COVID-19: Agent-based simulation-optimization to vaccine center location vaccine allocation problem

Abstract

This article presents an agent-based simulation-optimization modeling and algorithmic framework to determine the optimal vaccine center location and vaccine allocation strategies under budget constraints during an epidemic outbreak. Both simulation and optimization models incorporate population health dynamics, such as susceptible (S), vaccinated (V), infected (I) and recovered (R), while their integrated utilization focuses on the COVID-19 vaccine allocation challenges. We first formulate a dynamic location–allocation Mixed-Integer Programming (MIP) model, which determines the optimal vaccination center locations and vaccines allocated to vaccination centers, pharmacies, and health centers in a multi-period setting in each region over a geographical location. We then extend the agent-based epidemiological simulation model of COVID-19 (Covasim) by adding new vaccination compartments representing people who take the first vaccine shot and the first two shots. The Covasim involves complex disease transmission contact networks, including households, schools, and workplaces, and demographics, such as age-based disease transmission parameters. We combine the extended Covasim with the vaccination center location-allocation MIP model into one single simulation-optimization framework, which works iteratively forward and backward in time to determine the optimal vaccine allocation under varying disease dynamics. The agent-based simulation captures the inherent uncertainty in disease progression and forecasts the refined number of susceptible individuals and infections for the current time period to be used as an input into the optimization. We calibrate, validate, and test our simulation-optimization vaccine allocation model using the COVID-19 data and vaccine distribution case study in New Jersey. The resulting insights support ongoing mass vaccination efforts to mitigate the impact of the pandemic on public health, while the simulation-optimization algorithmic framework could be generalized for other epidemics.

Keywords:

• Agent-based simulation
• optimization
• vaccination center facility location
• vaccine allocation
• vaccine distribution
• mixed-integer programming
• COVID-19
• SIR model
• epidemiological model
• supply chain and logistics

Additional information

Funding

The authors acknowledge the generous support from the National Science Foundation CAREER Award co-funded by the CBET/ENG Environmental Sustainability program and the Division of Mathematical Sciences in MPS/NSF under Grant No. CBET-1554018.

Notes on contributors

Xuecheng Yin

Xuecheng (Ethan) Yin, PhD is an assistant professor in the Department of Management Science & Information Systems (MSIS) at Oklahoma State University Spears School of Business. His research focuses on resource allocation in health care and public health and the model-based evaluation of health policies. His dissertation addresses resource allocation issues during the COVID-19 pandemic and Ebola outbreaks, using simulation and stochastic optimization models to inform allocation decisions in real-time while accounting for various sources of uncertainties in the future. He has been funded by CDC. He also works on multiple NSF- and NIH-funded projects. The main objective of his current work is to develop decision models to project the long-term population impact and cost of different public health strategies to extend the lifespan of antibiotics. These models allow policymakers to translate data from surveillance systems of antimicrobial-resistant gonorrhea into evidence-based and cost-effective recommendations for treating gonorrhea. He collaborates with several interdisciplinary teams around the world.

Sabah Bushaj

Sabah Bushaj, PhD is an assistant professor of business analytics at the State University of New York at Plattsburgh. He earned his PhD in industrial engineering from the New Jersey Institute of Technology (NJIT) in August 2021, focusing on multi-stage stochastic optimization and reinforcement learning for forestry and epidemic control planning. His research interests include using learning algorithms to address uncertainty in decision analysis, epidemic disease modeling in forestry, farming, and human population, and economic damage modeling of invasive species.

Yue Yuan

Yue Yuan, PhD is a research associate at Altfest Personal Wealth Management company. Her research applies the consumer utility theory to the capital asset pricing model. She develops the consumption capital asset pricing model with an application of econometric methods to analyze the relationship between consumption and the stock market. She received a PhD degree in economics from Lehigh University in May 2019. Her dissertation focuses on applying business analytics, such as empirical econometric models, in studying online luxury goods marketing and the impact of social effects on consumer behavior. Her current research also includes the topics of resource allocation in public health and health economics in aging problems.

İ. Esra Büyüktahtakın

İ. Esra Büyüktahtakιn Toy, PhD  is an associate professor in the Grado Department of Industrial and Systems Engineering at Virginia Tech. Dr. Büyüktahtakιn Toy’s research focuses on advancing the state-of-the-art in multi-stage stochastic combinatorial optimization with a mix of theory and algorithms. Dr. Büyüktahtakιn Toy is a national leader in epidemic disease modeling and logistics optimization to tackle epidemic diseases in healthcare, agriculture, and forestry. Examples of applications include infectious diseases that ravage the human body, such as COVID-19, Ebola virus disease (EVD), and HIV, and invasive species that create havoc on forests, such as the emerald ash borer (EAB) in North America and Canada, Sericea Lespedeza damaging agricultural products in the Great Plains, and flammable buffelgrass in the Sonoran Desert. Her work helped policymakers optimize resource allocation strategies to fight harmful invasions impacting human and environmental health. Dr. Büyüktahtakιn Toy is the recipient of the 2016 NSF CAREER Award. She has also been awarded various projects by the United States Department of Agriculture (USDA) and the U.S. Forest Service. She has published her research in 40 flagship journals in Operations Research. She has taken leadership roles in INFORMS JFIG and is the associate editor for the Springer Nature Operations Research Forum journal.