An indirect method to monitor the fraction of people ever infected with COVID-19: An application to the United States.

The number of COVID-19 infections is key for accurately monitoring the pandemics. However, due to differential testing policies, asymptomatic individuals and limited large-scale testing availability, it is challenging to detect all cases. Seroprevalence studies aim to address this gap by retrospecti...

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Main Authors: Miguel Sánchez-Romero, Vanessa di Lego, Alexia Prskawetz, Bernardo L Queiroz
Format: Article
Language:English
Published: Public Library of Science (PLoS) 2021-01-01
Series:PLoS ONE
Online Access:https://doi.org/10.1371/journal.pone.0245845
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spelling doaj-42d4bd3b0bab4c6a85a20ba7fe835f4f2021-03-04T13:10:44ZengPublic Library of Science (PLoS)PLoS ONE1932-62032021-01-01161e024584510.1371/journal.pone.0245845An indirect method to monitor the fraction of people ever infected with COVID-19: An application to the United States.Miguel Sánchez-RomeroVanessa di LegoAlexia PrskawetzBernardo L QueirozThe number of COVID-19 infections is key for accurately monitoring the pandemics. However, due to differential testing policies, asymptomatic individuals and limited large-scale testing availability, it is challenging to detect all cases. Seroprevalence studies aim to address this gap by retrospectively assessing the number of infections, but they can be expensive and time-intensive, limiting their use to specific population subgroups. In this paper, we propose a complementary approach that combines estimated (1) infection fatality rates (IFR) using a Bayesian melding SEIR model with (2) reported case-fatality rates (CFR) in order to indirectly estimate the fraction of people ever infected (from the total population) and detected (from the ever infected). We apply the technique to the U.S. due to their remarkable regional diversity and because they count with almost a quarter of all global confirmed cases and deaths. We obtain that the IFR varies from 1.25% (0.39-2.16%, 90% CI) in Florida, the most aged population, to 0.69% in Utah (0.21-1.30%, 90% CI), the youngest population. By September 8, 2020, we estimate that at least five states have already a fraction of people ever infected between 10% and 20% (New Jersey, New York, Massachussets, Connecticut, and District of Columbia). The state with the highest estimated fraction of people ever infected is New Jersey with 17.3% (10.0, 55.8, 90% CI). Moreover, our results indicate that with a probability of 90 percent the fraction of detected people among the ever infected since the beginning of the epidemic has been less than 50% in 15 out of the 20 states analyzed in this paper. Our approach can be a valuable tool that complements seroprevalence studies and indicates how efficient have testing policies been since the beginning of the outbreak.https://doi.org/10.1371/journal.pone.0245845
collection DOAJ
language English
format Article
sources DOAJ
author Miguel Sánchez-Romero
Vanessa di Lego
Alexia Prskawetz
Bernardo L Queiroz
spellingShingle Miguel Sánchez-Romero
Vanessa di Lego
Alexia Prskawetz
Bernardo L Queiroz
An indirect method to monitor the fraction of people ever infected with COVID-19: An application to the United States.
PLoS ONE
author_facet Miguel Sánchez-Romero
Vanessa di Lego
Alexia Prskawetz
Bernardo L Queiroz
author_sort Miguel Sánchez-Romero
title An indirect method to monitor the fraction of people ever infected with COVID-19: An application to the United States.
title_short An indirect method to monitor the fraction of people ever infected with COVID-19: An application to the United States.
title_full An indirect method to monitor the fraction of people ever infected with COVID-19: An application to the United States.
title_fullStr An indirect method to monitor the fraction of people ever infected with COVID-19: An application to the United States.
title_full_unstemmed An indirect method to monitor the fraction of people ever infected with COVID-19: An application to the United States.
title_sort indirect method to monitor the fraction of people ever infected with covid-19: an application to the united states.
publisher Public Library of Science (PLoS)
series PLoS ONE
issn 1932-6203
publishDate 2021-01-01
description The number of COVID-19 infections is key for accurately monitoring the pandemics. However, due to differential testing policies, asymptomatic individuals and limited large-scale testing availability, it is challenging to detect all cases. Seroprevalence studies aim to address this gap by retrospectively assessing the number of infections, but they can be expensive and time-intensive, limiting their use to specific population subgroups. In this paper, we propose a complementary approach that combines estimated (1) infection fatality rates (IFR) using a Bayesian melding SEIR model with (2) reported case-fatality rates (CFR) in order to indirectly estimate the fraction of people ever infected (from the total population) and detected (from the ever infected). We apply the technique to the U.S. due to their remarkable regional diversity and because they count with almost a quarter of all global confirmed cases and deaths. We obtain that the IFR varies from 1.25% (0.39-2.16%, 90% CI) in Florida, the most aged population, to 0.69% in Utah (0.21-1.30%, 90% CI), the youngest population. By September 8, 2020, we estimate that at least five states have already a fraction of people ever infected between 10% and 20% (New Jersey, New York, Massachussets, Connecticut, and District of Columbia). The state with the highest estimated fraction of people ever infected is New Jersey with 17.3% (10.0, 55.8, 90% CI). Moreover, our results indicate that with a probability of 90 percent the fraction of detected people among the ever infected since the beginning of the epidemic has been less than 50% in 15 out of the 20 states analyzed in this paper. Our approach can be a valuable tool that complements seroprevalence studies and indicates how efficient have testing policies been since the beginning of the outbreak.
url https://doi.org/10.1371/journal.pone.0245845
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