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Estimating the wave 1 and wave 2 infection fatality rates from SARS-CoV-2 in India
OBJECTIVE: There has been much discussion and debate around the underreporting of COVID-19 infections and deaths in India. In this short report we first estimate the underreporting factor for infections from publicly available data released by the Indian Council of Medical Research on reported numbe...
Autores principales: | , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
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BioMed Central
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8264482/ https://www.ncbi.nlm.nih.gov/pubmed/34238344 http://dx.doi.org/10.1186/s13104-021-05652-2 |
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author | Purkayastha, Soumik Kundu, Ritoban Bhaduri, Ritwik Barker, Daniel Kleinsasser, Michael Ray, Debashree Mukherjee, Bhramar |
author_facet | Purkayastha, Soumik Kundu, Ritoban Bhaduri, Ritwik Barker, Daniel Kleinsasser, Michael Ray, Debashree Mukherjee, Bhramar |
author_sort | Purkayastha, Soumik |
collection | PubMed |
description | OBJECTIVE: There has been much discussion and debate around the underreporting of COVID-19 infections and deaths in India. In this short report we first estimate the underreporting factor for infections from publicly available data released by the Indian Council of Medical Research on reported number of cases and national seroprevalence surveys. We then use a compartmental epidemiologic model to estimate the undetected number of infections and deaths, yielding estimates of the corresponding underreporting factors. We compare the serosurvey based ad hoc estimate of the infection fatality rate (IFR) with the model-based estimate. Since the first and second waves in India are intrinsically different in nature, we carry out this exercise in two periods: the first wave (April 1, 2020–January 31, 2021) and part of the second wave (February 1, 2021–May 15, 2021). The latest national seroprevalence estimate is from January 2021, and thus only relevant to our wave 1 calculations. RESULTS: Both wave 1 and wave 2 estimates qualitatively show that there is a large degree of “covert infections” in India, with model-based estimated underreporting factor for infections as 11.11 (95% credible interval (CrI) 10.71–11.47) and for deaths as 3.56 (95% CrI 3.48–3.64) for wave 1. For wave 2, underreporting factor for infections escalate to 26.77 (95% CrI 24.26–28.81) and to 5.77 (95% CrI 5.34–6.15) for deaths. If we rely on only reported deaths, the IFR estimate is 0.13% for wave 1 and 0.03% for part of wave 2. Taking underreporting of deaths into account, the IFR estimate is 0.46% for wave 1 and 0.18% for wave 2 (till May 15). Combining waves 1 and 2, as of May 15, while India reported a total of nearly 25 million cases and 270 thousand deaths, the estimated number of infections and deaths stand at 491 million (36% of the population) and 1.21 million respectively, yielding an estimated (combined) infection fatality rate of 0.25%. There is considerable variation in these estimates across Indian states. Up to date seroprevalence studies and mortality data are needed to validate these model-based estimates. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s13104-021-05652-2. |
format | Online Article Text |
id | pubmed-8264482 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-82644822021-07-08 Estimating the wave 1 and wave 2 infection fatality rates from SARS-CoV-2 in India Purkayastha, Soumik Kundu, Ritoban Bhaduri, Ritwik Barker, Daniel Kleinsasser, Michael Ray, Debashree Mukherjee, Bhramar BMC Res Notes Research Note OBJECTIVE: There has been much discussion and debate around the underreporting of COVID-19 infections and deaths in India. In this short report we first estimate the underreporting factor for infections from publicly available data released by the Indian Council of Medical Research on reported number of cases and national seroprevalence surveys. We then use a compartmental epidemiologic model to estimate the undetected number of infections and deaths, yielding estimates of the corresponding underreporting factors. We compare the serosurvey based ad hoc estimate of the infection fatality rate (IFR) with the model-based estimate. Since the first and second waves in India are intrinsically different in nature, we carry out this exercise in two periods: the first wave (April 1, 2020–January 31, 2021) and part of the second wave (February 1, 2021–May 15, 2021). The latest national seroprevalence estimate is from January 2021, and thus only relevant to our wave 1 calculations. RESULTS: Both wave 1 and wave 2 estimates qualitatively show that there is a large degree of “covert infections” in India, with model-based estimated underreporting factor for infections as 11.11 (95% credible interval (CrI) 10.71–11.47) and for deaths as 3.56 (95% CrI 3.48–3.64) for wave 1. For wave 2, underreporting factor for infections escalate to 26.77 (95% CrI 24.26–28.81) and to 5.77 (95% CrI 5.34–6.15) for deaths. If we rely on only reported deaths, the IFR estimate is 0.13% for wave 1 and 0.03% for part of wave 2. Taking underreporting of deaths into account, the IFR estimate is 0.46% for wave 1 and 0.18% for wave 2 (till May 15). Combining waves 1 and 2, as of May 15, while India reported a total of nearly 25 million cases and 270 thousand deaths, the estimated number of infections and deaths stand at 491 million (36% of the population) and 1.21 million respectively, yielding an estimated (combined) infection fatality rate of 0.25%. There is considerable variation in these estimates across Indian states. Up to date seroprevalence studies and mortality data are needed to validate these model-based estimates. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s13104-021-05652-2. BioMed Central 2021-07-08 /pmc/articles/PMC8264482/ /pubmed/34238344 http://dx.doi.org/10.1186/s13104-021-05652-2 Text en © The Author(s) 2021 https://creativecommons.org/licenses/by/4.0/Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/ (https://creativecommons.org/publicdomain/zero/1.0/) ) applies to the data made available in this article, unless otherwise stated in a credit line to the data. |
spellingShingle | Research Note Purkayastha, Soumik Kundu, Ritoban Bhaduri, Ritwik Barker, Daniel Kleinsasser, Michael Ray, Debashree Mukherjee, Bhramar Estimating the wave 1 and wave 2 infection fatality rates from SARS-CoV-2 in India |
title | Estimating the wave 1 and wave 2 infection fatality rates from SARS-CoV-2 in India |
title_full | Estimating the wave 1 and wave 2 infection fatality rates from SARS-CoV-2 in India |
title_fullStr | Estimating the wave 1 and wave 2 infection fatality rates from SARS-CoV-2 in India |
title_full_unstemmed | Estimating the wave 1 and wave 2 infection fatality rates from SARS-CoV-2 in India |
title_short | Estimating the wave 1 and wave 2 infection fatality rates from SARS-CoV-2 in India |
title_sort | estimating the wave 1 and wave 2 infection fatality rates from sars-cov-2 in india |
topic | Research Note |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8264482/ https://www.ncbi.nlm.nih.gov/pubmed/34238344 http://dx.doi.org/10.1186/s13104-021-05652-2 |
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