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A Method for Estimating the Number of Infections From the Reported Number of Deaths
Umeå University, Faculty of Science and Technology, Department of Mathematics and Mathematical Statistics. Advancing Systems Analysis Program, International Institute for Applied Systems Analysis, Laxenburg, Austria.ORCID iD: 0000-0001-9862-816x
Umeå University, Faculty of Medicine, Department of Public Health and Clinical Medicine, Section of Sustainable Health.
Umeå University, Faculty of Medicine, Department of Public Health and Clinical Medicine, Section of Sustainable Health.ORCID iD: 0000-0003-4030-0449
2022 (English)In: Frontiers In Public Health, ISSN 2296-2565, Vol. 9, article id 648545Article in journal (Refereed) Published
Abstract [en]

At the outset of an epidemic, available case data typically underestimate the total number of infections due to insufficient testing, potentially hampering public responses. Here, we present a method for statistically estimating the true number of cases with confidence intervals from the reported number of deaths and estimates of the infection fatality ratio; assuming that the time from infection to death follows a known distribution. While the method is applicable to any epidemic with a significant mortality rate, we exemplify the method by applying it to COVID-19. Our findings indicate that the number of unreported COVID-19 infections in March 2020 was likely to be at least one order of magnitude higher than the reported cases, with the degree of underestimation among the countries considered being particularly high in the United Kingdom.

Place, publisher, year, edition, pages
Frontiers Media S.A., 2022. Vol. 9, article id 648545
Keywords [en]
COVID-19, estimating, infectives, nowcasting, surveillance
National Category
Public Health, Global Health, Social Medicine and Epidemiology Probability Theory and Statistics
Identifiers
URN: urn:nbn:se:umu:diva-192376DOI: 10.3389/fpubh.2021.648545PubMedID: 35111706Scopus ID: 2-s2.0-85123950757OAI: oai:DiVA.org:umu-192376DiVA, id: diva2:1636943
Available from: 2022-02-11 Created: 2022-02-11 Last updated: 2022-02-11Bibliographically approved

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Brännström, ÅkeSjödin, HenrikRocklöv, Joacim

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Brännström, ÅkeSjödin, HenrikRocklöv, Joacim
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Public Health, Global Health, Social Medicine and EpidemiologyProbability Theory and Statistics

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