SARS-CoV-2 infection fatality rates in India: systematic review, meta-analysis and model-based estimation

Abstract

Introduction Fervorous 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 nations 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 April 1, 2020 to June 30, 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 July 3, 2021 (with results verified through August 15, 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 41 datapoints included in the quantitative synthesis. Using a random effects model with DerSimonian-Laird estimation, we meta-analyze IFR1 which is defined as the ratio of the total number of observed reported deaths divided by the total number of estimated infections and IFR2 (which accounts for death underreporting in the numerator of IFR1). For the latter, we provide 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 try to stratify our empirical results across the first and the second wave. In tandem, we present updated SEIR model estimates of IFRs for waves 1, 2, and combined across the waves with observed case and death count data from April 1, 2020 to June 30, 2021. Results For India countrywide, underreporting factors (URF) for cases (sourced from serosurveys) range from 14.3-29.1 in the four nationwide serosurveys; URFs for deaths (sourced from excess deaths reports) range from 4.4-11.9 with cumulative excess deaths ranging from 1.79-4.9 million (as of June 2021). Nationwide pooled IFR1 and IFR2 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 IFR2 changes as excess deaths estimates vary. Among the included studies in this meta-analysis, the IFR1 generally appear to decrease over time from the earliest study end date to the latest study end date (from 4 June 2020 to 6 July 2021, IFR1 changed from 0.199 to 0.055%), whereas a similar trend is not as readily evident for IFR2 due to the wide variation in excess death estimates (from 4 June 2020 to 6 July 2021, IFR2 ranged from (0.290-1.316) to (0.241-0.651) %). Nationwide SEIR model-based combined estimates for IFR1 and IFR2 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 disadvantages 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 IFR2 is at least 3.6 times more than IFR1. Conclusion When incorporating case and death underreporting, the meta-analyzed cumulative infection fatality rate in India varies from 0.36%-0.48%, with a case underreporting factor ranging from 25-30 and a death underreporting factor ranging from 4-12. This implies, by June 30, 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 (covid19india.org) 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.

Extending the susceptible‐exposed‐infected‐removed (SEIR) model to handle the false negative rate and symptom‐based administration of COVID‐19 diagnostic tests: SEIR‐fansy

“…Section 3.3 contains the predicted proportion of unreported counts which is substantial for a country like India. 35 , 36 This helps us in understanding the true extent of the disease beyond the reported figures which is important from a policy making perspective. RT‐PCR tests are well known to have a substantial false negative rate.…”

“…We also model the undetected and false negative individuals as two new compartments compared to the standard SEIR model which enables us to estimate the number of active/cumulative undetected cases/deaths at any given time point more accurately. Section 3.3 contains the predicted proportion of unreported counts which is substantial for a country like India 34,35 . This helps us in understanding the true extent of the disease beyond the reported figures which is important from a policy making perspective.…”

Meta-analysis of nationwide SARS-CoV-2 infection fatality rates in India

“…In brief, we describe the systematic review framework that has previously been detailed in full with the complete search strategy [1]. Adhering to PRISMA guidelines (Table A in S1 Text includes the PRISMA checklist), the databases PubMed, Embase, Global Index Medicus, as well as BioRxiv, MedRxiv, and SSRN for preprints (accessed through iSearch), were searched on July 3, 2021 and results were updated through May 26, 2022.…”

“…The second wave of SARS-CoV-2 in the 2 nd most populous country in the world, India, registered 414 thousand daily cases and 4.5 thousand daily deaths at its peak in May of 2021 [2], and led to a collapse of healthcare infrastructure [3]. Multiple studies indicate that the true number of infections and deaths are orders of magnitude larger [1,4,5]. Considerable effort has been devoted towards investigating the true number of SARS-CoV-2 attributed deaths and inferred infection fatality rates (IFR) in India.…”

Lessons from SARS-CoV-2 in India: A data-driven framework for pandemic resilience

“…A massive humanitarian catastrophe unfolded that was termed as an "unprecedented public health crisis" (20). Health care infrastructure collapsed under surges in hospitalizations (21), while crematoriums were overflowing (22) with evidence suggesting that the actual death toll far exceeded official numbers (23)(24)(25). The lack of timely and stringent preventive PHI measures and the role of emerging variants in a largely unvaccinated population defined the conversations around India's second wave (26,27).…”