confirmed COVID-19 case incidence of 10.8 per 100,000 and pre-pandemic contact rates range from 0.4 to 8.9 per 100,000 for the four deciles of age between 20 and 60 years. The corresponding 90-day period risk of hospitalization ranges from 13.7 to 69.2 per 100,000. Assuming a non-household secondary infection risk of 4% and pre-pandemic contact rates, the share of transmissions attributable to household settings ranges from 73% to 78%. These estimates are sensitive to the parameter assumptions; nevertheless, they are comparable to the COVID-19 hospitalization and fatality rates observed over the time period. We conclude that individual risk of hospitalization and death from SARS-CoV-2 infection is calculable from publicly available data sources. Access to publicly reported infection incidence data by setting and other exposure characteristics along with setting specific estimates of secondary infection risk would allow for more precise individual risk estimation. |
url |
https://doi.org/10.1371/journal.pone.0243026 |
work_keys_str_mv |
AT rajivbhatia estimatingindividualrisksofcovid19associatedhospitalizationanddeathusingpubliclyavailabledata AT jeffreyklausner estimatingindividualrisksofcovid19associatedhospitalizationanddeathusingpubliclyavailabledata |
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