Prem Kiesha, Cook Alex R, Jit Mark
Saw Swee Hock School of Public Health, National University of Singapore and National University Health System, Singapore, Singapore.
Program in Health Services and Systems Research, Duke-NUS Graduate Medical School, Singapore, Singapore.
PLoS Comput Biol. 2017 Sep 12;13(9):e1005697. doi: 10.1371/journal.pcbi.1005697. eCollection 2017 Sep.
Heterogeneities in contact networks have a major effect in determining whether a pathogen can become epidemic or persist at endemic levels. Epidemic models that determine which interventions can successfully prevent an outbreak need to account for social structure and mixing patterns. Contact patterns vary across age and locations (e.g. home, work, and school), and including them as predictors in transmission dynamic models of pathogens that spread socially will improve the models' realism. Data from population-based contact diaries in eight European countries from the POLYMOD study were projected to 144 other countries using a Bayesian hierarchical model that estimated the proclivity of age-and-location-specific contact patterns for the countries, using Markov chain Monte Carlo simulation. Household level data from the Demographic and Health Surveys for nine lower-income countries and socio-demographic factors from several on-line databases for 152 countries were used to quantify similarity of countries to estimate contact patterns in the home, work, school and other locations for countries for which no contact data are available, accounting for demographic structure, household structure where known, and a variety of metrics including workforce participation and school enrolment. Contacts are highly assortative with age across all countries considered, but pronounced regional differences in the age-specific contacts at home were noticeable, with more inter-generational contacts in Asian countries than in other settings. Moreover, there were variations in contact patterns by location, with work-place contacts being least assortative. These variations led to differences in the effect of social distancing measures in an age structured epidemic model. Contacts have an important role in transmission dynamic models that use contact rates to characterize the spread of contact-transmissible diseases. This study provides estimates of mixing patterns for societies for which contact data such as POLYMOD are not yet available.
接触网络中的异质性在决定病原体是否会流行或在地方病水平持续存在方面具有重大影响。确定哪些干预措施能够成功预防疫情爆发的流行模型需要考虑社会结构和混合模式。接触模式因年龄和地点(如家庭、工作场所和学校)而异,将它们作为通过社交传播的病原体传播动态模型的预测因素纳入其中,将提高模型的现实性。利用贝叶斯分层模型,通过马尔可夫链蒙特卡罗模拟,估计了欧洲八个国家基于人群的接触日记数据(来自POLYMOD研究)在其他144个国家的情况,该模型估算了各国特定年龄和地点的接触模式倾向。来自九个低收入国家的人口与健康调查的家庭层面数据以及来自152个国家的多个在线数据库的社会人口因素,被用于量化各国之间的相似性,以估计没有接触数据的国家在家庭、工作场所、学校和其他地点的接触模式,同时考虑人口结构、已知的家庭结构以及包括劳动力参与率和入学率在内的各种指标。在所考虑的所有国家中,接触在年龄方面具有高度的同质性,但家庭中特定年龄接触的明显区域差异很显著,亚洲国家的代际接触比其他环境更多。此外,接触模式因地点而异,工作场所的接触同质性最低。这些差异导致了年龄结构流行模型中社会距离措施效果的不同。接触在使用接触率来描述接触传播疾病传播的传播动态模型中起着重要作用。这项研究提供了尚未获得如POLYMOD等接触数据的社会的混合模式估计。