Coronavirus: ‘Severe’ steps can cut cases drastically, says study

Amitabh Sinha
Coronavirus: 'Severe' steps can cut cases drastically, says study

Migrant workers walk along a road to return to their villages, during a 21-day nationwide lockdown to limit the spreading of coronavirus disease (COVID-19), in New Delhi, March 26, 2020. (Reuters Photo: Danish Siddiqui)

The 21-day lockdown that began at midnight Tuesday has brought enormous misery and suffering to some of India's most vulnerable people -- however, in the absence of aggressive government intervention, the infection could, spreading at its current rate, reach 4,800 people by April 15 and, a month from then, balloon to 58,600, says a new study.

The study published earlier this week has weighty institutional backing -- it has been carried out by scientists at Johns Hopkins University and the University of Michigan among other American universities. However, it uses data until March 16 -- and does not reflect the impact of government interventions made before (such as airport screenings and quarantines) and after that date ('Janata Curfew' and the ongoing nationwide lockdown).

Social distancing measures like the ban on travel and stopping of trains and flights would likely have slowed the spread of the novel coronavirus.

The study acknowledges this -- "the most severe form of interventions", it says, would drastically bring down the number of infected people, and restrict the spread to just about 13,800 cases by May 15, instead of 58,600. In this situation, the study's authors say, it is "highly appropriate to adopt draconian measures".

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The study does not specify what the "draconian measures" or "severe" interventions might be, but it does discuss the possible impact of social distancing, travel ban and lockdown, without which the number of infected people (including those who would have recovered) could rise to even 1.31 lakh by June 15. Even these numbers, according to the study, could be "at best underestimates" based on early-phase data.

Bhramar Mukherjee, a professor of epidemiology and chair of biostatistics at The University of Michigan School of Public Health, and one of the authors of the study, told The Indian Express in an email that the researchers had run the models again with data up to March 21, and the numbers had indeed increased from the ones that were published in the study.

"We just ran the model with data till March 21 and the projections for May 15 became around 100,000 (upper limit could go up to 1.3 million), which used to be 58,600 (upper limit of 915,000). It is still important to look at data every day, and keep updating our model for the most informed projections," Prof Mukherjee said.

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A large number of labourers from Bihar , Rajasthan have been stuck at a school construction site in Delhi's Uttam Nagar due to the lockdown. (Express Photo by Tashi Tobgyal)

The COV-IND-19 Study Group, which did the modelling, found that an infected person in India was passing on the infection to at least two more people on average. "This is a modest value. The range of 2 to 3.5 (persons per infected person) is sensible for COVID-19 in early phase without intervention," she said.

A different study by two scientists at the Institute of Mathematical Sciences at Chennai, which was reported by this newspaper last week, had found that every infected person was transmitting to only 1.7 persons on average. That study, by Soumya Easwaran and Sitabhra Sinha, did not make a long-term projection for the spread.

The past four months (since the outbreak of the disease in Wuhan) has been "a failure of our social collective imagination", Prof Mukherjee said. "From the point of 'COVID-19 is not a big deal' to a complete lockdown has taken only a few days in many places. I feel watchful waiting, intelligent surveillance are needed to come up with clever and perhaps modulated intervention strategies."

The study acknowledges that there are uncertainties in the calculations due to the several unknowns. "The uncertainties in our predictions is largely due to many unknowns arising from model assumptions, population size, accuracy of diagnostic tests for COVID-19, heterogeneity in implementation of different government-initiated interventions and community-level protective measures," it says.

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