As 2020 ends, India managed to diagnose COVID-19 in a little over one crore people and 150,000 deaths. While the proportion of undiagnosed cases is above 20 cases for everyone diagnosed case, a precise estimate is unknown. Although COVID-19 affects a small fraction of the population, a large proportion of communities experience socio-economic distress. This phenomenon is true to all epidemics where the indirect effect of the disease spread is far greater and longer – than the disease itself. Epidemics limit human mobility and cripple the economic activities of communities.
Over the years, advances in epidemiology – aided by data science, have increased the capacity of health systems to respond and control epidemics. The advent of large data analytics now provides real-time leads for major public health interventions – which even low resources countries of Africa used to control Ebola and measles. Undoubtedly, Government interventions driven by evidence and data control epidemics efficiently and minimizes the indirect socio-economic effects greatly.
In response to the COVID-19 pandemic, countries with research capacities – first activated a system of science feeding into political decisions. Other countries had an option of following advice by the WHO – an intergovernmental technical agency. Governments in India, unlike several developing nations, had (and still have) the luxury to base interventions on scientific evidence. However, Indian governments faltered in using Indian scientific community medical research systems to best use.
Based on Chinese data, Epidemiologists at the Indian Council of Medical Research had estimated COVID-19 outbreak patterns in Indian cities as early as the 3rd week of February 2020. Public intellectuals projected the imminence of severe socio-economic distress. Yet, the union government was in denial of an epidemic threat throughout the first half of March and constituted a task force only on 19th March 2020; seven days after WHO declared a pandemic. Given that a total lockdown was announced within five days of constituting the task force, it is unlikely that the experts contributed to the first major intervention. Moreover, the task force was (and continues to be) co-led by a pediatrician and a cardiologist. Likewise, prominent clinicians or government medical officers led expert committees in several states including Karnataka. In advanced countries, epidemiologists, public health, and infectious disease specialists advise governments for COVID-19 interventions. To begin with, Indian governments delayed acknowledging the threat of an epidemic despite scientific evidence and denied themselves (and continues to) appropriate expert advice.
By mid-March 2020, three months into the epidemic, Chinese researchers had published basic epidemiological characteristics of COVID-19. These sets of data informed containment measures in most of Europe and the USA. The WHO quickly designed protection schemes for vulnerable populations meant for low-income countries. Groups of population and areas were treated differently based on modeled risks. Geo-spatial mapping guided the identification of high and low transmission areas.
Protocols for public safety and COVID-19 appropriate behaviors were implemented in public areas. Based on transmission dynamics data from Europe, Germany adopted unique surveillance and micro-containment plan, thus avoiding a painful peak and high case-fatality rate. All these data-driven approach allowed minimal functioning of the economy and thus protected livelihoods. Based on Chinese and Italian epidemiological data, around April, Italy, Spain and France published data on clinical course and management of COVID-19. This allowed countries to refine testing strategy and treatment methods. Hospitals were now organized better to treat COVID-19 and non-COVID diseases by adapting multiple triage protocols.
India too has a robust system of data collection and analysis, mainly handled by integrated disease surveillance program. Advanced IT and geo-spatial technology environment further augment the Data science capacity of India. Yet, to date, there is no worthwhile publication on epidemiological or clinical characteristics of COVID-19 in India or its states. Governments simply enforced a total lockdown, with multiple extensions, without considering transmission dynamics relative to the population. The arbitrary number of positive cases decided local containments, and its perimeter. Without appropriate data, health systems could not re-organize their structure or resources to manage the non-COVID illness. Moreover, treatment guidelines were based on general WHO guidelines and not on Indian contextual issues. The testing strategy was determined only by the availability of testing kits and human resources. Later in the epidemic, the government heavily relied on rapid tests without considering its issues of false-negative tests. Rapid tests help in quick containment of a peak but are not useful for routine surveillance. There was scope to adopt smarter testing algorithms using epidemiological strategies that offsets false negative issues of rapid tests. This strategy has also limited India’s capacity to survey new virus strains.
However, groups of Indian scientists did attempt to provide actionable data to the government. In April 2020, ICMR did conduct and publish a SARI survey prior to the availability of widespread testing. Formulation of a community-based micro-containment strategy followed. Two other academic groups analyzed the pattern of COVID spread in closed spaced during the lockdown. None of these pieces of evidence influenced any government action. However, this does not mean that governments were oblivious to data. They often publically interpreted a series of seroprevalence surveys (conducted by ICMR and state governments) to suit the political narrative. Prior to this, data of ‘Tabliqi’ jamaat attendees was liberally discussed; leading to wide-spread stigma. Selective application and discussion of data only proved counterproductive to containment efforts.
Despite strong foundations, the medical research community appeared jittered – often yielding to media (and possibly political) pressure. This was apparent in ICMRs approval to use Hydroxycholoroquine, Remedesvir, and the Chinese rapid test kits, despite no evidence of benefit. The use of two drugs consumed organizational resources, nationwide and provided a false sense of hope – all to no benefit. Further, the government allowed alternate medical systems to make baseless and sometimes – fake claims of cure, thus distracting containment efforts. Encouraging non-scientific interventions and adopting multiple contradicting strategies is counterproductive in managing an epidemic.
Evidence-based policies and intervention is the cornerstone to manage any challenge to society. India is fortunate to have nurtured a research eco-system capable of generating actionable evidence. However, the public health research system was underutilized to manage the COVID-19 outbreak. Unlike advanced countries, public health researchers in India, work in silos and are not part of health system leadership. On the other hand, governments are often reactive to situations and attempt to manage perceptions more than the problem. However, to tackle the emerging challenge of Coronavirus and its strains, Indian governments need to upgrade epidemiological capacity to generate evidence that is more actionable and uses the same for the public good.