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| Auteurs principaux: | , |
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| Format: | Preprint |
| Publié: |
2024
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| Sujets: | |
| Accès en ligne: | https://arxiv.org/abs/2409.11631 |
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| _version_ | 1866929522228592640 |
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| author | Gestetner, Ari Say, Buser |
| author_facet | Gestetner, Ari Say, Buser |
| contents | A pandemic is the spread of a disease across large regions, and can have devastating costs to the society in terms of health, economic and social. As such, the study of effective pandemic mitigation strategies can yield significant positive impact on the society. A pandemic can be mathematically described using a compartmental model, such as the Susceptible Infected Removed (SIR) model. In this paper, we extend the solution equations of the SIR model to a state transition model with lockdowns. We formalize a metric hybrid planning problem based on this state transition model, and solve it using a metric hybrid planner. We improve the runtime effectiveness of the metric hybrid planner with the addition of valid inequalities, and demonstrate the success of our approach both theoretically and experimentally under various challenging settings. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2409_11631 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | A Metric Hybrid Planning Approach to Solving Pandemic Planning Problems with Simple SIR Models Gestetner, Ari Say, Buser Artificial Intelligence A pandemic is the spread of a disease across large regions, and can have devastating costs to the society in terms of health, economic and social. As such, the study of effective pandemic mitigation strategies can yield significant positive impact on the society. A pandemic can be mathematically described using a compartmental model, such as the Susceptible Infected Removed (SIR) model. In this paper, we extend the solution equations of the SIR model to a state transition model with lockdowns. We formalize a metric hybrid planning problem based on this state transition model, and solve it using a metric hybrid planner. We improve the runtime effectiveness of the metric hybrid planner with the addition of valid inequalities, and demonstrate the success of our approach both theoretically and experimentally under various challenging settings. |
| title | A Metric Hybrid Planning Approach to Solving Pandemic Planning Problems with Simple SIR Models |
| topic | Artificial Intelligence |
| url | https://arxiv.org/abs/2409.11631 |