Bu Fan, Aiello Allison E, Volfovsky Alexander, Xu Jason
Department of Biostatistics, University of Michigan, 1415 Washington Heights, Ann Arbor, MI 48109, USA.
Mailman School of Public Health, Columbia University, 722 West 168th Street, New York, NY 10032, USA.
Biostatistics. 2024 Dec 31;26(1). doi: 10.1093/biostatistics/kxae018.
We develop a stochastic epidemic model progressing over dynamic networks, where infection rates are heterogeneous and may vary with individual-level covariates. The joint dynamics are modeled as a continuous-time Markov chain such that disease transmission is constrained by the contact network structure, and network evolution is in turn influenced by individual disease statuses. To accommodate partial epidemic observations commonly seen in real-world data, we propose a stochastic EM algorithm for inference, introducing key innovations that include efficient conditional samplers for imputing missing infection and recovery times which respect the dynamic contact network. Experiments on both synthetic and real datasets demonstrate that our inference method can accurately and efficiently recover model parameters and provide valuable insight at the presence of unobserved disease episodes in epidemic data.
我们开发了一种在动态网络上进展的随机流行病模型，其中感染率是异质的，并且可能随个体水平的协变量而变化。联合动态被建模为连续时间马尔可夫链，使得疾病传播受到接触网络结构的约束，而网络演化又受到个体疾病状态的影响。为了适应现实世界数据中常见的部分流行病观测，我们提出了一种用于推理的随机期望最大化（EM）算法，引入了关键创新，包括用于估算缺失感染和恢复时间的高效条件采样器，这些采样器考虑了动态接触网络。在合成数据集和真实数据集上的实验表明，我们的推理方法能够准确有效地恢复模型参数，并在流行病数据中存在未观察到的疾病发作的情况下提供有价值的见解。
