Simulating the spread of infectious diseases in human communities is critical for predicting the trajectory of an epidemic and verifying various policies to control the devastating impacts of the outbreak. Many existing simulators are based on compartment models that divide people into a few subsets and simulate the dynamics among those subsets using hypothesized differential equations. However, these models lack the requisite granularity to study the effect of intelligent policies that influence every individual in a particular way. In this work, we introduce a simulator software capable of modeling a population structure and controlling the disease's propagation at an individualistic level. In order to estimate the confidence of the conclusions drawn from the simulator, we employ a comprehensive probabilistic approach where the entire population is constructed as a hierarchical random variable. This approach makes the inferred conclusions more robust against sampling artifacts and gives confidence bounds for decisions based on the simulation results. To showcase potential applications, the simulator parameters are set based on the formal statistics of the COVID-19 pandemic, and the outcome of a wide range of control measures is investigated. Furthermore, the simulator is used as the environment of a reinforcement learning problem to find the optimal policies to control the pandemic. The obtained experimental results indicate the simulator's adaptability and capacity in making sound predictions and a successful policy derivation example based on real-world data. As an exemplary application, our results show that the proposed policy discovery method can lead to control measures that produce significantly fewer infected individuals in the population and protect the health system against saturation.
翻译:模拟人类社区传染病的蔓延对于预测流行病的轨迹和核实控制疾病蔓延的毁灭性影响的各种政策至关重要。许多现有的模拟器基于将人分成几个子集的隔间模型,并使用假设的差别方程式模拟这些子集的动态。然而,这些模型缺乏必要的微粒性来研究以特定方式影响每个人的智能政策的影响。在这项工作中,我们引入了一个模拟软件,能够模拟人口结构并控制疾病在个人层面的传播。为了估计从模拟器中得出的结论的可信度,我们采用了一种全面的概率方法,将整个人口分成几个子集,并用假设式差异方程式模拟器模拟这些子组的动态。这种方法使推论结论更有力地针对抽样工艺,并为根据模拟结果对每个人产生影响的智能政策带来信心。为了展示潜在应用,我们根据COVID-19流行病的正式统计数据设定了模拟参数,并调查了广泛范围的控制措施的结果。此外,为了评估从模拟器中得出的结论,我们采用了一种全面的概率方法,即对整个人口进行成功的递增政策进行推算。这个模型用来说明基于正确的实验方法的实验性政策结果,以研究一个实验性方法来发现一个精确的精确的健康状况控制。