Optimising pandemic response through vaccination strategies using neural networks

2025-11-20

AI Summary

This study addresses the trade-off between public health outcomes and economic costs in pandemic vaccine allocation strategies. Method: We propose a dynamic optimization framework integrating epidemiological and economic modeling, structured into three phases: (1) stochastic compartmental modeling of disease transmission; (2) formulation of a multi-objective optimal control problem—minimizing both cumulative infections and government expenditure; and (3) neural-network-based approximation of high-dimensional, nonlinear optimal control policies, coupled with data-driven parameter calibration. Contribution/Results: Evaluated on real-world COVID-19 data from Victoria, Australia, the framework yields vaccination strategies that significantly reduce both infection incidence and fiscal burden, achieving approximately 23% total cost savings over baseline policies. The approach supports real-time policy adaptation, enhancing the flexibility and scalability of evidence-informed decision-making during pandemics.

Technology Category

• Search and Optimization: Sampling/Simulation-based Search
• Reasoning under Uncertainty: Stochastic Optimization
• Constraint Satisfaction and Optimization: Constraint Optimization

Application Category

• Economics, Online Markets and Human Computation: Incentives in network design for Web infrastructures and ecosystems
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• User Modeling, Personalization and Recommendation: Accountability, Transparency, and Ethics for personalization

Abstract

Epidemic risk assessment poses inherent challenges, with traditional approaches often failing to balance health outcomes and economic constraints. This paper presents a data-driven decision support tool that models epidemiological dynamics and optimises vaccination strategies to control disease spread whilst minimising economic losses. The proposed economic-epidemiological framework comprises three phases: modelling, optimising, and analysing. First, a stochastic compartmental model captures epidemic dynamics. Second, an optimal control problem is formulated to derive vaccination strategies that minimise pandemic-related expenditure. Given the analytical intractability of epidemiological models, neural networks are employed to calibrate parameters and solve the high-dimensional control problem. The framework is demonstrated using COVID-19 data from Victoria, Australia, empirically deriving optimal vaccination strategies that simultaneously minimise disease incidence and governmental expenditure. By employing this three-phase framework, policymakers can adjust input values to reflect evolving transmission dynamics and continuously update strategies, thereby minimising aggregate costs, aiding future pandemic preparedness.

Problem

• Optimizing vaccination strategies to control disease spread during pandemics
• Balancing health outcomes with economic constraints in epidemic response
• Solving high-dimensional control problems using neural networks for policy decisions

Innovation

• Neural networks calibrate epidemiological model parameters
• Optimal control minimizes disease spread and economic costs
• Three-phase framework enables dynamic vaccination strategy updates