基于深度确定性策略梯度的大规模智能体流行病学模型中的疫情控制
Epidemic Control on a Large-Scale-Agent-Based Epidemiology Model using Deep Deterministic Policy Gradient
AI总结：
本研究利用深度确定性策略梯度（DDPG）在10万个体的大规模智能体流行病学模拟中实现多目标优化，确定封锁与疫苗接种的最优策略，以平衡经济与健康目标。
摘要：
为减轻大流行的影响，多项措施包括封锁、快速疫苗接种计划、学校关闭和经济刺激。这些干预措施可能产生积极或意外的负面后果。当前通过往返迭代自动建模并确定最优干预措施的研究受到模拟目标、规模（数千个体）、不适合干预研究的模型类型以及它们可探索的干预策略数量（离散与连续）的限制。我们使用基于深度确定性策略梯度（DDPG）的策略优化框架，在包含100,000个个体的流行病学智能体模拟中解决这些挑战，并进行多目标优化。我们在一个简约的按年龄分层、多疫苗场景中，结合基本的经济活动模拟，确定封锁和疫苗接种的最优策略。在没有封锁和疫苗接种（中年和老年）的情况下，结果显示最优经济（贫困线以下的个体）与平衡的健康目标（感染和住院）。需要进行深入模拟以进一步验证我们的结果，并将我们的框架开源。
英文摘要：
To mitigate the impact of the pandemic, several measures include lockdowns, rapid vaccination programs, school closures, and economic stimulus. These interventions can have positive or unintended negative consequences. Current research to model and determine an optimal intervention automatically through round-tripping is limited by the simulation objectives, scale (a few thousand individuals), model types that are not suited for intervention studies, and the number of intervention strategies they can explore (discrete vs continuous). We address these challenges using a Deep Deterministic Policy Gradient (DDPG) based policy optimization framework on a large-scale (100,000 individual) epidemiological agent-based simulation where we perform multi-objective optimization. We determine the optimal policy for lockdown and vaccination in a minimalist age-stratified multi-vaccine scenario with a basic simulation for economic activity. With no lockdown and vaccination (mid-age and elderly), results show optimal economy (individuals below the poverty line) with balanced health objectives (infection, and hospitalization). An in-depth simulation is needed to further validate our results and open-source our framework.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
