Where to locate COVID‐19 mass vaccination facilities?

Abstract

The outbreak of COVID‐19 led to a record‐breaking race to develop a vaccine. However, the limited vaccine capacity creates another massive challenge: how to distribute vaccines to mitigate the near‐end impact of the pandemic? In the United States in particular, the new Biden administration is launching mass vaccination sites across the country, raising the obvious question of where to locate these clinics to maximize the effectiveness of the vaccination campaign. This paper tackles this question with a novel data‐driven approach to optimize COVID‐19 vaccine distribution. We first augment a state‐of‐the‐art epidemiological model, called DELPHI, to capture the effects of vaccinations and the variability in mortality rates across age groups. We then integrate this predictive model into a prescriptive model to optimize the location of vaccination sites and subsequent vaccine allocation. The model is formulated as a bilinear, nonconvex optimization model. To solve it, we propose a coordinate descent algorithm that iterates between optimizing vaccine distribution and simulating the dynamics of the pandemic. As compared to benchmarks based on demographic and epidemiological information, the proposed optimization approach increases the effectiveness of the vaccination campaign by an estimated 20%, saving an extra 4000 extra lives in the United States over a 3‐month period. The proposed solution achieves critical fairness objectives—by reducing the death toll of the pandemic in several states without hurting others—and is highly robust to uncertainties and forecast errors—by achieving similar benefits under a vast range of perturbations.

A mass vaccination site selection problem: An application of GIS and entropy-based MAUT approach

“…Since there is no study in the literature that uses the proposed methodology (GIS-based MCDM) on mass vaccination site selection problem, different problems are summarized that apply similar methodology in different fields. For the mathematical modeling applications to locate mass vaccination sites, the reader is referred to recent studies of Bertsimas et al [ 7 ] and Bravo et al [ 8 ].…”

Towards a Unified Pandemic Management Architecture: Survey, Challenges and Future Directions

“…There are several works that utilize a similar optimization formulation that distributes vaccines among communities based on their direct and herd immunity and mortality minimization [57][58][59]. Bertsimas et al proposed a model that combines DELPHI epidemiological model with a bilinear, non-convex optimization to minimize exposure, mortality, and distance between vaccination centers and population centers [60]. Rao et al present an analytical model for vaccine allocation with the aim of minimizing the effective reproduction number [61].…”

Optimization in the Context of COVID-19 Prediction and Control: A Literature Review

“…Thus, the logistics of mass vaccination is an imperative aspect to consider. In this regard, [183] developed a bilinear nonconvex optimization model to determine the best location for vaccination sites.…”

“…Thus, the logistics of mass vaccination is an imperative aspect to consider. In this regard, [183] developed a bilinear nonconvex optimization model to determine the best location for vaccination sites. Other studies [184] , [185] developed a vaccination drive-through simulation tool to optimize the operation and effectiveness of mass vaccination facilities by implementing an agent-based modeling technique and a machine learning model, respectively.…”