COVID-19: Karnataka, Bihar have higher than average infection spread rate, says new prediction model

Karnataka and Bihar are the two Indian states that have experienced an exponential growth in COVID-19 infected patients in the last two weeks, according to an Indian disease prediction model.

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Both states report an R (reproduction number) value of 1.62 in the May 16-24 period, which is way above the national average (1.23) and higher than states like Maharashtra (1.27), Gujarat (1.26) and Tamil Nadu (1.56) with high case load.

R is the number of persons, one infected person can spread the infection. An R value of 1.9 means that 10 infected persons will on average cause 19 new infections.

India initially had an R of 1.8, which has now come down to 1.23, as per the model.

Developed by Sitabhra Sinha, a scientist at the Institute of Mathematical Sciences, Chennai the statistical model has captured the number of active cases till lockdown 3.0 accurately but overestimated the numbers a bit for the lockdown 4.0 period.

In the latest run of the model, Sinha estimated that India would be having around 100,000 active cases by May 30. In reality, there are more than 90,000 active cases currently.

Karnataka and Bihar exhibited increased growth rate in the previous run of the model on May 20. A third state, Odisha, too was also in the same bracket at that time.

"Both the states are showing a much higher exponential growth rate than earlier, because the last week has seen many active cases emerging at a rate much higher than expected from the earlier growth rate," Sinha gold DH.

"Given that usually it takes about 10 days to two weeks for any change in ground conditions to be reflected in the active case numbers, the causative factors (contributing such a growth) happened between April 24-28 for Bihar and between May 2-5 for Karnataka," he said.

Meanwhile, a second Indian disease prediction model questions the NITI Ayog's claim of averting 37,000 to 210,000 fatalities during the lockdown.

"We estimate that approximately 8000 to 32,000 fatalities have been averted, till May 15, compared to a “do nothing” scenario," a team of nine scientists who developed the dynamic INDSCI-SIM model said in a statement. The researchers are from five institutes including IMSc and Indian Institute of Sciences, Bengaluru.

In a set of presentations last week, NITI Ayog presented four sets of figures on the number of lives saved due to the lockdown, but didn't disclose the methods adopted to arrive at such figures.

The estimates were made by different agencies like the Boston Consulting Group, Public Health Foundation of India, Indian Statistical Institute and two sets of independent experts.

"Our estimates are more conservative, but explicitly documented. Our analysis estimates that a large number of infected cases remains undetected. We urge that the governmental agencies should provide details of their analysis while making projections or estimates related to the epidemic spread," the INDSCI-SIM team said.