Logo
ScienceStack
Table of Contents
Fetching ...
Sign In

A Multi-echelon Demand-driven Supply Chain Model for Proactive Optimal Control of Epidemics: Insights from a COVID-19 Study

Kimiya Jozani, Nihal A. Sageer, Hode Eldardiry, Sait Tunc, Esra Buyuktahtakin Toy

TL;DR

This paper addresses the challenge of controlling epidemics while ensuring equitable vaccine distribution by integrating SVIR-type epidemic dynamics with a multi-echelon vaccine supply chain. It introduces two equity-aware formulations—a Gini-based multiobjective model and a knapsack-based model with region-specific vulnerability weights—coupled with two scalable, Benders-inspired decompositions to enable national-scale planning. Through calibration on COVID-19 data and SARIMA-based validation, the study shows that a data-driven, need-based allocation can substantially reduce infections (approximately two million cases over six months in the U.S.) and improve vaccine access in underserved regions, outperforming myopic policies. The framework offers policymakers a scalable, operational tool for proactive, equitable epidemic preparedness and response that can adapt to future outbreaks beyond COVID-19.

Abstract

Timely and effective decision-making is critical during epidemics to reduce preventable infections and deaths. This demands integrated models that jointly capture disease dynamics, vaccine distribution, regional disparities, and behavioral responses. However, most existing approaches decouple epidemic forecasting from logistics planning, hindering adaptive and regionally responsive interventions. We propose a novel epidemiological-optimization framework that jointly models epidemic progression and a multiscale vaccine supply chain. The model incorporates spatio-temporally varying effective infection rates to reflect regional policy and behavioral dynamics. It supports coordinated, data-driven decision-making across spatial scales through two formulations: a multi-objective Gini-based model and a knapsack-based model that leverages regional vulnerability indicators for tractability and improved mitigation. To address computational complexity, we design two scalable heuristic decomposition algorithms inspired by the Benders decomposition. The model is validated using COVID-19 data in the U.S.. We introduce SARIMA-based forecasting as a novel approach for validating epidemic-optimization models under data limitations. The results show that our approach can prevent more than 2 million infections and 30,000 deaths in just six months while significantly improving the accessibility of vaccines in underserved regions. Our framework demonstrates that integrating fairness and epidemic dynamics with vaccine logistics leads to superior outcomes compared to traditional myopic policies. Fairness improves overall efficiency in the long term by prioritizing the most vulnerable populations, leading to better long-term public health outcomes. The model offers policymakers a scalable and operationally relevant tool to strengthen preparedness and ensure a more effective and equitable response to epidemics.

A Multi-echelon Demand-driven Supply Chain Model for Proactive Optimal Control of Epidemics: Insights from a COVID-19 Study

TL;DR

This paper addresses the challenge of controlling epidemics while ensuring equitable vaccine distribution by integrating SVIR-type epidemic dynamics with a multi-echelon vaccine supply chain. It introduces two equity-aware formulations—a Gini-based multiobjective model and a knapsack-based model with region-specific vulnerability weights—coupled with two scalable, Benders-inspired decompositions to enable national-scale planning. Through calibration on COVID-19 data and SARIMA-based validation, the study shows that a data-driven, need-based allocation can substantially reduce infections (approximately two million cases over six months in the U.S.) and improve vaccine access in underserved regions, outperforming myopic policies. The framework offers policymakers a scalable, operational tool for proactive, equitable epidemic preparedness and response that can adapt to future outbreaks beyond COVID-19.

Abstract

Timely and effective decision-making is critical during epidemics to reduce preventable infections and deaths. This demands integrated models that jointly capture disease dynamics, vaccine distribution, regional disparities, and behavioral responses. However, most existing approaches decouple epidemic forecasting from logistics planning, hindering adaptive and regionally responsive interventions. We propose a novel epidemiological-optimization framework that jointly models epidemic progression and a multiscale vaccine supply chain. The model incorporates spatio-temporally varying effective infection rates to reflect regional policy and behavioral dynamics. It supports coordinated, data-driven decision-making across spatial scales through two formulations: a multi-objective Gini-based model and a knapsack-based model that leverages regional vulnerability indicators for tractability and improved mitigation. To address computational complexity, we design two scalable heuristic decomposition algorithms inspired by the Benders decomposition. The model is validated using COVID-19 data in the U.S.. We introduce SARIMA-based forecasting as a novel approach for validating epidemic-optimization models under data limitations. The results show that our approach can prevent more than 2 million infections and 30,000 deaths in just six months while significantly improving the accessibility of vaccines in underserved regions. Our framework demonstrates that integrating fairness and epidemic dynamics with vaccine logistics leads to superior outcomes compared to traditional myopic policies. Fairness improves overall efficiency in the long term by prioritizing the most vulnerable populations, leading to better long-term public health outcomes. The model offers policymakers a scalable and operationally relevant tool to strengthen preparedness and ensure a more effective and equitable response to epidemics.
Paper Structure (42 sections, 1 theorem, 24 equations, 13 figures, 1 table, 1 algorithm)

This paper contains 42 sections, 1 theorem, 24 equations, 13 figures, 1 table, 1 algorithm.

Table of Contents

  1. Introduction
  2. Literature Review
  3. Contributions and Innovations
  4. Integration of Epidemic Dynamics and Vaccine Supply Chain
  5. Model Assumptions and Features
  6. Mathematical Model Formulation
  7. Notation and Parameters
  8. Gini-based Formulation
  9. Knapsack-based Formulation
  10. Methodology
  11. Knapsack-based Decomposition
  12. Case Study, Data Calibration, and Effective Infection Rate
  13. Estimating Infection Rates
  14. Computing Knapsack Weights
  15. Case Study, Model Validation and Generalizability
  16. ...and 27 more sections

Key Result

Proposition 1

The optimal solution produced by the Knapsack-based Decomposition dec2-objective1-eq1.1--dec2-objective3-eq2.1.3 is a feasible solution to the Knapsack-based Formulation eq2.2--eq7.1.

Figures (13)

  • Figure 1: Proposed Policy-informed Integrated Epidemic-Resource Supply Chain Model
  • Figure 2: Temporal Effective infection rate in 50 states and DC
  • Figure 3: County-level knapsack weights
  • Figure 4: Disparities between actual and model-based vaccine allocations under state-level caps, shown in millions of doses. Positive values (+) indicate overallocation (blue), and negative values (–) indicate underallocation (yellow).
  • Figure 5: Multi-layer Supply Chain Model
  • ...and 8 more figures

Theorems & Definitions (1)

  • Proposition 1