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COVID epidemiology: Is India special?

In Contagion, the 2011 Hollywood blockbuster, a deadly bat-derived virus spreads rapidly through respiratory droplets. Within a decade, a new virus called SARS-CoV2 turned fiction into fact. Since its emergence in Wuhan, China in December 2019, it has been detected in 215 countries with 33.8 million confirmed cases. The disease it causes, Covid-19, has already killed over a million globally.

The first case in India was detected on January 30. Even as the outbreak expanded quickly in Europe and USA, it picked up slowly in India due to stringent lockdowns imposed since March 25. But now there are about 6.2 million confirmed cases in India, second only to the USA at 7.4 million cases. Given that India is now adding about 83,000 cases daily compared to 42,800 for the USA, it may have most Covid-19 cases within a few weeks.

Reasonable estimates, including a seroprevalence study released by ICMR on September 29, suggest that about 100 million people may have already been infected. But the associated mortality remains low, with 97,500 confirmed deaths and a case fatality rate (CFR) of about 1.6%, although both deaths and cases are likely to be severely underestimated. The mortality figures for India mirror similar numbers across other parts of South Asia, which is home to about 25% of the world’s population and has 20% of global COVID-19 cases, but only 10% of mortality.

Was India’s country-wide lockdown responsible for the low mortality? Or could some other, yet-unknown, biological factor be at play? Do younger populations make the difference, since mortality from Covid-19 increases sharply with age? Addressing these questions requires quality data from India, which is currently lacking. Our understanding of Covid-19 comes from the experience of high-income countries, even as increasing numbers of cases come from low and middle countries (LMICs).

Different countries show different rates and modes of transmission, due to factors such as varying population density, differential adherence to public health measures, variations in testing and contact tracing, and the under-reporting of mortality. LMIC populations are typically younger, and thus at lower risk of severe infection and mortality. However, the presence of poorer healthcare infrastructure and nutrition, pre-existing conditions and prior exposure to other infectious diseases make the relationship between pandemic control and health policy complex and unpredictable.

Ramanan Laxminarayan and colleagues, reporting today in the journal
Science, present the largest study so far on the pandemic in India. It uses data from surveillance and contact tracing on 575,071 individuals exposed to 84,965 confirmed cases in two southern Indian states—Tamil Nadu and Andhra Pradesh—during the first few months of initial case detection. Together, these states account for about 10% of India’s population, 20.5% of its COVID-19 cases and 15.6% of its mortality. These states have relatively better public healthcare systems and levels of governance and initiated rigorous disease surveillance and contact tracing early in the pandemic.

Epidemiological studies worldwide show the importance of “superspreading” events, in which a small number of individuals are responsible for most further infections. This study confirms it for India. It finds the overall transmission risk from a primary case to be 4.7% to 10.7%, depending upon the degree of risk. This risk was estimated to be 1.2% in healthcare settings, 2.6% in the community, and 9% within households. Of some interest in the LMIC context is disease transmission in long-duration shared public transport. Here, the secondary attack rate was found to be extremely high, at about 80%. The study suggests that transmission is most efficient within the same age group, especially between children (0-14 years) and the elderly (>65 years). This may reflect social interactions across generations and within joint families, which is common in India. Social interactions among children appear to be particularly conducive to transmission, which is important to consider while formulating policy to reopen schools.

As in high-income countries, this study showed age-specific estimates of mortality in a LMIC setting to range from 0.05% (5-17 years) to 16.6% (>85 years), with men being more susceptible across all ages. However, the study found low median time to mortality, of 6 days in India as compared to 13 days in the US. This is consistent with reports from Mumbai early in the pandemic, where patients sought care in late stages of the disease. The data also underline the importance of comorbidities like diabetes, hypertension, coronary artery disease and renal disease, with over 60% fatalities having at least one comorbid condition. Surprisingly, pre-existing asthma and chronic lung disease added little risk.

The study highlights the peculiarities of Covid-19 spread in a South Asian and LMIC setting. The two Indian states showed reducing incidence for people older than 30-39 years, whereas, in the US, the incidence rose sharply in people over 65 years. Covid-19 mortality in these Indian states levelled off over 65 years of age in contrast to the US, where the highest mortality is among those aged 85 years or more. The limited incidence and mortality among older adults (>65 years) found in this study was also observed in other data from Mumbai and Delhi. While some have attributed this to undercounting, it might also reflect as-yet-unknown socio-biological factors in elderly Indians. Stringent stay-at-home orders for older adults may have contributed to lower exposure to infection. As life expectancy in India is about ten years lower than in the US, socioeconomic factors that separate individuals who survive to old age from the general population are likely to be more pronounced in India than in countries with longer life expectancy.

This first detailed epidemiological study, limited to two states, comes about eight months after the first COVID-19 case was identified in India. This is disappointing for a vibrant democracy with global aspirations. India ought to by now have had comparable data across most of its states, so that even regional and sub-regional differences, could have been identified to guide more nuanced approaches to Covid-19. This reinforces the importance of making available public health data for analysis in real-time. A sensible policy can only be formulated based on timely and useful evidence.

Shahid Jameel is director, Trivedi School of Biosciences, and Gautam Menon is professor of Physics and Biology, Ashoka University – Financial Express