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

Without some measures of suppression after lockdown is lifted, the impact of lockdown in bringing down the case-counts (the now ubiquitous term, "flattening the curve") can be negated by as early as the first week of June. In fact, in Figure 5a , the pre-intervention ("normal") curve first passes the social distancing and travel ban curve on June 8. In particular, if people immediately go back to pre-intervention ("normal") activities post-lockdown, a surge in the predicted case-counts is expected in the long-term beyond what we would have seen if there were only social distancing and travel ban measures without lockdown (7.1 million when post-lockdown activity returns to pre-intervention levels vs. 6.9 million under social distancing and travel ban without a lockdown period on July 31; Figure 5 ). We estimated that 195 (upper 95% CI 3494) and 380 (upper 95% CI 6463) cases per 100,000 are avoided by June 15 and July 15 respectively if people are cautious in their activities post-lockdown compared to the scenario where people return to normal preintervention activities. Long-term forecasting under slow adherence (2-week delay) can be seen in Supplementary Figure 2 .