论文标题
半参数贝叶斯对COVID-19的传输动力学推断具有状态空间模型
Semiparametric Bayesian Inference for the Transmission Dynamics of COVID-19 with a State-Space Model
论文作者
论文摘要
2019年冠状病毒病（COVID-19）爆发是一场持续的大流行，影响了200多个国家和地区。关于COVID-19的传播动力学的推断可以为疾病传播速度和缓解政策的影响提供重要的见解。我们使用每日确认的COVID-19病例的数据基于概率隔室模型开发了一种新型的贝叶斯方法来进行这种推理。特别是，我们考虑了经典易感性感染模型的概率扩展，该模型考虑了无证感染，并允许流行病学参数随时间变化。我们通过高斯过程估计疾病传播率，该过程会随着时间的推移捕获非线性变化而无需特定的参数假设。我们利用平行的马尔可夫链蒙特卡洛算法从高度相关的后空间中有效采样。未来观察的预测是通过从其后验预测分布中取样来完成的。使用模拟数据集评估所提出方法的性能。最后，我们的方法适用于美国四个州的共同数据：华盛顿，纽约，加利福尼亚和伊利诺伊州。 https://github.com/tianjianzhou/baysir提供了R套件Baysir，以便公众进行独立分析或在本文中重现结果。
The outbreak of Coronavirus Disease 2019 (COVID-19) is an ongoing pandemic affecting over 200 countries and regions. Inference about the transmission dynamics of COVID-19 can provide important insights into the speed of disease spread and the effects of mitigation policies. We develop a novel Bayesian approach to such inference based on a probabilistic compartmental model using data of daily confirmed COVID-19 cases. In particular, we consider a probabilistic extension of the classical susceptible-infectious-recovered model, which takes into account undocumented infections and allows the epidemiological parameters to vary over time. We estimate the disease transmission rate via a Gaussian process prior, which captures nonlinear changes over time without the need of specific parametric assumptions. We utilize a parallel-tempering Markov chain Monte Carlo algorithm to efficiently sample from the highly correlated posterior space. Predictions for future observations are done by sampling from their posterior predictive distributions. Performance of the proposed approach is assessed using simulated datasets. Finally, our approach is applied to COVID-19 data from four states of the United States: Washington, New York, California, and Illinois. An R package BaySIR is made available at https://github.com/tianjianzhou/BaySIR for the public to conduct independent analysis or reproduce the results in this paper.
