Evidence map›Paper›PMID 34238344›Full record

ArticleBMC research notes2021

Estimating the wave 1 and wave 2 infection fatality rates from SARS-CoV-2 in India.

Soumik Purkayastha, Ritoban Kundu, Ritwik Bhaduri, Daniel Barker, Michael Kleinsasser, Debashree Ray, Bhramar Mukherjee

Abstract read
In one paragraph

Article in BMC research notes, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers.

0numbers the graph read from it
0cells of the map it votes in
18citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

18 citing papers in PubMed.

  1. Review
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  8. Death as a metric for outcome: Dusk between day and night.Journal of the Association of Medical Microbiology and Infectious Disease Canada = Journal officiel de l'Association pour la microbiologie medicale et l'infectiologie Canada · 2022
    Article
  9. A Statistical Model of COVID-19 Infection Incidence in the Southern Indian State of Tamil Nadu.International journal of environmental research and public health · 2022
    Article
  10. Article
  11. Article
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  13. Community seroprevalence and risk factors for SARS-CoV-2 infection in different subpopulations in Vellore, India, and their implications for future prevention.International journal of infectious diseases : IJID : official publication of the International Society for Infectious Diseases · 2022
    Article
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

7 authors.

Soumik PurkayasthaDepartment of Biostatistics, University of Michigan, Ann Arbor, MI, 48109, USA.
Ritoban KunduIndian Statistical Institute, Kolkata, West Bengal, 700108, India.
Ritwik BhaduriIndian Statistical Institute, Kolkata, West Bengal, 700108, India.
Daniel BarkerDepartment of Biostatistics, University of Michigan, Ann Arbor, MI, 48109, USA.
Michael KleinsasserDepartment of Biostatistics, University of Michigan, Ann Arbor, MI, 48109, USA.
Debashree RayDepartment of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, 21205, USA.
Bhramar MukherjeeDepartment of Biostatistics, University of Michigan, Ann Arbor, MI, 48109, USA. bhramar@umich.edu.ORCID http://orcid.org/0000-0003-0118-4561

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

objectiveThere 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.

resultsBoth 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.

Indexed as

Biomedical ResearchCOVID-19HumansIndiaSARS-CoV-2Seroepidemiologic StudiesCase fatality rateExcess deathsFalse negative ratesIndiaRT-PCR testSEIR modelUnderreporting

Identifiers

PMID34238344
PMCPMC8264482

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.