Optimising Lockdown Policies for Epidemic Control using Reinforcement Learning: An AI-Driven Control Approach Compatible with Existing Disease and Network Models
Optimising Lockdown Policies for Epidemic Control using Reinforcement Learning: An AI-Driven Control Approach Compatible with Existing Disease and Network Models
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DOI：
10.1007/s41403-020-00129-3
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发表时间：
2020-06-24
期刊：
影响因子：
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通讯作者：
Seetharam DP
中科院分区：
文献类型：
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作者：
Khadilkar H;Ganu T;Seetharam DP
关于在 Covid-19 背景下采取封锁政策以限制对健康和经济的损害，存在着激烈的争论。我们提出了一种人工智能驱动的方法，用于生成最佳的锁定政策，控制疾病的传播，同时平衡健康和经济成本。此外，所提出的强化学习方法会根据疾病和人口参数自动学习这些策略。该方法考虑了不完美的锁定，可用于探索一系列使用可调参数的策略，并且可以轻松扩展到细粒度的锁定严格性。该控制方法可与任何兼容的疾病和网络模拟模型一起使用。
There has been intense debate about lockdown policies in the context of Covid-19 for limiting damage both to health and to the economy. We present an AI-driven approach for generating optimal lockdown policies that control the spread of the disease while balancing both health and economic costs. Furthermore, the proposed reinforcement learning approach automatically learns those policies, as a function of disease and population parameters. The approach accounts for imperfect lockdowns, can be used to explore a range of policies using tunable parameters, and can be easily extended to fine-grained lockdown strictness. The control approach can be used with any compatible disease and network simulation models.