{"ID":2838426,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2511.16932","arxiv_id":"2511.16932","title":"Optimising pandemic response through vaccination strategies using neural networks","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.","short_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...","url_abs":"https://arxiv.org/abs/2511.16932","url_pdf":"https://arxiv.org/pdf/2511.16932v1","authors":"[\"Chang Zhai\",\"Ping Chen\",\"Zhuo Jin\",\"David Pitt\"]","published":"2025-11-21T03:56:03Z","proceeding":"stat.AP","tasks":"[\"stat.AP\",\"econ.EM\",\"math.OC\"]","methods":"[]","has_code":false}