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Optimizing Vaccine Site Locations While Considering Travel Inconvenience and Public Health Outcomes

Suyanpeng Zhang, Sze-chuan Suen, Han Yu, Maged Dessouky, Fernando Ordonez

TL;DR

The paper tackles vaccine-site location optimization during COVID-19 by integrating commuting-pattern informed travel inconvenience, disease dynamics, and equitable vaccine distribution within a tractable multi-objective MILP. The approach introduces herd-immunity targets as a health proxy, derives district-level vaccination goals, and employs a detailed compartmental model to evaluate infections averted. Key findings show dispersed mega-sites can substantially reduce travel burden and increase infections averted relative to empirical LAC locations, with equity considerations further refining allocations. Practically, the framework informs policy decisions on site placement and allocation in ongoing or future vaccination campaigns, balancing accessibility, health impact, and fairness.

Abstract

During the COVID-19 pandemic, there were over three million infections in Los Angeles County (LAC). To facilitate distribution when vaccines first became available, LAC set up six mega-sites for dispensing a large number of vaccines to the public. To understand if another choice of mega-site location would have improved accessibility and health outcomes, and to provide insight into future vaccine allocation problems, we propose a multi-objective mixed integer linear programming model that balances travel convenience, infection reduction, and equitable distribution. We provide a tractable objective formulation that effectively proxies real-world public health goals of reducing infections while considering travel inconvenience and equitable distribution of resources. Compared with the solution empirically used in LAC in 2020, we recommend more dispersed mega-site locations that result in a 28% reduction in travel inconvenience and avert an additional 1,000 infections.

Optimizing Vaccine Site Locations While Considering Travel Inconvenience and Public Health Outcomes

TL;DR

The paper tackles vaccine-site location optimization during COVID-19 by integrating commuting-pattern informed travel inconvenience, disease dynamics, and equitable vaccine distribution within a tractable multi-objective MILP. The approach introduces herd-immunity targets as a health proxy, derives district-level vaccination goals, and employs a detailed compartmental model to evaluate infections averted. Key findings show dispersed mega-sites can substantially reduce travel burden and increase infections averted relative to empirical LAC locations, with equity considerations further refining allocations. Practically, the framework informs policy decisions on site placement and allocation in ongoing or future vaccination campaigns, balancing accessibility, health impact, and fairness.

Abstract

During the COVID-19 pandemic, there were over three million infections in Los Angeles County (LAC). To facilitate distribution when vaccines first became available, LAC set up six mega-sites for dispensing a large number of vaccines to the public. To understand if another choice of mega-site location would have improved accessibility and health outcomes, and to provide insight into future vaccine allocation problems, we propose a multi-objective mixed integer linear programming model that balances travel convenience, infection reduction, and equitable distribution. We provide a tractable objective formulation that effectively proxies real-world public health goals of reducing infections while considering travel inconvenience and equitable distribution of resources. Compared with the solution empirically used in LAC in 2020, we recommend more dispersed mega-site locations that result in a 28% reduction in travel inconvenience and avert an additional 1,000 infections.
Paper Structure (31 sections, 15 equations, 10 figures, 3 tables, 1 algorithm)

This paper contains 31 sections, 15 equations, 10 figures, 3 tables, 1 algorithm.

Table of Contents

  1. Introduction
  2. Literature Review
  3. Contributions
  4. Model Formulation
  5. Minimization of Travel Inconvenience Considering Commuting Patterns
  6. Modeling Disease Dynamics
  7. Compartmental Model
  8. Herd Immunity Threshold Analysis
  9. Equitable Distribution of Vaccines
  10. An Multi-Objective Model
  11. Numerical Example
  12. Model Inputs
  13. Commuters and Non-commuters
  14. Parameterization of Travel Cost Matrix
  15. Herd Immunity Threshold
  16. ...and 16 more sections

Figures (10)

  • Figure 1: Estimation of daily traffic flow using PeMS data. Each entry represents the number of vehicles going from an origin HD (row) to a destination HD (column). A darker color indicates a higher volume of traffic. We assume individuals live in the origin HD and work in the destination HD.
  • Figure 2: The compartmental model for modeling COVID-19 disease dynamics. Greek letters represent rates of flow between states over time.
  • Figure 3: A map of 26 health districts in LAC, with HDs with mega-sites selected by P0 marked in red and green, HDs with mega-sites selected by P1 marked in blue and green, and HDs without mega-sites marked in yellow.
  • Figure 4: Total travel inconvenience by each mega-sites over 6 months. The total travel inconvenience is zero for individuals assigned to West Valley for P0 as it assigns only individuals who live in West Valley to mega-site in West Valley.
  • Figure 5: Changes in the number of infections averted with varying $\lambda$.
  • ...and 5 more figures