论文标题
计划作为流行病学模型的推论
Planning as Inference in Epidemiological Models
论文作者
论文摘要
在这项工作中，我们演示了如何通过推断现有流行病学模型来自动化传染病控制决策过程的一部分。所执行的推理任务包括通过直接决策选择，模拟模型参数来计算可控制的模型参数，从而导致可接受的疾病进展结果。除其他外，我们说明了使用概率编程语言的使用，该语言可以自动化现有模拟器中的推断。目前，此工具的全部功能都没有广泛传播。及时了解基于模拟的模型和推理自动化工具如何用于支持决策可能会导致经济上损害的政策处方较少，尤其是在当前的COVID-19-19中大流行期间。
In this work we demonstrate how to automate parts of the infectious disease-control policy-making process via performing inference in existing epidemiological models. The kind of inference tasks undertaken include computing the posterior distribution over controllable, via direct policy-making choices, simulation model parameters that give rise to acceptable disease progression outcomes. Among other things, we illustrate the use of a probabilistic programming language that automates inference in existing simulators. Neither the full capabilities of this tool for automating inference nor its utility for planning is widely disseminated at the current time. Timely gains in understanding about how such simulation-based models and inference automation tools applied in support of policymaking could lead to less economically damaging policy prescriptions, particularly during the current COVID-19 pandemic.
