Optimising pandemic response through vaccination strategies using neural networks

TL;DR

This paper introduces a neural network-based framework for optimizing vaccination strategies during pandemics, balancing health outcomes and economic costs through a data-driven, adaptable decision support tool.

Contribution

It presents a novel economic-epidemiological framework that integrates neural networks to calibrate models and optimize vaccination strategies in real-time.

Findings

Empirically derived vaccination strategies for COVID-19 in Victoria, Australia.

Framework effectively balances disease control and economic expenditure.

Supports continuous policy adjustment based on evolving data.

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.