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Mathematical modelling can enable the entire gamut of decision-makers to make informed choices

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Researchers from Jawaharlal Nehru Centre for Advanced Scientific Research (JNCASR), under the Department of Science and Technology (DST), Government of India; Indian Institute of Science (IISc) Bengaluru and Indian Institute of Technology (IIT) Mumbai have collaborated to develop a model to predict the evolution of COVID-19 and the medical needs that will arise as a consequence. Prof Santosh Ansumali, Associate Professor, Engineering Mechanics Unit, JNCASR, Bengaluru; Dr Aloke Kumar, Assistant Professor, IISc Bengaluru; and Prof P Sunthar, Associate Professor, IIT Mumbai give more details about their model, its relevance, and the importance of utilising mathematical models to design effective public health intervention strategies, in an exclusive interaction with Lakshmipriya Nair

Can you outline the role of mathematical modelling in optimising healthcare decisions, especially during emergencies and pandemics?

Healthcare decisions, in an unprecedented scenario like the COVID-19 pandemic, throw up many challenges for decision-makers. What to prioritise? Which area to prioritise? In what quantity should healthcare products like PPE, etc. be ordered? All these questions can be challenging enough for a single hospital; now imagine the enormity of the challenge when one is planning for an entire district, state or even nation.

This is exactly where mathematical modelling can come to aid – by providing a sneak peek at what might be the issues that will crop up in the coming weeks. Mathematical modelling can enable the entire gamut of decision-makers to make informed choices. We know that in the case of COVID-19, the healthcare debacle in many European nations was primarily a result of lack of preparation and the total surprise of decision-makers at the scale of the problem. We know that for COVID-19, the healthcare debacle in many European nations was primarily a result of lack of preparation and the total surprise of decision-makers at the scale of the problem.

Here, we wish to emphasise that mathematical modelling in the context of planning during an epidemic is not for predicting precise numbers like the stock market or rocket science. The goal here is to get approximate estimates of the demand and plan accordingly.

How have mathematical models already enabled health systems to design effective public health intervention strategies? Can you give us a few examples, both Indian and global?

Interestingly, India has a long history of using mathematical models successfully in disaster management planning in the context of metrological events. In 2019, before Cyclone Fani could hit Orissa, more than a million people were evacuated. Models are used in metrology for predicting paths of cyclones.

In the context of COVID-19, we have a lot of examples already.  Almost every developed country is utilising mathematical modelling as one of the central tools in decision making. Countries like New Zealand and China have tried lockdown based on the prediction of its models. The UK had a failed attempt of trying to acquire herd immunity by letting everything stay in the open as long as possible. While the idea has a scientific background, the consequences in terms of human suffering and possible overload on the healthcare system can be too high.

The US has adopted a data-driven model (Murray model) for its planning purposes.  Our attempt, in the Indian context is chronologically parallel to the US’ attempt and we are trying to make healthcare preparations based on such models.

Our work is now available as a medical inventory projection dashboard.


The development of this dashboard is an academic initiative facilitated by the Office of the Principal Science Advisor (PSA) to GoI among JNCASR, IISc, IIT Bombay and AFMS.

Which are the most common/preferred mathematical modelling methods used in healthcare? How do they work and what makes them the preferred models?

Standard tools for pandemics include various epidemiological models that typically involve the solution of coupled ordinary differential equations. These differential equations model utilise various known aspects of the disease to simulate how the disease may progress in a social environment with the “normal” movement and mixing of people.

Our model differs from the standard approaches; it takes a heuristic and data-based approach to predict the path of COVID-19. Since many biological parameters of COVID-19 are still not well-known and understood, on-the-go predictions can be quite challenging. Hence, we identified various nations whose trajectories preceded India and extracted key information that allows us to predict possible paths of COVID-19. Moreover, our model is adaptive, implying that it corrects for errors between prediction and reality as more data comes in. This makes it robust.

Going forward, what are the steps that India should take to effectively incorporate mathematical models and computational technologies to develop a multi-criteria risk analysis system for diseases, both infectious and chronic, to improve our preparedness for healthcare challenges and ramp up our capacities?

We hope that the COVID-19 pandemic will be a turning point for Indian science, where Indian S&T will take a lead in solving problems of societal relevance and in turn, India will invest more in building up capacity in S&T.

Again, we would like to draw a parallel between what is done in the case of metrological disasters in this country. Computational modelling and sensor network-related infrastructure put together, provide enough input for preparation of disaster management. Year after year, we have shown that a system which is well prepared can minimise the loss of life and livelihood.

We need to replicate that success in healthcare too. This idea of war rooms-like interface where the needs for a whole country can be monitored and scientific projections can be used for planning can solve a lot of problems in the Indian healthcare system.

What are the major challenges faced by mathematical models and computational technologists in the current public health scenario? How can they be overcome?

There is a lack of directed research effort that ails this sector. But apart from that, there are other real-world challenges in disease prediction and modelling in countries like India, such as a lack of reliable information. Mathematical modelling and computation do not work in isolation from the ground realities; rather they need to work with real data in order to evolve properly. One of the singular challenges in India remains the lack of reliable information. COVID-19, however, has seen unprecedented collaborative effort, where crowd-sourced data has become easily available and has been a boon for modellers like us.

Once the dust settles, learnings from this pandemic should be used to create a healthcare data collection network which is robust, takes into account scientific needs and where data is easily accessible.

How is your model helping tackle the coronavirus pandemic, be it in outbreak investigation, risk assessment or epidemiological research? How can it be applied in a more effective manner to improve outcomes?

Our modelling efforts have brought about an unprecedented co-operation between central and state administrations as well as university-level research groups. The raison d’etre of models is to foretell disasters and help society prepare for what would normally not be expected. Our model addresses not only the progress of the pandemic at the district levels, but also addresses the potential shortfalls in intensive care, acute care, supportive care and key medical supplies. These models can be made available to the district-level administrations, who can then assess the differential between the ground condition and projected requirements and thus fill any required gaps.

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