PDF(Pubmed)
Abstract：
Network models are increasingly used to study infectious disease spread. Exponential Random Graph models have a history in this area, with scalable inference methods now available. An alternative approach uses mechanistic network models. Mechanistic network models directly capture individual behaviors, making them suitable for studying sexually transmitted diseases. Combining mechanistic models with Approximate Bayesian Computation allows flexible modeling using domain-specific interaction rules among agents, avoiding network model oversimplifications. These models are ideal for longitudinal settings as they explicitly incorporate network evolution over time. We implemented a discrete-time version of a previously published continuous-time model of evolving contact networks for men who have sex with men and proposed an ABC-based approximate inference scheme for it. As expected, we found that a two-wave longitudinal study design improves the accuracy of inference compared to a cross-sectional design. However, the gains in precision in collecting data twice, up to 18%, depend on the spacing of the two waves and are sensitive to the choice of summary statistics. In addition to methodological developments, our results inform the design of future longitudinal network studies in sexually transmitted diseases, specifically in terms of what data to collect from participants and when to do so.
摘要：
网络模型越来越多地用于研究传染病传播。指数随机图模型在这方面有历史，现在可以使用可扩展的推理方法。另一种方法使用机械网络模型。机械网络模型直接捕获个体行为，使它们适合研究性传播疾病。将机械模型与近似贝叶斯计算相结合，可以使用代理之间特定领域的交互规则进行灵活的建模，避免网络模型过度简化。这些模型对于纵向设置是理想的，因为它们明确地结合了网络随时间的演变。我们实现了先前发布的连续时间模型的离散时间版本，该模型用于与男性发生性关系的男性不断发展的联系网络，并为此提出了基于ABC的近似推理方案。不出所料,我们发现，与横截面设计相比，两波纵向研究设计提高了推断的准确性。然而,两次收集数据的精度提高，高达18%,取决于两个波的间距，并且对汇总统计的选择很敏感。除了方法的发展，我们的结果为未来性传播疾病纵向网络研究的设计提供了信息，特别是在从参与者那里收集什么数据以及何时收集数据方面。
