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Deploying vaccine distribution sites for improved accessibility and equity to support pandemic response

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Abstract

In response to COVID-19, many countries have mandated social distancing and banned large group gatherings in order to slow down the spread of SARS-CoV-2. These social interventions along with vaccines remain the best way forward to reduce the spread of SARS CoV-2. In order to increase vaccine accessibility, states such as Virginia have deployed mobile vaccination centers to distribute vaccines across the state. When choosing where to place these sites, there are two important factors to take into account: accessibility and equity. We formulate a combinatorial problem that captures these factors and then develop efficient algorithms with theoretical guarantees on both of these aspects. Furthermore, we study the inherent hardness of the problem, and demonstrate strong impossibility results. Finally, we run computational experiments on real-world data to show the efficacy of our methods.

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Data Availability

All data and code for the experiments can be found at https://github.com/Ann924/MobileFacility.

Notes

  1. https://github.com/gzli929/MobileVaccClinic.

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Acknowledgements

We express our sincere thanks to the referees for suggesting the experiments in Sect. 6.5 and the extension of capacity constraints. We also thank members of the Biocomplexity COVID-19 Response Team and the Network Systems Science and Advanced Computing (NSSAC) Division for their thoughtful comments and suggestions related to epidemic modeling and response support. George Li, Aravind Srinivasan, and Leonidas Tsepenekas were supported in part by NSF award number CCF-1918749. Ann Li, Madhav Marathe, and Anil Vullikanti were supported by DTRA (Contract HDTRA1-19-D-0007), University of Virginia Strategic Investment Fund award number SIF160, National Institutes of Health (NIH) Grants 1R01GM109718, 2R01GM109718, OAC-1916805 (CINES), CCF-1918656 (Expeditions), CNS-2028004 (RAPID), OAC-2027541 (RAPID), IIS-1908530, IIS-1955797, and IIS-2027848. The U.S. Government is authorized to reproduce and distribute reprints for Governmental purposes notwithstanding any copyright annotation thereon.

Funding

George Li, Aravind Srinivasan, and Leonidas Tsepenekas were supported in part by NSF award number CCF-1918749. Ann Li, Madhav Marathe, and Anil Vullikanti were supported by DTRA (Contract HDTRA1-19-D-0007), University of Virginia Strategic Investment Fund award number SIF160, National Institutes of Health (NIH) Grants 1R01GM109718, 2R01GM109718, OAC-1916805 (CINES), CCF-1918656 (Expeditions), CNS-2028004 (RAPID), OAC-2027541 (RAPID), IIS-1908530, IIS-1955797, and IIS-2027848.

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Authors and Affiliations

  1. Computer Science, University of Maryland, College Park, USA

    George Z. Li, Aravind Srinivasan & Leonidas Tsepenekas

  2. Computer Science, University of Virginia, Charlottesville, USA

    Ann Li, Madhav Marathe & Anil Vullikanti

  3. Biocomplexity Institute, University of Virginia, Charlottesville, USA

    Ann Li, Madhav Marathe & Anil Vullikanti

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  1. George Z. Li
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  2. Ann Li
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Contributions

AS and MM and AV came up with the problem formulation. GL and LT designed the algorithms. LT proved the hardness results. GL and AL wrote the code and generated the figures. All authors contributed to writing and reviewing the manuscript.

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Correspondence to George Z. Li.

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Li, G.Z., Li, A., Marathe, M. et al. Deploying vaccine distribution sites for improved accessibility and equity to support pandemic response. Auton Agent Multi-Agent Syst 37, 31 (2023). https://doi.org/10.1007/s10458-023-09614-9

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