Covid-19 is a Black Swan event, so one has to take decisions in an uncertain environment. The government had the task of choosing what path to follow in fighting the disease with little available information. Initially, various experts predicted India was going to be the next hot spot and many had spelt doomsday for India with over 300 million cases and 2 million to 2.5 million deaths in the near future. On the other hand, there were others who thought it won’t be too serious.
In tackling the pandemic, India followed an approach similar to the Barbell strategy in finance – hedging for the worst outcome initially, and updating its response step by step via feedback. Hence, the government announced a complete lockdown in late March, hedging for the worst outcome of millions being infected. The 40-day lockdown period was used to scale up our health infra — building hospitals, procuring and manufacturing testing and PPE kits, educating citizens about social distancing and masks, etc. With time, as our understanding of the pandemic improved, the country started opening up gradually. To date, we have witnessed three stages on Unlock and are currently in the fourth phase. One specific statistics i.e. “proportion of people tested positive out of total tests performed” lies at the heart of understanding the spread of the pandemic. We use Bayes framework to interpret the trajectory of the positivity rate and how it fits with the policy followed.
The ratio of positive cases to tests can be interpreted in terms of “Sensitivity” and “Specificity” of the testing criteria and “Prevalence” of the virus using Bayesian framework. Prevalence is the existing rate of infection in the population, Sensitivity is the ability of a test to correctly identify those with the disease and Specifity relates to the conditions to get oneself tested. At the beginning of the pandemic, it is expected that the proportion of positive cases would increase. But, over time, with decreased specificity along with decrease in prevalence, it should fall. Similar trajectory can be seen for India.
On the other hand, Uttar Pradesh and Assam have managed to keep the positive case to test ratio in a lower range. Tamil Nadu and West Bengal saw an increase in positivity rate in Unlock 1.0, stabilising at about 8.3 per cent since August 15, though Tamil Nadu’s spike in positivity rate was sharper than West Bengal. Further, Delhi, which saw a huge spike in positivity rate in Unlock 1.0 and 2.0, is seeing a gradual fall since mid-July. However, one has to keep in mind the varied testing rates while comparing states.
It is true that the total number of positive cases is increasing daily but it should be looked together with higher testing, increasing recovery and a falling case-fatality rate. The interpretation from Bayes framework shows that we have early signs of attenuating growth of prevalence of infection at an all-India level, however prevalence is still increasing in many states, including Maharashtra, Andhra Pradesh and Karnataka, whereas it has been controlled in others like UP and Bihar. In line with the Barbell strategy, the government has been gradually opening up the economy, and the results from our Bayesian Framework also conform to it.
Aakanksha Arora is deputy director, Ministry of Finance, and Mahima is consultant, Ministry of Finance, Aasheerwad Dwivedi is assistant professor, SRCC, Delhi University