论文标题
polsird：在干预政策下建模流行病的扩散
PolSIRD: Modeling Epidemic Spread under Intervention Policies
论文作者
论文摘要
传统上，在没有任何干预政策的情况下代表疾病的自由演化，在人群中的流行病扩散是建模的。此外，这些模型假定疾病病例的完全可观察性，并且不解释不足。我们提出了一个数学模型，即Polsird，该模型通过引入观察机制来解释不足的报道。它还通过利用干预策略数据以及报告的疾病病例来捕获干预政策对疾病扩散参数的影响。此外，我们允许我们的经常性模型通过基于梯度的培训来学习端到端所有隔室的初始隐藏状态以及其他参数。我们将我们的模型应用于最近在美国Covid-19的近期全球爆发，在美国，我们的模型的表现优于CDC在预测扩散方面采用的方法。我们还提供了来自模型的反事实模拟，以过早地解除干预策略的效果，并正确预测流行病的第二波。
Epidemic spread in a population is traditionally modeled via compartmentalized models which represent the free evolution of disease in absence of any intervention policies. In addition, these models assume full observability of disease cases and do not account for under-reporting. We present a mathematical model, namely PolSIRD, which accounts for the under-reporting by introducing an observation mechanism. It also captures the effects of intervention policies on the disease spread parameters by leveraging intervention policy data along with the reported disease cases. Furthermore, we allow our recurrent model to learn the initial hidden state of all compartments end-to-end along with other parameters via gradient-based training. We apply our model to the spread of the recent global outbreak of COVID-19 in the United States, where our model outperforms the methods employed by the CDC in predicting the spread. We also provide counterfactual simulations from our model to analyze the effect of lifting the intervention policies prematurely and our model correctly predicts the second wave of the epidemic.
