Predictions, role of interventions and effects of a historic national lockdown in India's response to the COVID-19 pandemic: data science call to arms

Debashree Ray; Maxwell Salvatore; Rupam Bhattacharyya; Lili Wang; Shariq Mohammed; Soumik Purkayastha; Aritra Halder; Alexander Rix; Daniel Barker; Michael Kleinsasser; Yiwang Zhou; Peter Song; Debraj Bose; Mousumi Banerjee; Veerabhadran Baladandayuthapani; Parikshit Ghosh; Bhramar Mukherjee

We did explore some alternative assumptions and conducted thorough sensitivity analysis before settling on the models presented above. In one example, we assumed that there are actually 10 times the number of reported cases to date to reflect potential underreporting of cases due to lack of testing. In another scenario, we assumed these cases occurred in metropolitan areas to reflect a potential intensification of case clustering. In yet a third scenario, we hypothesized that R0 prior starts with 2.5 instead of 2.0 (i.e., a single infected individual would infect 2.5 susceptible individuals, on average, instead of 2). These scenarios did not appreciably change our conclusions in broad qualitative terms, though the exact quantitative projections are quite sensitive to such choices. Across these scenarios, the projected total number of infected cases by the entire first phase of the pandemic varied between 2-15% of the population, again showing the significant variability in these numbers. The estimates we present here may appear conservative and are at best underestimates, and, in all cases, our confidence in these projections decreases markedly the farther into the future we try to forecast. It is extremely important to update these models as new data arise.