Introduction

Main text

In late August 2020, India was predicted to surpass the United States in terms of reported case counts from SARS-CoV-2 infections. To the surprise of many modelers the curve turned corner in late September with the highest number (97,894) of daily new cases reported on 16 September 2020 [1]. After a steady decline for nearly five months, the curve started rising again, growing into an astronomic second wave. The highest number (414,280) of daily new cases in wave 2 was reported on May 6, 2021. As of May 15, 2021, India has reported 24.7 million cases, the second highest in the world, and nearly 270 thousand deaths, the third highest in the world. In this brief report, we reconcile estimates of the infection fatality rate (IFR) inferred from seroprevalence studies with epidemiologic model-based estimates that account for underreporting of infections and deaths in India for wave 1. We then proceed to compute, compare and combine wave 1 with wave 2 IFR estimates.

Methods

Synthesizing evidence from seroprevalence studies

We review available seroprevalence results that vary across states and specifically across rural versus urban areas. Whereas in many major metros and slum areas the seroprevalences were reported to be more than 50%, in rural areas there is a wide variation (Table 1). The latest national serosurvey (from 17 December 2020 to 8 January 2021) reports 21.4% of all Indians above age 18 have antibodies present that indicate past SARS-CoV-2 infection [2]. Since approximately 59% [3] of India’s 1.36 billion citizens are above age 18 and 10.45 million infections were reported as of 8 January 2021, this points to approximately 172.47 million infections, with an implied underreporting factor of 16.5 (172.47/10.45). In other words, only 6% of India’s COVID-19 infections are reported, while 94% remained undetected or unreported. We use this estimated number of infections to calculate the IFR. Regional studies based on crematorium data and counting obituaries in India have suggested an underreporting factor in the range of 2 to 5 for COVID-deaths; this is at best ad hoc and anecdotal in nature and no rigorous quantification of missing death numbers is currently available [4].

Table 1 Summary of results from various serological surveys conducted in India during 2020–21
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Model-based estimates

Using a compartmental epidemiologic model (as explained in the Supplementary Methods) with a compartment for unascertained cases and deaths after accounting for the false negative rates of RT-PCR and rapid antigen tests used in India [5] we estimate the national and state-level IFR in India by inferring underreporting factors for cases and deaths. We assume that the estimated total infections (deaths) are comprised of reported and unreported infections (deaths). The model divides the population into ten disjoint compartments: S (Susceptible), E (Exposed), T (Tested), U (Untested), P (Tested positive), F (Tested False Negative), RR (Reported Recovered), RU (Unreported Recovered), DR (Reported Deaths) and DU (Unreported Deaths), as described in Additional file 1: Figure S1. A set of nine differential equations govern the transmission dynamics, which are approximated by means of discrete recurrence relations. For any compartment \(X\), the instantaneous rate of change at time \(t\) (given by \(\frac{{dX}}{{dt}}\)) is approximated by the difference of counts in that specific compartment on the \(\left( {t + 1} \right)\) th day and the \(\left( t \right)\) th day, i.e., say \(X\left( {t + 1} \right) - X\left( t \right)\). Parameters are estimated using Bayesian techniques by generating samples from the posterior distribution using a Metropolis–Hastings algorithm with Gaussian proposal density, with 95% credible intervals (CrI) to quantify uncertainty of the estimates. Additional file 1: Table T4 presents an overview of the parameter descriptions and settings for this model.

Comparing and combining waves 1 and 2

Due to the stress on the healthcare and reporting infrastructure, the fatality and underreporting processes were very different across the two waves. Thus, we consider two separate phases of the pandemic, with wave 1 from April 1, 2020–January 31, 2021 and wave 2 starting on February 1, 2021. This definition is artificial and is guided by the fact that the national effective reproduction number (Reff) crossed unity for the first time in 2021 on February 14 and we allow a two-week incubation period before that date. Using daily time series of case, death and recovery counts we compare fatality rates and underreporting factors associated with the two time periods using the compartmental models. Further, using observed data from the two waves and the model-based underreporting factor estimates, we compute cumulative case and death counts for the total duration of waves 1 and 2. We multiply the wave-specific cumulative counts with relevant underreporting factors and sum over both waves to get combined counts of cases and deaths. The estimated numbers of cumulative deaths and infections provide us with a combined IFR estimate for India as of May 15.

Results

IFR estimates for wave 1 using seroprevalence surveys

The observed case fatality rate (CFR) in India is low. With 154,428 deaths and 10.76 million cases reported as of January 31, 2021 the estimated CFR for wave 1 is 1.435% (95% confidence interval 1.428–1.442%) [1]. The estimated number of infections from the January seroprevalence survey imply an approximate infection fatality rate of 0.09% (i.e. 154,428/172.47 M). The anecdotal underreporting factor for deaths (in the range of 2–5) implies an ad hoc estimate of IFR in the range of 0.19–0.45%.

