Where to locate COVID-19 mass vaccination facilities?

TL;DR

This paper presents a data-driven optimization model for locating COVID-19 vaccination sites in the US, improving vaccination effectiveness and fairness while being robust to uncertainties.

Contribution

It introduces a novel integrated epidemiological and prescriptive model to optimize vaccination site locations and allocations, enhancing campaign effectiveness.

Findings

Increases vaccination campaign effectiveness by 20%.

Saves approximately 4,000 lives over three months.

Achieves fairness and robustness in vaccine distribution.

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, non-convex 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 three-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.