纵向机制性艾滋病毒接触网络的近似推断。

IF 1.3 Q3 COMPUTER SCIENCE, THEORY & METHODS Applied Network Science Pub Date : 2024-01-01 Epub Date: 2024-04-30 DOI:10.1007/s41109-024-00616-4
Octavious Smiley, Till Hoffmann, Jukka-Pekka Onnela
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引用次数: 0

摘要

网络模型越来越多地被用于研究传染病的传播。指数随机图模型在这一领域有着悠久的历史,目前已有可扩展的推理方法。另一种方法是使用机理网络模型。机理网络模型直接捕捉个体行为,因此适合研究性传播疾病。将机理模型与近似贝叶斯计算相结合,可以利用特定领域的代理之间的交互规则灵活建模,避免网络模型过于简化。这些模型非常适合纵向设置,因为它们明确包含了网络随时间的演变。我们实现了以前发表的男男性行为者接触网络演变连续时间模型的离散时间版本,并提出了基于 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,两次数据收集所带来的精确度提升(最高可达 18%)取决于两次波次的间隔,并且对汇总统计量的选择非常敏感。除了方法上的发展,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,特别是在从参与者那里收集哪些数据以及何时收集数据方面。
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Approximate inference for longitudinal mechanistic HIV contact network.

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.

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来源期刊
Applied Network Science
Applied Network Science Multidisciplinary-Multidisciplinary
CiteScore
4.60
自引率
4.50%
发文量
74
审稿时长
5 weeks
期刊最新文献
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