The cCFR, aCFR, sCFR, and HFR estimates, as per our study, have been depicted in Figure 1(a) and 1(b), for four different age groups and three Psym (Psym = 0.5, 0.75, 0.95). The overall cCFR is 2.32% (2.05–2.59), sCFR is 0.14% (0.12–0.16), HFR is 0.32% (0.27–0.36), aCFR is 1.86% (1.64–2.07), values expressed as mean (95% confidence interval). The current estimates of cCFR, aCFR, sCFR, and HFR are age as well as Psym dependent. A decrease in cCFR, aCFR, sCFR, and HFR was noticed for all Psym with the increase of age up to 75 years, while the above estimates were lowest for cases above 75 years (Figure 1). With the increase of Psym values, the cCFR, aCFR, sCFR, and HFR estimates increased gradually for all age groups (<45 years to >75 years). However, earlier authors explained that their age-specific estimates of sCFR were Psym sensitive, while susceptibility to symptomatic infection were Psym insensitive, in relation to the study of COVID–19 pandemic in China [19]. Age distribution of cCFR of COVID–19 in India showed that though highest cCFR (40%) was seen among people of 60–75 years age group, the CFR, aCFR, sCFR, and HFR were ~50 times higher among people <60 years compared to the older population above 60 years. Herein, the highest prevalence of COVID–19 infection was estimated to be 67% in India among people <45 years, which was ~34 times higher than those <75 years, following published information of cases [14].
The relative susceptibility estimates of developing symptoms and infection due to COVID–19 are represented in Figure 2(a) and 2(b) respectively. The relative susceptibility estimates of developing symptoms due to COVID–19 disease were 1.97 (0.47–3.47), 0.62 (0.15–1.09), 0.29 (0.07–0.52), 0.06 (0.02–0.10) respectively for <45 years, 45–60, 60–75, >75 years of age groups. Similar trend, for the relative susceptibility estimates of developing COVID–19 infection, were found for all Psym and age groups, with relatively higher value, compared to the relative susceptibility of developing symptoms, which decreased with the increase of age as well as Psym (Figure 2). Our estimates revealed that the RSODI was ~6 times higher than RSODI among all cases, the RSODS and RSODI being 4 and 25 respectively from asymptomatic and uninfected condition respectively, that is, prevalence of four asymptomatic people over one symptomatic person, and one infected case prevails among 25 uninfected people, respectively. In our estimate the RSODS and RSODI both increased from the baseline Psym = 0.5 to Psym = 0.95 at the top level. The RSODS values were 0.16 and 2.0 times higher among patients of <30 years and >59 years, respectively, compared to those aged 30–59 years, as has been explained by Wu et al. [19].
The CFR estimates in the Diamond Princess ship and in China were 2.6% and 1.2% as of February 2020 [20]. Figure 3 demonstrates different estimates of COVID–19 fatalities in India. The values of 14-day lag estimate of CFR were 18.07 (15.67–20.47) and outcome (deaths plus recoveries) based estimate of CFR were 16.57 (14.65–18.49). The outcome-based and 14-day lag estimates of CFR were, respectively, ~7 and ~8 times higher than cCFR estimate [2.32 (2.05–2.59)]. In China, the age adjusted CFR (ACFR) was 3.8% as of February 22, 2020, and in Italy, the ACFR ranged 4.7%–7% as of March 20, 2020 [21], both of which were lower than the ACFR value of 15% (1.58–48.42) in India, as estimated in the present study. The outcome-based CFR estimate was applied for SARS by Ghani et al [22], and that was independent of any window period providing point-estimate during the course of epidemic.
The best linear fitted CFR estimates by confirmed cases were 3.32% (R2 = 0.9988), 14-day lag estimates were 7.8% (R2 = 0.9784) and by outcome based estimates were 10.27% (R2 = 0.9806), as represented in the insets of Figure 3. Considering the 14-day delay from confirmation to death, the time-dependent average CFR estimate was two times less than the overall 14-day delay CFR estimate by regression analysis. Similar method was used by Yang et al. [17] and Oztoprak et al. [23], in estimating CFR for COVID–19 in different geographical regions. Considering the population density of 19 Indian states and union territories displaying deaths as of May 1, 2020, the population-adjusted CFR in India was 0.17%, in our study, which provide an estimate of the COVID–19 CFR in India at the population level; such CFR was estimated by employing population adjusted number of COVID–19 cases, as reported by Iype and Gulati [24].
The difference in CFR estimates were plausibly due to the presence of comorbidities (diabetes mellitus, chronic obstructive pulmonary disease, coronary artery disease, hypertension), which in Indian situation represent 78% of the COVID–19 infection [15]; furthermore, the hospitalized cases (70% of symptomatic infection), of which 15% leading into severe ICU admission, could presumably overvalue the CFR, if mild or asymptomatic cases were not detected. The incomplete testing as well as reporting of deaths, if any, were accounted by estimating CFR with a time delay from infection onset to death, which in our estimate was 11.85 days (Figure 5). Several other factors can result in CFR disparities owing to uncertainty related to case definition, right-censoring of cases, and the untraceable cases [24].
The CFR estimates can be biased upwards by underreporting of cases and downwards by failure in accounting the delay from case confirmation to death. However, this might not be true for COVID–19 pandemic situation in India so far the ICMR testing strategy comprises both symptomatic and asymptomatic cases infected with COVID–19 [13]. Moreover, the serial interval estimates (3.5 days) indicate the presence of only asymptomatic COVID–19 infection for two days (March 2, 2020, and March 3, 2020). Besides, based upon the SI values of 7.8 days, for other days prior to the commencing date of ICMR testing that was on March 19, 2020, the infected cases in India were all symptomatic in nature [25].
Figure 4 displays the Psym-variable current estimates of the major epidemiologic factors of COVID–19 pandemic in Indian context. The growth rate, serial interval, and reproduction number and average time from onset of COVID–19 infection to death, were 6.12% (5.30%–6.99%), 11.4 days (9.91–12.85), 1.03 (1.01–1.05), and 11.85 days (10.55–13.15), respectively. As per the ICMR status update on SARS-CoV–2 (COVID–19) testing [18], a total of 1,673,688 samples were tested out of which 3.76 % (62939) tested positive for COVID–19 (Figure 5) accounting for 1228 tests per one million population of India, as of May 10, 2020. As per our estimates, the average 4.84% (4.27- 5.41) COVID–19 cumulative infected cases and 3.69% (3.33- 4.06) daily infected cases against the tests were recorded, while following regression analysis the values were respectively, 3.68% (R2 = 0.9975) and 3.12% (R2 = 0.8697) (Figure 5).
According to the published data on the facility preparedness of the states/union territories governments as well as the central government on COVID–19 in India, there have been 7,740 facilities in 483 districts to support COVID–19, a total of 305,567 beds for confirmed cases, 99,492 oxygen supported beds, 34,076 ICU beds, in all the states/union territories, as of May 10, 2020 [26,27], for an infection attack rate of 465 per million as per the current estimation, following the criteria of Centers for Disease Control and Prevention [28]. Such medical resources and healthcare setup achieved average daily recovery of 19.45% (14.75- 24.15) (Figure 5) and average cumulative recovery of 12.68% (10.70- 14.66) COVID–19 patients, with overall best-fitted estimates of 37.42% (R2 = 0.9348) and 28.34% (R2 = 0.9706) respectively, as we have estimated. As per our belief and knowledge, this is the first study of its kind in reporting COVID–19 severity in Indian context during pandemic.