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

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

A data-driven statistical model for Covid-19, which goes by the name Adaptive Control, has shown that India was successful in controlling the spread of infection in the second week of August. But the spread of the disease has worsened once again in the third week and on, and the situation remains critical once again after a brief period of control, the model indicates. 

The model calculates the dynamic reproductive rate or Rt, which shows how the R0—number of new people infected per infected person—is changing over time. 

The Adaptive Control model shows that Rt rose in India after July 15, about the day when confirmed cases in India crossed one million. This was a hint that the two-million mark would be crossed earlier than expected, and it did happen that way. Rt  had gone below 1.0 in the period between August 5 and August 11—meaning that ten infected people transferred the infection about nine people, signifying control in spread.

The Rt sprung back to nearly 1.2 on August 18, meaning that 10 infected people transfer the virus to 12 new people. It has now further declined to 1.12, indicating a slight improvement, as confirmed cases crossed 3 million on August 22. Among major infected states, too, the rate of spread is slowly reducing. 

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 model, or the Susceptible, Infected and Recovered model. Mumbai-based think tank IDFC Institute is a collaborator in this project. 

Researchers indicated that this may represent a missed opportunity. In addition, they also underlined the possibility that the current estimate of Rt according to their model may undercount the actual spread of the infection given the delay in centralised data collection. 

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

Andhra Pradesh, which led among states in terms of fastest spread of the disease in July, is now under control, with 96 new people getting infected per 100 infected. 

The situation in Karnataka worsened before improving slightly, and had 126 new infections per 100 pre-infected people on August 23, according to the Adaptive Control model. Maharashtra and Karnataka, thus, continue to be in a critical condition.

Delhi, on the other hand, was recording the highest daily cases in India at one point of time in June. After nearly 50 days of having dynamic reproductive rate for Delhi below 1.0, Delhi is once again inching towards critical spread. 

Anup Malani, professor at the University of Chicago Law School, and one of the lead investigators in the group said that 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 get insights on people’s movement that is crucial to the spread of Covid-19,” he told Business Standard.

Luis Bettencourt, the architect behind the Adaptive Control model likens the data exercise to a detective story.

Bettencourt, who is the director at Mansueto Institute of Urban Innovation at the University of Chicago, said that his model 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 told Business Standard. 

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

In terms of policy implementation, he said that dealing with clusters or smaller areas—such as stratified localities in cities—is crucial in dealing with Covid-19.

Emergence of new hotspots, though a common phenomenon now, is what is making things worse for all administrations, now especially when rural pockets are showing rapid spread. 

By its virtue, the Adaptive Control model attempts to reduce the element of surprise by giving out district-wise reproductive rate of infections. This indicator is better than the headline number of cases or recoveries to direct policy response, helping decision makers make informed choices. 

The good part is that some large states are taking inputs from these models to design policy responses, effectively making way for data-driven, evidence-based policy making. These models track real-time data and offer the best insights available from it, more than just the headline number of confirmed cases and deaths.