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Inferring the true number of SARS-CoV-2 infections in Japan (preprint)
medrxiv; 2022.
Preprint in English | medRxiv | ID: ppzbmed-10.1101.2022.04.01.22273214
ABSTRACT
Introduction. In Japan, as of December 31, 2021, more than 1.73 million laboratory-confirmed cases have been reported. However, the actual number of infections is likely to be under-ascertained due to the epidemiological characteristics such as mild and subclinical infections and limited testing availability in the early days of the pandemic. In this study, we infer the true number of infections in Japan between January 16, 2020, and December 31, 2021 , using a statistical modelling framework that combines data on reported cases and fatalities. Methods. We used reported daily COVID-19 deaths stratified into 8 distinct age-groups and age-specific infection fatality ratios (IFR) to impute the true number of infections. Estimates of IFR were informed from published studies as well seroprevalence studies conducted in Japan. To account for the uncertainty in IFR estimates, we sampled values from relevant distributions. Results. We estimated that as of December 31, 2021, 2.90 million (CrI 1.77 to 4.27 million) people had been infected in Japan, which is 1.68 times higher than the 1.73 million reported cases. Our meta-analysis confirmed that these findings were consistent with the intermittent seroprevalence studies conducted in Japan. Conclusions. We have estimated that a substantial number of COVID-19 infections in the country were unreported, particularly in adults. Our approach provides a more realistic assessment of the true underlying burden of COVID-19. The results of this study can be used as fundamental components to strengthen population health control and surveillance measures.
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Full text: Available Collection: Preprints Database: medRxiv Main subject: Severe Acute Respiratory Syndrome / COVID-19 Language: English Year: 2022 Document Type: Preprint

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Full text: Available Collection: Preprints Database: medRxiv Main subject: Severe Acute Respiratory Syndrome / COVID-19 Language: English Year: 2022 Document Type: Preprint