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

standard SIR model, called eSIR model, 11 where we can create hypothetical intervention scenarios in a time dependent manner. The goal of any intervention is to reduce the chance that an infected person meets a susceptible person. We create models for declines/drops in contact probabilities when an intervention is rolled out. Thus, there is some intrinsic ad-hocery to our assumptions. Any statistical model is wrinkled with such assumptions. Similarly, the predictions themselves have large uncertainty (as reflected by the upper credible limits). As we interpret the numbers from any model, let us use caution in not over-interpreting them. A rigorous quantitative treatment often allows us to analyze a problem with clarity and objectivity, but we recommend focusing more on the qualitative takeaway messages from this exercise rather than concentrating on the exact numerical projections or quoting them with certainty.