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A demographic scaling model for estimating the total number of COVID-19 infections

BACKGROUND: Understanding how widely COVID-19 has spread is critical information for monitoring the pandemic. The actual number of infections potentially exceeds the number of confirmed cases. DEVELOPMENT: We develop a demographic scaling model to estimate COVID-19 infections, based on minimal data...

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Autores principales: Bohk-Ewald, Christina, Dudel, Christian, Myrskylä, Mikko
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Oxford University Press 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7799106/
https://www.ncbi.nlm.nih.gov/pubmed/33349859
http://dx.doi.org/10.1093/ije/dyaa198
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author Bohk-Ewald, Christina
Dudel, Christian
Myrskylä, Mikko
author_facet Bohk-Ewald, Christina
Dudel, Christian
Myrskylä, Mikko
author_sort Bohk-Ewald, Christina
collection PubMed
description BACKGROUND: Understanding how widely COVID-19 has spread is critical information for monitoring the pandemic. The actual number of infections potentially exceeds the number of confirmed cases. DEVELOPMENT: We develop a demographic scaling model to estimate COVID-19 infections, based on minimal data requirements: COVID-19-related deaths, infection fatality rates (IFRs), and life tables. As many countries lack IFR estimates, we scale them from a reference country based on remaining lifetime to better match the context in a target population with respect to age structure, health conditions and medical services. We introduce formulas to account for bias in input data and provide a heuristic to assess whether local seroprevalence estimates are representative for the total population. APPLICATION: Across 10 countries with most reported COVID-19 deaths as of 23 July 2020, the number of infections is estimated to be three [95% prediction interval: 2–8] times the number of confirmed cases. Cross-country variation is high. The estimated number of infections is 5.3 million for the USA, 1.8 million for the UK, 1.4 million for France, and 0.4 million for Peru, or more than one, six, seven and more than one times the number of confirmed cases, respectively. Our central prevalence estimates for entire countries are markedly lower than most others based on local seroprevalence studies. CONCLUSIONS: The national infection estimates indicate that the pandemic is far more widespread than the numbers of confirmed cases suggest. Some local seroprevalence estimates largely deviate from their corresponding national mean and are unlikely to be representative for the total population.
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spelling pubmed-77991062021-01-25 A demographic scaling model for estimating the total number of COVID-19 infections Bohk-Ewald, Christina Dudel, Christian Myrskylä, Mikko Int J Epidemiol Covid-19 BACKGROUND: Understanding how widely COVID-19 has spread is critical information for monitoring the pandemic. The actual number of infections potentially exceeds the number of confirmed cases. DEVELOPMENT: We develop a demographic scaling model to estimate COVID-19 infections, based on minimal data requirements: COVID-19-related deaths, infection fatality rates (IFRs), and life tables. As many countries lack IFR estimates, we scale them from a reference country based on remaining lifetime to better match the context in a target population with respect to age structure, health conditions and medical services. We introduce formulas to account for bias in input data and provide a heuristic to assess whether local seroprevalence estimates are representative for the total population. APPLICATION: Across 10 countries with most reported COVID-19 deaths as of 23 July 2020, the number of infections is estimated to be three [95% prediction interval: 2–8] times the number of confirmed cases. Cross-country variation is high. The estimated number of infections is 5.3 million for the USA, 1.8 million for the UK, 1.4 million for France, and 0.4 million for Peru, or more than one, six, seven and more than one times the number of confirmed cases, respectively. Our central prevalence estimates for entire countries are markedly lower than most others based on local seroprevalence studies. CONCLUSIONS: The national infection estimates indicate that the pandemic is far more widespread than the numbers of confirmed cases suggest. Some local seroprevalence estimates largely deviate from their corresponding national mean and are unlikely to be representative for the total population. Oxford University Press 2020-12-08 /pmc/articles/PMC7799106/ /pubmed/33349859 http://dx.doi.org/10.1093/ije/dyaa198 Text en © The Author(s) 2020. Published by Oxford University Press on behalf of the International Epidemiological Association. http://creativecommons.org/licenses/by/4.0/ This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Covid-19
Bohk-Ewald, Christina
Dudel, Christian
Myrskylä, Mikko
A demographic scaling model for estimating the total number of COVID-19 infections
title A demographic scaling model for estimating the total number of COVID-19 infections
title_full A demographic scaling model for estimating the total number of COVID-19 infections
title_fullStr A demographic scaling model for estimating the total number of COVID-19 infections
title_full_unstemmed A demographic scaling model for estimating the total number of COVID-19 infections
title_short A demographic scaling model for estimating the total number of COVID-19 infections
title_sort demographic scaling model for estimating the total number of covid-19 infections
topic Covid-19
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7799106/
https://www.ncbi.nlm.nih.gov/pubmed/33349859
http://dx.doi.org/10.1093/ije/dyaa198
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