Branch-and-Price for Prescriptive Contagion Analytics

Abstract

A new optimization methodology helps allocate vaccines to combat a pandemic. During the COVID-19 pandemic, the extraordinary speed of vaccine developments quickly gave rise to a critical operational problem: How to distribute a scarce stockpile of vaccines across communities? In “Branch-and-Price for Prescriptive Contagion Analytics,” A. Jacquillat, M. Li, M. Rame, and K. Wang address this question by presenting a methodology to allocate shared resources across subpopulations governed by contagion dynamics. This problem combines the difficulties of mixed-integer nonconvex optimization and those of optimization with constraints governed by ordinary differential equations. By combining novel column generation, approximate dynamic programming, and branch-and-bound elements, the authors’ methodology can solve large and otherwise-intractable instances, outperforming state-of-the-art benchmarks. From a practical standpoint, their approach can significantly enhance the effectiveness of a vaccination campaign. Ultimately, the results from the paper outline vaccine allocation—beyond vaccine development and vaccine manufacturing—as a critical lever to mitigate the impact of a pandemic on public health.