Learning Fair Policies for Infectious Diseases Mitigation using Path Integral Control
Fuente:
arXiv
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| Autores principales: | , , , |
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| Formato: | Preprint |
| Publicado: |
2025
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| Materias: | |
| Acceso en línea: | |
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| _version_ | 1866912232012513280 |
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| author | Jia, Zhuangzhuang Park, Hyuk Dayanıklı, Gökçe Hanasusanto, Grani A. |
| author_facet | Jia, Zhuangzhuang Park, Hyuk Dayanıklı, Gökçe Hanasusanto, Grani A. |
| contents | Infectious diseases pose major public health challenges to society, highlighting the importance of designing effective policies to reduce economic loss and mortality. In this paper, we propose a framework for sequential decision-making under uncertainty to design fairness-aware disease mitigation policies that incorporate various measures of unfairness. Specifically, our approach learns equitable vaccination and lockdown strategies based on a stochastic multi-group SIR model. To address the challenges of solving the resulting sequential decision-making problem, we adopt the path integral control algorithm as an efficient solution scheme. Through a case study, we demonstrate that our approach effectively improves fairness compared to conventional methods and provides valuable insights for policymakers. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2502_09831 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Learning Fair Policies for Infectious Diseases Mitigation using Path Integral Control Jia, Zhuangzhuang Park, Hyuk Dayanıklı, Gökçe Hanasusanto, Grani A. Machine Learning Optimization and Control Infectious diseases pose major public health challenges to society, highlighting the importance of designing effective policies to reduce economic loss and mortality. In this paper, we propose a framework for sequential decision-making under uncertainty to design fairness-aware disease mitigation policies that incorporate various measures of unfairness. Specifically, our approach learns equitable vaccination and lockdown strategies based on a stochastic multi-group SIR model. To address the challenges of solving the resulting sequential decision-making problem, we adopt the path integral control algorithm as an efficient solution scheme. Through a case study, we demonstrate that our approach effectively improves fairness compared to conventional methods and provides valuable insights for policymakers. |
| title | Learning Fair Policies for Infectious Diseases Mitigation using Path Integral Control |
| topic | Machine Learning Optimization and Control |
| url | https://arxiv.org/abs/2502.09831 |