AbstractThe outbreak of COVID-19 has spurred extensive research worldwide to develop a vaccine. However, when a vaccine becomes available, limited production and distribution capabilities will likely lead to another challenge: who to prioritize for vaccination to mitigate the near-end impact of the pandemic? To tackle that question, this paper first expands a state-of-the-art epidemiological model, called DELPHI, to capture the effects of vaccinations and the variability in mortality rates across subpopulations. It then integrates this predictive model into a prescriptive model to optimize vaccine allocation, formulated as a bilinear, non-convex optimization model. To solve it, this paper proposes a coordinate descent algorithm that iterates between optimizing vaccine allocations and simulating the dynamics of the pandemic. We implement the model and algorithm using real-world data in the United States. All else equal, the optimized vaccine allocation prioritizes states with a large number of projected cases and sub-populations facing higher risks (e.g., older ones). Ultimately, the optimized vaccine allocation can reduce the death toll of the pandemic by an estimated 10–25%, or 10,000–20,000 deaths over a three-month period in the United States alone.Highlights–This paper formulates an optimization model for vaccine allocation in response to the COVID-19 pandemic. This model, referred to as DELPHI–V–OPT, integrates a predictive epidemiological model into a prescriptive model to support the allocation of vaccines across geographic regions (e.g., US states) and across risk classes (e.g., age groups).–This paper develops a scalable coordinate descent algorithm to solve the DELPHI–V–OPT model. The proposed algorithm converges effectively and in short computational times. Therefore, the proposed approach can be implemented efficiently, and allows extensive sensitivity analyses for scenario planning and policy analysis.–Computational results demonstrate that optimized vaccine allocation strategies can curb the death toll of the COVID-19 pandemic by an estimated at 10–25%, or 10,000–20,000 deaths over a three-month period in the United States alone. These results highlight the critical role of vaccine allocation to combat the COVID-19 pandemic, in addition to vaccine design and vaccine production.
Semismooth Newton Coordinate Descent Algorithm for Elastic-Net Penalized Huber Loss Regression and Quantile Regression
