论文标题
COVID-19和通过政策干预进行控制的隔室模型
Compartmental Models for COVID-19 and Control via Policy Interventions
论文作者
论文摘要
我们展示了一种使用概率编程语言工具包（PPLS）的工具包，以复制和预测SARS-COV-2（COVID-19）流行的传播方法。我们的目标是研究各种建模假设的影响，并激励制定的政策干预措施以限制传染病的传播。使用现有的隔室模型，我们展示了如何在PPL中使用推断以获得疾病参数的后验估计。我们改进了流行的现有模型，以反映实际的考虑因素，例如对COVID-19案例的真实数量不足，并激发了对现实世界数据进行策略干预措施的需求。我们将SEI3RD模型设计为可重复使用的模板，并与其他模型相比演示了其灵活性。我们还提供了一种贪婪的算法，该算法选择了一系列最佳的政策干预措施，这些干预措施可能控制受到约束的受感染人群。我们在一个简单，模块化且可重复的框架中工作，以使概率推断的最新访问能够立即跨域访问，重点是对政策干预的重视。我们不是流行病学家。这项研究的唯一目的是作为方法的解释，而不是直接推断出对Covid-19的政策制定的现实影响。
We demonstrate an approach to replicate and forecast the spread of the SARS-CoV-2 (COVID-19) pandemic using the toolkit of probabilistic programming languages (PPLs). Our goal is to study the impact of various modeling assumptions and motivate policy interventions enacted to limit the spread of infectious diseases. Using existing compartmental models we show how to use inference in PPLs to obtain posterior estimates for disease parameters. We improve popular existing models to reflect practical considerations such as the under-reporting of the true number of COVID-19 cases and motivate the need to model policy interventions for real-world data. We design an SEI3RD model as a reusable template and demonstrate its flexibility in comparison to other models. We also provide a greedy algorithm that selects the optimal series of policy interventions that are likely to control the infected population subject to provided constraints. We work within a simple, modular, and reproducible framework to enable immediate cross-domain access to the state-of-the-art in probabilistic inference with emphasis on policy interventions. We are not epidemiologists; the sole aim of this study is to serve as an exposition of methods, not to directly infer the real-world impact of policy-making for COVID-19.
