COVID-19: Prediction, Prevalence, and the Operations of Vaccine Allocation

Problem definition: Mitigating the COVID-19 pandemic poses a series of unprecedented challenges, including predicting new cases and deaths, understanding true prevalence beyond what tests are able to detect, and allocating different vaccines across various regions. In this paper, we describe our efforts to tackle these issues and explore the impact on combating the pandemic in terms of case and death prediction, true prevalence, and fair vaccine distribution. Methodology/results: We present the methods we developed for predicting cases and deaths using a novel machine-learning-based aggregation method to create a single prediction that we call MIT-Cassandra. We further incorporate COVID-19 case prediction to determine true prevalence and incorporate this prevalence into an optimization model for efficiently and fairly managing the operations of vaccine allocation. We study the trade-offs of vaccine allocation between different regions and age groups, as well as first- and second-dose distribution of different vaccines. This also allows us to provide insights into how prevalence and exposure of the disease in different parts of the population can affect the distribution of different vaccine doses in a fair way. Managerial implications: MIT-Cassandra is currently being used by the Centers for Disease Control and Prevention and is consistently among the best-performing methods in terms of accuracy, often ranking at the top. In addition, our work has been helping decision makers by predicting how cases and true prevalence of COVID-19 will progress over the next few months in different regions and utilizing the knowledge for vaccine distribution under various operational constraints. Finally, and very importantly, our work has specifically been used as part of a collaboration with the Massachusetts Institute of Technology's (MIT’s) Quest for Intelligence and as part of MIT’s process to reopen the institute. Funding: Financial support from MIT Quest for Intelligence is gratefully acknowledged. Supplemental Material: The online appendix is available at https://doi.org/10.1287/msom.2022.1160 .