India is in the middle of a 21-day nationwide lockdown to blunt the spread of COVID-19. Internal immigration and impracticality of social-distancing in joint families and crowded neighbourhoods make for a potentially volatile situation. Also rampant are anecdotes and preliminary evidence that India may have some sort of natural immunity to COVID-19 and the numbers of infections have not yet exploded as projected.
However, considering that the number of new infections is still growing, the big question is whether it is better to attribute the ‘low’ number of new infections and deaths to early social distancing and lockdown efforts or some hitherto unverified natural immunity. The decision about relaxing or continuing the lockdowns is not an easy one.
That is where the Infectious Disease Forecasting (IDF) models come in. Very briefly, these models divide the population into Susceptible, Infected and Removed (cured or dead) compartments and use mathematical equations with various biological and physical parameters to move people between these boxes. The outcomes are predictions and what-if scenarios of the future evolution of an infectious disease. These models are referred to as SIR (Susceptible-Infected-Removed) or SEIR (Susceptible-Exposed-Infected-Removed) models. They have been employed as a part of the decision-making process in recent outbreaks such as the SARS, H1N1 and Ebola.
What-if scenarios can include parameters such as the impact of lockdowns, environmental parameters (temperature, humidity), socioeconomic vulnerability and lack of social distancing in joint families.
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While the predictions of SRI/SEIR models are not always as accurate as one would want, they have proven very helpful for estimating the requirements for equipment, masks, and other non-pharmaceutical interventions (NPIs). In some sense, the prediction cannot be very accurate because constant interventions keep changing the evolution of the outbreak. Thus the models are shooting at a fast moving target. Nonetheless, it is clear as day that they are a valuable tool to deal with outbreaks.
The IDF and the related public health decision-making are broadly termed as outbreak science. Clearly, outbreak science should include data gathering, surveillance, and related genomic, viral, and pharmaceutical activities. Related efforts include awareness, training and capacity building to manage outbreaks.
India established a Department of Health Research (DHR) in 2007 under the Ministry of Health and Family Welfare to utilise modern health technologies in diagnosing, treating and vaccinating. Products are envisioned to find their way into the public health system. DHR has since been implementing its mandate of promoting and coordinating clinical and operation research.
It has set up international collaborations for training and scientific exchange. It administers the Indian Council of Medical Research (ICMR). DHR has set up a network of Viral Research and Diagnostics Labs, Multi-Disciplinary Research Units, and Model Rural Health Research Units. A Human Resource Development for Health Research is also implemented. There are data gathering, quality control and curating efforts as well as surveillance plans.
A 3D model of the novel coronavirus. Photo: Pixabay
The COVID-19 pandemic, which is still underway with no apparent end in sight, underscores the fact that IDF efforts in India are few and far in between. India needs a dedicated IDF centre like the ones established for weather and climate predictions. India’s weather and climate prediction enterprise is beginning to yield dividends on the investments made thus far. The applications of these predictions to various sectors like aviation and agriculture are also extending into other sectors.
A recent report by the Johns Hopkins University (JHU) can provide some guidelines for India on a charter for an IDF centre. The report notes the urgency of developing outbreak science and the need to effectively connect IDF with public health decisions. Current approach of calling on the academic and other communities to assist with IDF in the middle of an outbreak has severe limitations.
The mission mode approach taken to bring India to the world-standard on weather and climate forecasting must be urgently implemented for outbreak science as well. Many gaps identified by the JHU report are also relevant for India. The lack of formal mechanisms to allow quick access to data and funding resources is a glaring impediment. Outdated incentive systems in academia, which prevent quick transition to translational or operational research is also a handicap. There are other gaps such as sustained funding for advancing models to remain ready for new outbreaks.
Funding is also needed for extensive deployment of sustained networks for physical and social data gathering. It is a complex combination of pathogens, humans and environment that determine the reproduction rates and spread of infectious diseases. Resilience to contagion also depends on socioeconomic vulnerability and family structures (joint vs nuclear). All data, including medical histories must be brought under uniform protocols while also protecting privacy. Data visualisation is also critical for quickly crossing the language barriers between natural and social scientists, public health workers and decision makers.
While DHR has an effort for training and education, the IDF centre can add real-time outbreak experience when possible. For example, medical residents and students are now being pushed to the frontline in the battle against COVID-19. Capacity building to have trained IDF experts must consider the disparate climate regimes, demographic mixes and locally endemic diseases to cover a country as inhomogeneous as India.
Climate vulnerability of the neighbouring countries is a security threat to India. Similarly, health vulnerabilities of the neighbours are also a health security threat for India. Pathogens do not respect national boundaries and are easily transported by air in many cases. Some training provided by the IMD on weather and climate predictions to other countries can serve as an initial template for the IDF. Long-term funding and collaborations between infectious disease modellers and public health workers are needed not only within India but across the region.
As noted by the JHU report, during non-outbreak times, the teams should be working on developing, identifying, testing and evaluating new models and methodologies. Also important are best practices, effective non-pharmaceutical interventions and data visualisations. Improvement of data gathering efforts, novel crowdsourcing approaches at short notices, instrumentation and surveillance technologies are also important. These can be accomplished by sustained public-private-academic partnerships.
It is not a matter of ‘if’ another outbreak will occur that will bring humanity to its knees, but only a matter of ‘when’. With climate change and increased global connectivity and food demand, a dedicated IDF Centre is a national imperative for India. In fact, India should lead the effort to have a coordinated global network of IDFs to build on its related mandate under DHR. COVID-19 is a global disaster that should serve as an opportunity for IDF.
Raghu Murtugudde is a professor of atmospheric and oceanic science and Earth system science at the University of Maryland. He is currently a visiting professor at IIT Bombay.
