Recently, a modelling study conducted by the Massachusetts Institute of Technology (MIT) predicted 2.87 lakh coronavirus cases in India per day by the end of winter 2021 in the absence of a COVID-19 vaccine or drug intervention. In a pre-print paper, MIT professors Hazhir Rahmandad and John Sterman, and PhD candidate Tse Yang Lim, noted that the top ten countries by projected daily infection rates at the end of winter 2021 are India with 2.87 lakh infections per day, followed by the US, South Africa, Iran, Indonesia, the UK, Nigeria, Turkey, France, and Germany.
The study has come under a fair bit of flak, with the Union health ministry stating that the lacunae of many mathematical models is that they just focus on how the virus would behave and not take into account other parameters. The ministry said instead of spending a lot of time on these models, focusing on containment, surveillance, testing and treatment will give better results.
What does the MIT study say?
While noting that the projections are highly sensitive to assumed testing, behavioural, and policy responses, the MIT researchers considered projections under three scenarios: When the current country-specific testing rates and response functions are extrapolated moving forward; if enhanced testing (of 0.1 per cent a day) is adopted on July 1; if sensitivity of contact rate to perceived risk is set to 8, with testing at current levels.
The first two scenarios project a very large burden of new cases in the fall 2020, with hundreds of millions of cases concentrated in a few countries estimated to have insufficient responses given perceived risks, primarily India, but also Bangladesh, Pakistan, and the US. "Our model simulates the progression and spread of COVID-19, including how people interact, how many get sick, how many get tested, how many are hospitalised, how many die—and how people change their behaviour in response to the risk they perceive," Rahmandad said, quoted PTI.
"We then use a wide range of data to estimate the parameters of the model—say, what fraction of infections are asymptomatic, and how contagious the virus is—to give the best match to the real world data," he said.
The model revealed several important insights. Most fundamentally, the magnitude of the epidemic is widely underreported, the researchers said. They estimate that cases and deaths through June 18 are, respectively, 11.8 and 1.48 times higher than official reports across the 84 nations considered. Despite these elevated numbers, the authors note that no country is remotely close to establishing herd immunity, they said. "While actual cases are far greater than official reports suggest, the majority of people remain susceptible. Waiting for herd immunity is not a viable path out of the current pandemic," Rahmandad said.
Why is it controversial?
The Union health ministry said multiple parameters need to be taken into consideration. "One parameter is that how the infection or virus will behave, the other parameter is how the governments would behave towards the infection and another parameter is how will the community behave," said Rajesh Bhushan, senior officer on special duty in the health ministry.
"Many mathematical models just focus on how the virus or infection would behave and not other parameters," he said. "We all know that in the last few months various organisations with the help of mathematical models tried to make projections on the number and prevalence of this disease. But we believe that this kind of attempt does not take into account many parameters that are the lacunae of such mathematical models," he said at a press briefing.
"So we believe instead of spending a lot of time on these models, if we spend time on containment, surveillance, testing and treatment, that will give us better results," he added.
A lot of modelling predictions have proved unreliable. A March prediction by an international team of scientists, including from the Johns Hopkins University, was that India could face between 1 lakh to 13 lakh confirmed cases of the novel coronavirus by mid-May provided the trend in the growing number of COVID-19 cases continued. "However, it didn’t come to pass fortunately — because of the lockdown, which is something that the modellers of the study could never have foreseen," according to Sitabhra Sinha, professor of Computational Biology and Theoretical Physics at Chennai’s Institute of Mathematical Sciences (IMSc)
According to him, correctly predicting cases two weeks into the future is the best one can do. He said, as quoted by media reports: "Scientists have argued that mathematical models predicting the trajectory of COVID-19 cases are only approximate guides to the truth. Many variable involved, along with the knowledge on COVID-19 is still evolving, scientists said. I think, if you want to use models for correctly predicting cases, two weeks into the future is the best one can do, and if one’s lucky the trend will continue for longer, but you can’t bank on that."
In June 29, Livemint quoted Dr Balram Bhargava, director general, ICMR, as saying that none of the mathematical models has correctly predicted the course of the virus spread so far. "In such a scenario India should focus on the basics of epidemic prevention—test, track and treat. Modelling cannot incorporate all the factors responsible for the spread of the virus. They might give an idea about best and worst possible scenarios for nations to prepare their health infrastructure," he said.
IJMR, the journal published by ICMR, published an article on June 20 that stated none of the mathematical models on India have proven correct: "Several mathematical models projected the severity of pandemic in terms of cases and deaths. At least in the context of India, none of these proved correct. Estimates that emerge from modelling studies are only as good as the validity of the epidemiological or statistical model used; and accuracy of assumptions made for modelling. It was obvious that the models proposed during the COVID-19 pandemic carried a strong element of bias and used assumptions which proved to be far from real. The power of these models to influence policy decisions for advance planning is a huge risk. Predicting infectious diseases for a novel pathogen is an extremely perilous proposition and should be avoided."
-Inputs from PTI
