Division of Infectious Diseases and Global Public, University of California San Diego, San Diego, California, USA.
The Public Health Company, Palo Alto, California, USA.
Stat Med. 2023 Sep 10;42(20):3593-3615. doi: 10.1002/sim.9816. Epub 2023 Jul 1.
To effectively mitigate the spread of communicable diseases, it is necessary to understand the interactions that enable disease transmission among individuals in a population; we refer to the set of these interactions as a contact network. The structure of the contact network can have profound effects on both the spread of infectious diseases and the effectiveness of control programs. Therefore, understanding the contact network permits more efficient use of resources. Measuring the structure of the network, however, is a challenging problem. We present a Bayesian approach to integrate multiple data sources associated with the transmission of infectious diseases to more precisely and accurately estimate important properties of the contact network. An important aspect of the approach is the use of the congruence class models for networks. We conduct simulation studies modeling pathogens resembling SARS-CoV-2 and HIV to assess the method; subsequently, we apply our approach to HIV data from the University of California San Diego Primary Infection Resource Consortium. Based on simulation studies, we demonstrate that the integration of epidemiological and viral genetic data with risk behavior survey data can lead to large decreases in mean squared error (MSE) in contact network estimates compared to estimates based strictly on risk behavior information. This decrease in MSE is present even in settings where the risk behavior surveys contain measurement error. Through these simulations, we also highlight certain settings where the approach does not improve MSE.
为了有效控制传染病的传播，有必要了解人群中个体之间传播疾病的相互作用，我们将这些相互作用集合称为接触网络。接触网络的结构对传染病的传播和控制计划的有效性都有深远的影响。因此，了解接触网络可以更有效地利用资源。然而，测量网络的结构是一个具有挑战性的问题。我们提出了一种贝叶斯方法，用于整合与传染病传播相关的多个数据源，以更精确和准确地估计接触网络的重要属性。该方法的一个重要方面是使用网络的同余类模型。我们通过模拟类似于 SARS-CoV-2 和 HIV 的病原体进行了模拟研究，以评估该方法；随后，我们将我们的方法应用于加利福尼亚大学圣地亚哥分校原发性感染资源联盟的 HIV 数据。基于模拟研究，我们证明了将流行病学和病毒遗传数据与风险行为调查数据相结合，与仅基于风险行为信息的估计相比，可以大大降低接触网络估计的均方误差 (MSE)。即使在风险行为调查包含测量误差的情况下，这种 MSE 的降低仍然存在。通过这些模拟，我们还强调了该方法不会提高 MSE 的某些情况。