Estimates from epidemiological models

For wave 1 our estimate for the national IFR1 (observed cumulative deaths/estimated cumulative total infections) is 0.129% (95% CrI 0.125–0.134%) and IFR2 (estimated total cumulative deaths/estimated total cumulative infections) is 0.461% (95% CrI 0.455–0.468%) with an underreporting factor for cases estimated at 11.11 (95% CrI 10.71–11.47) and for deaths at 3.56 (95% CrI 3.48–3.64). These model-based estimates in wave 1 are largely consistent with the estimates from the latest and third nationwide seroprevalence study.

In wave 2, using the same model we see a stark contrast with wave 1, with case and death underreporting factor estimates escalate to 26.73 (95% CrI 24.26–28.81) and 5.77 (95% CrI 5.34–6.15) respectively, leading to IFR1 estimate of 0.032% (95% CrI 0.029–0.035%) and IFR2 estimate of 0.183% (95% CrI 0.18–0.186%). This pattern is consistent with wave 2 CFR being estimated at 0.845% (95% CrI 0.840–0.849%), 59% of wave 1 estimate.

Figure 1 shows underreporting factors and estimated infections and deaths in waves 1 and 2 for India while Fig. 2 highlights state-level variations in IFR1, IFR2, CFR for waves 1 and 2 for 20 states in India with large case/death counts.

Fig. 1
figure 1

Comparison of observed and estimated case and death counts and associated underreporting factors from waves 1, 2 and both waves combined

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Fig. 2
figure 2

Forest plot of wave 1 and wave 2 infection fatality rates (IFR) and case fatality rates (CFR) associated with SARS-CoV-2 in various states in India. IFR1 is based on reported deaths whereas IFR2 estimates and includes the unreported deaths

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Combining waves 1 and 2

The composite CFR as of May 15 stands at 1.1%. The estimate for total (reported + unreported) cumulative case count for waves 1 and 2 combined is 491.73 (95% CrI 453.03–524.56) million, while the estimated number of total (reported + unreported) deaths is 1216.35 (95% CrI 1154.21–1272.70) thousand. This leads to a combined IFR1 estimate of 0.06% and IFR2 estimate of 0.24%. Detailed numerical estimates of underreporting factors across states for waves 1 and 2 are presented in Additional file 1: Tables T1, T2 and T3 and Additional file 1: Figures S2 and S3.

Discussion

Despite accounting for underreported deaths, the large number of asymptomatic/undetected infections (more than 90% by any calculation) indicate a lower IFR in India in comparison with other Western countries. A meta-analysis across the world places the pooled mean of IFRs at 0.68% (95% CI: 0.53–0.82%) [6], while another meta-analysis places the median at 0.27% [7] (with a range of 0–1.63%). Seroprevalence surveys and epidemiologic models qualitatively agree on the estimated IFR for India for wave 1. Up to date serosurvey and excess death/mortality data are needed to validate wave 2 and combined estimates. The estimated number of total infections as of May 15 suggests roughly 36% of Indians have an active or past infection, a number that will need to be verified with synchronous sero-surveys.

The current reduction in fatality rates in wave 2 that we notice could be primarily due to two reasons, one is that we do not have the same length of follow-up period and complete data on the decay phase of wave 2 curve. The second could be the different age composition of the infected populations in the two waves; it has been reported that the younger population got infected in larger numbers in wave 2 and they have lower risk of COVID-19 mortality. A fraction of the older population (aged 65 + years) also got vaccinated during wave 2. However, this hypothesis about reduced fatality rates in wave 2 cannot be verified without more granular, age-sex stratified nationwide time-series data on case and death counts, which is currently unavailable.

Limitations

We do not have a rigorous way to validate the extent of underreporting of deaths. An excess death calculation based on historical mortality data is infeasible at this point due to absence of all-cause-mortality data in the last three years from India. India has a very young population with only 6.4% in age group 65 + (compared to the US where this proportion is 16.5%) so a comparison of overall IFR between India and say the US is not fair, and only age-specific IFRs should be calculated and compared when more data become available. We do recognize that wave 2 information is appreciably incomplete, and the estimates will change as we have more complete information on deaths. For example, while our wave 2 analysis period ended on May 15, the highest daily number of deaths (4529 daily new deaths) were reported shortly after on May 18. Thus, our analysis presents an updated but incomplete picture of wave 2.