Predictions, role of interventions and implications of a national lockdown on the COVID-19 outbreak in India
India has taken strong and early public health measures for arresting the spread of the COVID-19 epidemic. With only 536 COVID-19 cases and 11 fatalities, India –-- a democracy of 1.34 billion people --– took the historic decision of a 21-day national lockdown on March 25. The lockdown was further extended to May 3rd, soon after the analysis of this paper was completed. The lockdown was again extended to May 18 while this paper was being revised.
In this paper, we use a Bayesian extension of the Susceptible-Infected-Removed (eSIR) model designed for intervention forecasting to study the short- and long-term impact of an initial 21-day lockdown on the total number of COVID-19 infections in India compared to other less severe non-pharmaceutical interventions. We compare effects of hypothetical durations of the lockdown on reducing the number of active and new infections. We find that the lockdown, if implemented correctly, has a high chance of reducing the total number of COVID-19 infected cases in the short term, and buy India invaluable time to prepare its healthcare and disease monitoring system. Our analysis shows we need to have some measures of suppression in place after the lockdown for increased benefit (as measured in terms of reducing the number of active cases). From an epidemiological perspective, a longer lockdown between 42-56 days is preferable to substantially "flatten the curve" when compared to 21-28 days of lockdown. Our models focus solely on projecting the number of COVID-19 infections and thus, inform policymakers about one aspect of this multi-faceted decision-making problem. We recognize that the collateral damage of a lockdown from social and economic perspective could be massive.
We conclude with a discussion on the pivotal role of increased testing, reliable and transparent data, proper uncertainty quantification, accurate interpretation of forecasting models, reproducible data science methods and tools that can enable data-driven policymaking during a pandemic. Our contribution to data science includes an interactive and dynamic RShiny app (covind19.org) with short- and long-term projections updated daily that can help inform policy and practice related to COVID-19 in India. We make our prediction code freely available for reproducible science and for other researchers to use these tools for their own prediction and data visualization work.
Bhramar Mukherjee, John D. Kalbfleisch Collegiate Professor and Chair, Department of Biostatistics; Professor, Department of Epidemiology, University of Michigan (UM) School of Public Health.
Bhramar is also Research Professor and Core Faculty Member, Michigan Institute of Data Science (MIDAS), University of Michigan. She also serves as the Associate Director for Quantitative Data Sciences, The University of Michigan Rogel Cancer Center. She is the cohort development core /co-director in the University of Michigan’s institution-wide Precision Health Initiative. Her research interests include statistical methods for analysis of electronic health records, studies of gene-environment interaction, Bayesian methods, shrinkage estimation, analysis of multiple pollutants. Collaborative areas are mainly in cancer, cardiovascular diseases, reproductive health, exposure science and environmental epidemiology. She has co-authored more than 240 publications in statistics, biostatistics, medicine and public health and is serving as PI on NSF and NIH funded methodology grants. She is the founding director of the University of Michigan’s summer institute on Big Data. Dr. Muhkerjee is a fellow of the American Statistical Association and the American Association for the Advancement of Science. She is the recipient of many awards for her scholarship, service and teaching at the University of Michigan and beyond, including the Gertrude Cox Award, from the Washington Statistical Society in 2016 and most recently the 2020 L.Adrienne Cupples Award from Boston University School of Public Health.
Bebashree Ray, Assistant Professor of Epidemiology and Biostatistics in the Johns Hopkins Bloomberg School of Public Health.
Bebashree's research focuses on developing novel statistical methods and software for discovering genetic determinants of common human diseases. She has primarily worked on type 2 diabetes, cardiovascular traits and orofacial clefts. Her research interests also include statistical methods for meta-analysis of cohorts, case-control studies, and multivariate analysis.
Rupam Bhattacharyya, second-year PhD student in the Department of Biostatistics at the University of Michigan.
Repam received his Master of Statistics and Bachelor of Statistics degrees from the Indian Statistical Institute, Kolkata. Currently progressing towards his candidacy, Rupam works on development and application of Bayesian methods in integrative omics and precision oncology with Prof. Veerabhadran Baladandayuthapani at the University of Michigan. Though his primary research interests circle around precision medicine and cancer research, Rupam is also excited about applied research in other wings of Biostatistics, including statistical genetics, disease epidemiology and clinical trials.
Maxwell Salvatore, research area specialist in the Department of Biostatistics at the University of Michigan.
Maxwell received his MPH in Epidemiology from the University of Michigan in 2017. He was advised by Dr. Rafael Meza and worked on liver cancer incidence trend analyses, while taking coursework in global health, cancer epidemiology, and modeling. Since then, he has been working with Dr. Bhramar Mukherjee on projects related to biobank-based research using Michigan Genomics Initiative and UK Biobank data, cancer risk and prevention, and health disparities. He is about to start as a doctoral student in Fall of 2020 in the Department of Epidemiology, University of Michigan School of Public Health.