Chang Zhai, Ping Chen, Zhuo Jin, David Pitt
arXiv 21 Nov 2025 · Statistics — Applications
arXiv:2511.16932 · PDF · DOI · OpenAlex · Extracted main text
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.
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The works this paper leans on most, across its whole bibliography — not restricted to papers in our corpus. Ranked by composite intensity, which combines how often a work is mentioned, how many sections mention it, and how much of that falls in the main text rather than the appendix.
| Reference | Intensity | Mentions | Sections | Main text | |
|---|---|---|---|---|---|
| 1 | Han, J. et al (2016) Deep learning approximation for stochastic control problems | 0.811 | 4 | 2 | 100% |
| 2 | Raissi, M., Perdikaris, P., and Karniadakis, G. E (2019) Physics-informed neural networks: A deep learning framework for solving forward and inverse problems involving nonlinear partial… | 0.737 | 3 | 2 | 100% |
| 3 | Shaier, S., Raissi, M., and Seshaiyer, P (2021) Data-driven approaches for predicting spread of infectious diseases through dinns: Disease informed neural networks | 0.737 | 3 | 2 | 100% |
| 4 | Acemoglu, D., Chernozhukov, V., Werning, I., and Whinston, M. D (2021) Optimal targeted lockdowns in a multigroup sir model | 0.644 | 2 | 2 | 100% |
| 5 | Adak, D., Majumder, A., and Bairagi, N (2021) Mathematical perspective of covid-19 pandemic: Disease extinction criteria in deterministic and stochastic models | 0.644 | 2 | 2 | 100% |
| 6 | Amato, M., Werba, J. P., Frigerio, B., Coggi, D., Sansaro, D., Ravan… (2020) Relationship between influenza vaccination coverage rate and covid-19 outbreak: an italian ecological study | 0.644 | 2 | 2 | 100% |
| 7 | Bellman, R. E. and Dreyfus, S. E (2015) Applied dynamic programming, volume 2050 | 0.644 | 2 | 2 | 100% |
| 8 | Acemoglu, D., Fallah, A., Giometto, A., Huttenlocher, D., Ozdaglar,… (2024) Optimal adaptive testing for epidemic control: combining molecular and serology tests | 0.405 | 1 | 1 | 100% |
| 9 | Acuña-Zegarra, M. A., Dáz-Infante, S., Baca-Carrasco, D., and Olmos-… (2021) Covid-19 optimal vaccination policies: A modeling study on efficacy, natural and vaccine-induced immunity responses | 0.405 | 1 | 1 | 100% |
| 10 | Allen, L. J (2008) An introduction to stochastic epidemic models | 0.405 | 1 | 1 | 100% |
Showing the top 10 of 69 scored citations.