Estimating COVID-19 pandemic severity in Indian context

The case fatality rate (CFR) represents one of the most vital factors in demonstrating the severity of novel infectious disease, COVID-19. Various estimates of COVID-19 fatalities (based on the publicly and published information and data in Indian context of COVID-19): confirmed CFR, asymptomatic CFR, symptomatic CFR, and hospitalized CFR were 2.32% (2.05-2.59), 0.14% (0.12-0.16), 0.32% (0.27-0.36), 1.86% (1.64-2.07) respectively. The relative susceptibility of developing symptoms (RSODS) and relative susceptibility of developing infection (RSODI) of COVID-19 were ~33 times higher among people aged <45 years. The RSODS estimates 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 patients <45 years, 45-60, 60-75, >75 years. Similar trend, for RSODI were found, with relatively higher value, compared to RSODS, which decreased with the increase of age. The 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 growth rate, serial interval, 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. Among 1,673,688 samples tested, 62,939 (3.76%) were positive for COVID-19 accounting 1,228 tests per million population of India, as of May 10, 2020. The average daily recovery was 19.45% (14.75- 24.15) and average cumulative recovery was 12.68%


Introduction
In India, SARS-CoV-2, the causative agent of COVID-19, transmits with a basic reproduction number of 1.03-1.55 [1,2], and as of May 10, 2020, the country registered 2,109 people who succumbed to the disease among 62,939 confirmed cases [3]. Notably, the virus spreads very powerfully from Wuhan of Hubei province in China, the primary epicentre of COVID-19 pandemic, with basic reproduction number 2.2 [4] to the neighboring countries and beyond [5]. Alongside the reproduction number that determines the transmissibility of SARS-COV-2, CFR (case fatality rate) represents one of the most vital factors in demonstrating the severity of this novel infectious disease, critical for policy decisions on optimal healthcare facility allotment. Currently, as of May 10, 2020, based upon the total deaths as the numerator and the total confirmed cases as the denominator, also known as confirmed CFR (cCFR) of COVID-19 was 7% globally, while the highest cCFR of 14.78% was noted for the United Kingdom, which were much higher compared to that demonstrated for India (3.35%-3.11%) [6,7]. When the pandemic is still ongoing, the cCFR, is presumed to be an overvaluation of disease severity likely due to low detection of asymptomatic cases and underestimation of CFR due to the right-censoring of cases related to the time delay from symptom onset to death [8]. Also, the CFR estimates relying on the formula 'death / (recovery + death)' outpace the previous one, because of the consideration of a time delay between diagnosis and death [9,10], and thus explaining that the 14-day delay estimate of CFR is not a real one in exploring COVID-19 case fatality [11,12]. A number of studies including that we have done though demonstrated the estimation of reproduction number [1,2], there is a dearth of reliable CFR estimates of COVID-19 ongoing pandemic particularly of Indian context. In view of the above background this communication explores various estimates of fatalities of COVID-19, along with estimation of doubling time, reproduction number, and serial interval, in estimating COVID-pandemic severity in Indian context.

Methods
The data on COVID-19 in India, were retrieved electronically from publicly accessible website of the Ministry of Health and Family Welfare, Government of India [3], since March 2, 2020 up to May 10, 2020. A total of 1,673,688 tests including 62,939 infected cases were recorded as of May 10, 2020. Concerning COVID-19 pandemic, India recommended testing as per the ICMR (Indian Council of Medical Research) strategy beginning March 19, 2020, that included both symptomatic and asymptomatic case contacts (laboratory confirmed and health care workers), and hospitalized severe acute respiratory illness (SARI) patients [13]. Various public and published data were used to estimate the disease severity based on information on age distribution of infected cases [14], CFR [15], and symptomatic, asymptomatic, and hospitalized cases [16].
The estimates of cCFR (cumulative number of deaths divided by number of infected cases per day), 14-day lag estimates of CFR (cumulative number of deaths divided by number of infected cases in the past 14 days), outcome (total number of recoveries and deaths)-based CFR (cumulative number of deaths divided by number of recoveries and deaths per day). The best fitted estimates of CFR based on linear least square method were also used [17].
The sCFR was defined as the probability of dying from the infection after developing symptoms of COVID-19, while the aCFR was defined as the probability of dying from the infection but not displaying COVID-19 symptoms. The HFR was defined as the probability of dying from the infection after developing symptoms of COVID-19 and getting hospitalized. The IFR (infection fatality rate) was defined as the probability of dying from COVID-19 infection, who may or may not be symptomatic. In the present study, IFR is equivalent to CFR, because the ICMR criteria of testing included both symptomatic and asymptomatic COVID-19 cases [13]. P sym was the probability of developing symptoms after infection with COVID-19. The RSODS (relative susceptibility of developing symptoms) from asymptomatic state (assuming relative proportion of 4) was estimated as per published reports [16]. The RSODI (relative susceptibility of developing infection) from uninfected state was estimated, assuming its relative proportion of 25 among people tested for COVID-19, following ICMR criteria [18]. Overall, the cCFR, aCFR, sCFR, HFR, RSODS, and RSODI were estimated for P sym values of 0.50, 0.75, and 0.95 each for all age groups mentioned, following Wu et al. [19].

Results And Discussion
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 P sym (P sym = 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 P sym dependent. A decrease in cCFR, aCFR, sCFR, and HFR was noticed for all P sym 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 P sym 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 P sym sensitive, while susceptibility to symptomatic infection were P sym 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 P sym 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 P sym ( 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 P sym = 0.5 to P sym = 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% (R 2 = 0.9988), 14-day lag estimates were 7.8% (R 2 = 0.9784) and by outcome based estimates were 10.27% (R 2 = 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]. 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% (R 2 = 0.9975) and 3.12% (R 2 = 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% (R 2 = 0.9348) and 28.34% (R 2 = 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.

Conclusion
Accounting the symptomatic as well as asymptomatic cases in testing COVID-19 infection, the dynamic CFR estimation upon confirmed cases reflect the severity of this emerging infectious disease overtime. With a rise in effective testing through contact tracing and survey of infection prevalence as well as detecting all possible cases throughout the course of the pandemic real CFR could be estimated to evaluate the effectiveness of healthcare systems and new treatments. With the progress of the disease, more data will become available improving the understanding of COVID-19 pandemic severity.

Conflict of Interest:
We declare that there is no conflict of interest.