Using Supervised Learning to Optimize Multi-objective Infectious Disease Control

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Poster_xu4015.pdf (2.24 MB)

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2023-03

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Abstract

Since Non-pharmaceutical interventions (NPIs) are the key tools that we have for combatting outbreaks and they often have substantial costs, it is important to investigate a better policy that can balance the cost and the effectiveness. We believe supervised learning can help us to find a better disease control policy that will directly reduce the infected rate of SARS-CoV-2 with an affordable cost. Additionally, optimizing contact tracing policy can be viewed as a multi-objective reinforcement learning problem and we show the potential of using reinforcement learning for this optimization problem.

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Engineering and Technology (The Ohio State University Denman Undergraduate Research Forum)

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http://hdl.handle.net/1811/102717

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28th Denman Undergraduate Research Forum (2023)