Simon A. Levin Mathematical Computational Modeling Science Center, Arizona State University, Tempe, AZ 85281, USA; Division of Applied Mathematics, Brown University, Providence, RI, 02906, USA.
Department of Mathematics, Tulane University, New Orleans, LA, 70118, USA.
Epidemics. 2021 Jun;35:100456. doi: 10.1016/j.epidem.2021.100456. Epub 2021 Mar 24.
Chlamydia trachomatis (Ct) is the most reported sexually transmitted infection in the United States, with a major cause of infertility, pelvic inflammatory disease, and ectopic pregnancy among women. Despite decades of screening women for Ct, rates increase among young African Americans (AA). We create and analyze a heterosexual agent-based network model to help understand the spread of Ct. We calibrate the model parameters to agree with survey data showing Ct prevalence of 12% of the women and 10% of the men in the 15-25 year-old AA in New Orleans, Louisiana. Our model accounts for both long-term and casual partnerships. The network captures the assortative mixing of individuals by preserving the joint-degree distributions observed in the data. We compare the effectiveness of intervention strategies based on randomly screening men, notifying partners of infected people, which includes partner treatment, partner screening, and rescreening for infection. We compare the difference between treating partners of an infected person both with and without testing them. We observe that although increased Ct screening, rescreening, and treating most of the partners of infected people will reduce the prevalence, these mitigations alone are not sufficient to control the epidemic. The current practice is to treat the partners of an infected individual without first testing them for infection. The model predicts that if a sufficient number of the partners of all infected people are tested and treated, then there is a threshold condition where the epidemic can be mitigated. This threshold results from the expanded treatment network created by treating an individual's infected partners' partners. Although these conclusions can help design future Ct mitigation studies, we caution the reader that these conclusions are for the mathematical model, not the real world, and are contingent on the validity of the model assumptions.
沙眼衣原体（Ct）是美国报告最多的性传播感染，是女性不孕、盆腔炎和异位妊娠的主要原因。尽管几十年来一直在对女性进行 Ct 筛查，但年轻非裔美国人（AA）的发病率仍在上升。我们创建并分析了一个异性恋基于主体的网络模型，以帮助理解 Ct 的传播。我们将模型参数校准为与调查数据一致，该数据显示，在路易斯安那州新奥尔良的 15-25 岁 AA 中，女性的 Ct 患病率为 12%，男性为 10%。我们的模型同时考虑了长期和偶然的伴侣关系。该网络通过保留数据中观察到的联合度分布来捕捉个体的混合。我们比较了基于随机筛查男性、通知感染人员的伴侣、包括伴侣治疗、伴侣筛查和重新筛查感染的干预策略的有效性。我们比较了在不检测的情况下同时治疗感染者的伴侣和治疗感染者的伴侣的效果。我们观察到，尽管增加 Ct 筛查、重新筛查和治疗大多数感染者的伴侣可以降低患病率，但这些缓解措施本身不足以控制疫情。目前的做法是在不首先检测感染者是否感染的情况下治疗感染者的伴侣。该模型预测，如果对所有感染者的足够数量的伴侣进行检测和治疗，那么就存在一个可以减轻疫情的阈值条件。该阈值源于通过治疗个体感染者的伴侣的伴侣而创建的扩展治疗网络。尽管这些结论有助于设计未来的 Ct 缓解研究，但我们提醒读者，这些结论是针对数学模型的，而不是真实世界的，并且取决于模型假设的有效性。
