纵向机制性HIV接触网络的近似推断
Approximate Inference for Longitudinal Mechanistic HIV Contact Networks
AI总结：
本文提出一种基于ABC的近似推断方法，用于纵向机制性HIV接触网络模型，发现两波次纵向设计比横断面设计推断更准确，且精度提升受波次间隔和摘要统计量影响。
摘要：
网络模型越来越多地被用于研究传染病传播。指数随机图模型在该领域具有历史，现已具备可扩展的推断方法。另一种替代方法是使用机制性网络模型。机制性网络模型直接捕捉个体行为，使其适用于研究性传播疾病。将机制性模型与近似贝叶斯计算相结合，能够利用智能体之间特定领域的交互规则进行灵活建模，从而避免网络模型的过度简化。这些模型非常适合纵向环境，因为它们明确地纳入了网络随时间的演化。我们实现了一个先前发表的关于男男性行为者（MSM）接触网络演化的连续时间模型的离散时间版本，并为其提出了一种基于ABC的近似推断方案。正如预期的那样，我们发现与横断面设计相比，两波次纵向研究设计提高了推断的准确性。然而，收集两次数据带来的精度提升（高达18%）取决于两波次之间的时间间隔，并且对摘要统计量的选择很敏感。除了方法学上的发展，我们的研究结果还为未来性传播疾病纵向网络研究的设计提供了参考，特别是在向参与者收集什么数据以及何时收集数据方面。
英文摘要：
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 (MSM) 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.
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