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 likelihood-based inference method based on the stochastic EM algorithm, 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.
翻译:我们开发了动态网络的随机流行病模型,这些网络的感染率各不相同,而且可能与个人水平的共变体不同。联合动态模型是连续时间的马尔科夫链条,这样,疾病传播受到接触网络结构的制约,而网络的演变反过来又受到个别疾病状况的影响。 为了容纳在现实世界数据中常见的部分流行病观察,我们提出了一个基于随机的EM算法的基于概率的推论方法,引入了关键的创新,包括高效的有条件取样器,用于估算失踪感染和恢复时间,从而尊重动态的联络网络。对合成和真实数据集的实验表明,我们的推断方法能够准确和高效地恢复模型参数,并对流行病数据中未观测到的疾病的存在提供有价值的洞察力。