Zeeman Institute: SBIDER, Warwick Mathematics Institute and School of Life Sciences, The University of Warwick, Coventry, CV4 8UW, United Kingdom.
National Institute of Water and Atmospheric Research, Evans Bay Parade, Wellington 6021, New Zealand.
PLoS One. 2018 Nov 1;13(11):e0206501. doi: 10.1371/journal.pone.0206501. eCollection 2018.
Understanding the spread of sexually transmitted infections (STIs) in a population is of great importance to the planning and delivery of health services globally. The worldwide rise of HIV since the 1980's, and the recent increase in common STIs (including HPV and Chlamydia) in many countries, means that there is an urgent need to understand transmission dynamics in order to better predict the spread of such infections in the population. Unlike many other infections which can be captured by assumptions of random mixing, STI transmission is intimately linked to the number and pattern of sexual contacts. In fact, it is the huge variation in the number of new sexual partners that gives rise to the extremes of risk within populations which need to be captured in predictive models of STI transmission. Such models are vital in providing the necessary scientific evidence to determine whether a range of controls (from education to screening to vaccination) are cost-effective.
We use probability sample survey data from Britain's third National Survey of Sexual Attitudes and Lifestyles (Natsal-3) to determine robust distributions for the rate of new partnerships that involve condomless sex and can therefore facilitate the spread of STIs. Different distributions are defined depending on four individual-level characteristics: age, sex, sexual orientation, and previous sexual experience. As individual behaviour patterns can change (e.g. by remaining in a monogamous relationship for a long period) we allow risk-percentiles to be randomly redrawn, to capture longer term behaviour as measured by Natsal-3. We demonstrate how this model formulation interacts with the transmission of infection by constructing an individual-based SIS-P (Susceptible-Infected-Susceptible-Protected) transmission model for the spread of a generic STI, and observing overall population demographics when varying the transmission probability within a partnership, recovery rate and the level of population protection (e.g. from vaccination where applicable).
了解人群中性传播感染(STIs)的传播对于全球卫生服务的规划和提供至关重要。自 20 世纪 80 年代以来,HIV 在全球范围内的上升,以及许多国家常见 STIs(包括 HPV 和衣原体)的最近增加,意味着迫切需要了解传播动态,以便更好地预测人群中此类感染的传播。与许多其他可以通过随机混合假设捕捉到的感染不同,STI 传播与性接触的数量和模式密切相关。事实上,正是新性伴侣数量的巨大变化,导致了人群中需要在 STI 传播预测模型中捕捉到的风险极端。这些模型对于提供必要的科学证据以确定一系列控制措施(从教育到筛查到疫苗接种)是否具有成本效益至关重要。
我们使用英国第三次全国性态度和生活方式调查(Natsal-3)的概率抽样调查数据,确定涉及无保护性行为的新伴侣关系的速度的稳健分布,从而促进 STIs 的传播。根据四个个体特征:年龄、性别、性取向和以前的性经验,定义了不同的分布。由于个体行为模式可能会发生变化(例如,长时间保持一夫一妻制关系),我们允许风险百分位随机重新绘制,以捕捉 Natsal-3 测量的长期行为。我们通过构建用于传播通用 STI 的基于个体的 SIS-P(易感-感染-易感-保护)传播模型,并在合作伙伴关系内改变传播概率、恢复率和人口保护水平(例如,如有可能通过疫苗接种)来观察总体人口人口统计数据,展示了这种模型构建如何与感染传播相互作用。