SARS-CoV-2 Infection Fatality Rates in India: Systematic Review, Meta-analysis and Model-based Estimation

  1. Lauren Zimmermann
  2. Subarna Bhattacharya
  3. Soumik Purkayastha
  4. Ritoban Kundu
  5. Ritwik Bhaduri
  6. Parikshit Ghosh
  7. Bhramar Mukherjee

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Abstract

Fervourous investigation and dialogue surrounding the true number of SARS-CoV-2-related deaths and implied infection fatality rates in India have been ongoing throughout the pandemic, and especially pronounced during the nation’s devastating second wave. We aim to synthesize the existing literature on the true SARS-CoV-2 excess deaths and infection fatality rates (IFR) in India through a systematic search followed by viable meta-analysis. We then provide updated epidemiological model-based estimates of the wave 1, wave 2 and combined IFRs using an extension of the Susceptible-Exposed-Infected-Removed (SEIR) model, using data from 1 April 2020 to 30 June 2021.

Methods:

Following PRISMA guidelines, the databases PubMed, Embase, Global Index Medicus, as well as BioRxiv, MedRxiv and SSRN for preprints (accessed through iSearch), were searched on 3 July 2021 (with results verified through 15 August 2021). Altogether, using a two-step approach, 4,765 initial citations were screened, resulting in 37 citations included in the narrative review and 19 studies with 41datapoints included in the quantitative synthesis. Using a random effects model with DerSimonian-Laird estimation, we meta-analysed IFR 1 , which is defined as the ratio of the total number of observed reported deaths divided by the total number of estimated infections, and IFR 2 (which accounts for death underreporting in the numerator of IFR 1 ). For the latter, we provided lower and upper bounds based on the available range of estimates of death undercounting, often arising from an excess death calculation. The primary focus is to estimate pooled nationwide estimates of IFRs with the secondary goal of estimating pooled regional and state-specific estimates for SARS-CoV-2-related IFRs in India. We also tried to stratify our empirical results across the first and second waves. In tandem, we presented updated SEIR model estimates of IFRs for waves 1, 2, and combined across the waves with observed case and death count data from 1 April 2020 to 30 June 2021.

Results:

For India, countrywide, the underreporting factors (URF) for cases (sourced from serosurveys) range from 14.3 to 29.1 in the four nationwide serosurveys; URFs for deaths (sourced from excess deaths reports) range from 4.4 to 11.9 with cumulative excess deaths ranging from 1.79 to 4.9 million (as of June 2021). Nationwide pooled IFR 1 and IFR 2 estimates for India are 0.097% (95% confidence interval [CI]: 0.067–0.140) and 0.365% (95% CI: 0.264–0.504) to 0.485% (95% CI: 0.344–0.685), respectively, again noting that IFR 2 changes as excess deaths estimates vary. Among the included studies in this meta-analysis, IFR 1 generally appears to decrease over time from the earliest study end date to the latest study end date (from 4 June 2020 to 6 July 2021, IFR 1 changed from 0.199 to 0.055%), whereas a similar trend is not as readily evident for IFR 2 due to the wide variation in excess death estimates (from 4 June 2020 to 6 July 2021, IFR 2 ranged from (0.290–1.316) to (0.241–0.651)%). Nationwide SEIR model-based combined estimates for IFR 1 and IFR 2 are 0.101% (95% CI: 0.097–0.116) and 0.367% (95% CI: 0.358–0.383), respectively, which largely reconcile with the empirical findings and concur with the lower end of the excess death estimates. An advantage of such epidemiological models is the ability to produce daily estimates with updated data, with the disadvantage being that these estimates are subject to numerous assumptions, arduousness of validation and not directly using the available excess death data. Whether one uses empirical data or model-based estimation, it is evident that IFR 2 is at least 3.6 times more than IFR 1 .

