  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.
