The chance to tame Covid-19 squandered after a good start

Adaptive Control model suggests India had shown improvement in the second week of August, but lost the opportunity later

Adata-driven statistical model for Covid-19, called Adaptive Control, has shown that India was successful in controlling the spread of the infection in the second week of August. But the situation worsened again in the third week, and now remains critical.

The model calculates the dynamic reproductive rate, or Rt, which shows how the Ro — number of new people infected per infected person — is changing over time.

The model shows that Rt rose in India after July 15, when the number of confirmed cases crossed one million. This was a sign that the two-million mark would be crossed earlier than expected, and that’s what happened. Rt had gone below 1.0 in the period between August 5 and August 11 — meaning that 10 infected people transferred the virus to about nine people, signifying a control in the spread.

The Rt sprung back to nearly 1.2 — meaning 10 Covid-positive people infected 12 more — on August 18. It has now further declined to 1.12, indicating a slight improvement, as confirmed cases crossed 3 million on August 22. The rate of infection is slowing in the worst-affected states, too.

The Adaptive Control model is being developed by researchers led by the University of Chicago. It dynamically calculates the “reproductive rate” of Covid-19 infections in India by using the SIR (Susceptible, Infected and Recovered) model. Mumbai-based think tank IDFC Institute is a collaborator in the project.

The data, researchers indicate, may represent a missed opportunity. They also underlined the possibility that the current estimate of Rt may undercount the actual spread of the infection, given the delay in centralised data collection.

The model points to the states where stronger policy response is needed. Though Maharashtra and Tamil Nadu lead in the number of cases, the latter has been successful in controlling the spread better than most top-infected states (see chart). In simple terms, 100 infected persons infect 111 new people in Maharashtra, but 77 in Tamil Nadu, the model shows.

In Andhra Pradesh, the state with the fastest spread of the disease in July, the number is 96 new infections per 100 infected. The situation in Karnataka, however, has worsened (126 new infections per 100). Besides Maharashtra and Karnataka, the picture is grim in Delhi, too. After nearly 50 days of Rt below 1.0, it is once again inching towards critical spread.

Anup Malani, professor at the University of Chicago Law School and one of the lead investigators, says the model updates the reproductive rate across Indian districts and states using various static and dynamic variables.

“We used Census data for mapping long-term migrations, Google data for current mobility, and by correlating historical data with current data, we gain insights into people’s movement that is crucial to the spread of Covid-19,” he explains.

Luis Bettencourt, the model’s architect and director at Mansueto Institute of Urban Innovation, University of Chicago, suggests that even at this evolved level of Covid-19 spread, contact tracing is more important than mobility restrictions. “It is important to disentangle efficient tracing from mobility. Better data can help to get a better measure of contacts. This is what our model points to,” he says.

He adds that a small Rt does not necessarily mean the situation is better. If the base number of confirmed cases is very high, such as in Maharashtra, Gujarat and Tamil Nadu, Rt only slightly more than 1.0 can also be very serious.