Conclusion:

When incorporating case and death underreporting, the meta-analysed cumulative infection fatality rate in India varied from 0.36 to 0.48%, with a case underreporting factor ranging from 25 to 30 and a death underreporting factor ranging from 4 to 12. This implies, by 30 June 2021, India may have seen nearly 900 million infections and 1.7–4.9 million deaths when the reported numbers stood at 30.4 million cases and 412 thousand deaths (Coronavirus in India) with an observed case fatality rate (CFR) of 1.35%. We reiterate the need for timely and disaggregated infection and fatality data to examine the burden of the virus by age and other demographics. Large degrees of nationwide and state-specific death undercounting reinforce the call to improve death reporting within India. JEL Classifications: I15, I18

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  1. Version published to 10.1177/23210222211054324
  2. ScreenIT

    SciScore for 10.1101/2021.09.08.21263296: (What is this?)

    Please note, not all rigor criteria are appropriate for all manuscripts.

    Table 1: Rigor

    NIH rigor criteria are not applicable to paper type.

    Table 2: Resources

    Antibodies
    SentencesResources
    Seroprevalence survey (i.e., serosurvey) in the context of SARS-CoV-2 are large-scale studies aimed at estimating the true prevalence of SARS-CoV-2 (i.e., the cumulative percent infected over a period of time) within the target population utilizing serology testing for IgG or IgM antibody presence (18) scaled to the level of a geographic location (e.g., nationwide, statewide, citywide, or districtwide), a community, or a smaller group.
    IgM
    suggested: None
    IFR[text word] OR infection*[text word] OR CFR[text word] OR case*[text word] OR transmission*[text word] OR mortalit*[text word] OR mortality[mesh] OR fatalit*[text word] OR lethalit*[text word] OR death*[text word] OR burden[text word] OR underreporting[text word] OR “under-reporting”[text word] OR seroprevalence[text word] OR serosurvey[text word] OR serology[text word] OR serology[mesh] OR seroconversion[text word] OR seroconversion[mesh] OR “serosurveillance”[text word] OR Seroepidemiologic studies[mesh] OR seroepid*[text word] OR seropositiv*[text word] OR antibod*[text word] OR antibodies[mesh] OR surveillance[text word] OR SIR[text word] OR SEIR[text word] OR “susceptible-exposed-infected-removed”[text word] OR “susceptible-infected-removed”[text word] (1 AND 2 AND 3) Embase (Elsevier) Date searched: 7/3/2021 Number of results: 1,119 Date filter: 2020 to 2021 Other filters applied: Embase only and not Medline (as Medline is included in PubMed) 1. covid-19:ti,ab,kw OR COVID19:ti,ab,kw OR SARS-CoV-2:ti,ab,kw OR SARS-CoV2:ti,ab,kw OR “severe acute respiratory syndrome coronavirus 2”:ti,ab,kw OR 2019-nCoV:ti,ab,kw OR 2019nCoV:ti,ab,kw OR coronavirus:ti,ab,kw OR ‘Coronavirinae’/exp OR ‘coronavirus disease 2019’/exp 2. india:ti,ab,kw OR ‘india’/exp OR indian:ti,ab,kw OR pakistan:ti,ab,kw OR pakistani:ti,ab,kw OR ‘pakistan’/exp OR bangladesh:ti,ab,kw OR bangladeshi:ti,ab,kw OR ‘bangladesh’/de OR nepal:ti,ab,kw OR ‘nepal’/de OR “sri lanka”:ti,ab,kw OR “sri lankan”:ti
    covid-19:ti
    suggested: None
    Software and Algorithms
    SentencesResources
    To identify relevant papers, we searched four databases: PubMed, Embase, Global Index Medicus, and isearch for preprints (encompassing bioRxiv, medRxiv, and SSRN).
    Embase
    suggested: (EMBASE, RRID:SCR_001650)
    bioRxiv
    suggested: (bioRxiv, RRID:SCR_003933)
    IFR[text word] OR infection*[text word] OR CFR[text word] OR case*[text word] OR transmission*[text word] OR mortalit*[text word] OR mortality[mesh] OR fatalit*[text word] OR lethalit*[text word] OR death*[text word] OR burden[text word] OR underreporting[text word] OR “under-reporting”[text word] OR seroprevalence[text word] OR serosurvey[text word] OR serology[text word] OR serology[mesh] OR seroconversion[text word] OR seroconversion[mesh] OR “serosurveillance”[text word] OR Seroepidemiologic studies[mesh] OR seroepid*[text word] OR seropositiv*[text word] OR antibod*[text word] OR antibodies[mesh] OR surveillance[text word] OR SIR[text word] OR SEIR[text word] OR “susceptible-exposed-infected-removed”[text word] OR “susceptible-infected-removed”[text word] (1 AND 2 AND 3) Embase (Elsevier) Date searched: 7/3/2021 Number of results: 1,119 Date filter: 2020 to 2021 Other filters applied: Embase only and not Medline (as Medline is included in PubMed) 1. covid-19:ti,ab,kw OR COVID19:ti,ab,kw OR SARS-CoV-2:ti,ab,kw OR SARS-CoV2:ti,ab,kw OR “severe acute respiratory syndrome coronavirus 2”:ti,ab,kw OR 2019-nCoV:ti,ab,kw OR 2019nCoV:ti,ab,kw OR coronavirus:ti,ab,kw OR ‘Coronavirinae’/exp OR ‘coronavirus disease 2019’/exp 2. india:ti,ab,kw OR ‘india’/exp OR indian:ti,ab,kw OR pakistan:ti,ab,kw OR pakistani:ti,ab,kw OR ‘pakistan’/exp OR bangladesh:ti,ab,kw OR bangladeshi:ti,ab,kw OR ‘bangladesh’/de OR nepal:ti,ab,kw OR ‘nepal’/de OR “sri lanka”:ti,ab,kw OR “sri lankan”:ti
    Medline
    suggested: (MEDLINE, RRID:SCR_002185)
    PubMed
    suggested: (PubMed, RRID:SCR_004846)

    Results from OddPub: Thank you for sharing your code and data.

    Results from LimitationRecognizer: We detected the following sentences addressing limitations in the study:
    This leads us to the limitations of the meta-analysis presented herein. Limitations of Meta-analysis: We were not able to incorporate Bangladesh, Nepal, Pakistan, nor Sri Lanka in the meta-analysis presented in this paper as no eligible studies were available for these countries (aside from Pakistan, which had 2 included seroprevalence surveys), as previously discussed in the Results section. There were insufficient age-as well as sex-disaggregated IFRs for India (and for the neighboring countries) to examine heterogeneity in IFR estimates by either demographic. Furthermore, although age and sex-disaggregated seroprevalence estimates were extracted from the included studies, disaggregated deaths and cases by these demographics are not available for India nor for its states, cities, nor districts, at the time of this review. Additionally, there were insufficient data on excess deaths for select states (e.g., Jammu and Kashmir and Puducherry) that precluded our ability to compute IFR2 for studies encompassed in these states and, as such, we were unable to include these studies in the meta-analysis for the regional IFR2. Additionally, we caution that several of the included studies in the quantitative synthesis are in the process of being peer-reviewed, and so multiple underlying datapoints have not yet been verified. Lastly, we note that global studies that examine India along with multiple other countries (as part of a country level global analysis) but do not explicitly refer...

    Results from TrialIdentifier: No clinical trial numbers were referenced.

    Results from Barzooka: We did not find any issues relating to the usage of bar graphs.

    Results from JetFighter: We did not find any issues relating to colormaps.

    Results from rtransparent:
    • Thank you for including a conflict of interest statement. Authors are encouraged to include this statement when submitting to a journal.
    • Thank you for including a funding statement. Authors are encouraged to include this statement when submitting to a journal.
    • No protocol registration statement was detected.

    Results from scite Reference Check: We found no unreliable references.

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  3. Version published to 10.1101/2021.09.08.21263296v1 on medRxiv