Social contacts and mixing patterns relevant to the spread of infectious diseases.
Social contacts and mixing patterns relevant to the spread of infectious diseases.
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与传染病的传播相关的社会接触和混合模式。
DOI：
10.1371/journal.pmed.0050074
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发表时间：
2008-03-25
期刊：
影响因子：
15.8
通讯作者：
Edmunds, W. John
中科院分区：
文献类型：
--
作者：
Mossong, Joeel;Hens, Niel;Jit, Mark;Beutels, Philippe;Auranen, Kari;Mikolajczyk, Rafael;Massari, Marco;Salmaso, Stefania;Tomba, Gianpaolo Scalia;Wallinga, Jacco;Heijne, Janneke;Sadkowska-Todys, Malgorzata;Rosinska, Magdalena;Edmunds, W. John
通过呼吸道或密切接触途径（例如大流行性流感）传播的传染病的数学模型正越来越多地用于确定可能的干预措施的影响。虽然已知混合模式是模型结果的关键决定因素，但研究人员往往依赖于很少或没有经验基础的先验接触假设。我们在八个欧洲国家进行了一项基于人群的前瞻性混合模式调查，使用了一种常见的纸质日记方法。7290名参与者记录了一天内与不同个体的97904次接触的特征，包括年龄、性别、地点、持续时间、频率和身体接触的发生情况。我们发现混合模式和接触特征在不同的欧洲国家非常相似。接触模式与年龄高度匹配：学童和年轻人尤其倾向于与同龄的人交往。持续至少一小时或每天发生的接触大多涉及身体接触，而持续时间短和不频繁的接触往往是非身体接触。与工作场所或旅行时的接触相比，家庭、学校或休闲场所的接触更有可能是身体接触。初步模型表明，在通过社会接触传播的新发感染的最初流行阶段，5至19岁人群的发病率预计最高，此时人口完全易感。据我们所知，我们的研究提供了与呼吸道或密切接触途径传播的感染相关的接触模式的第一个大规模定量方法，结果应该导致用于设计控制策略的数学模型的参数化改进。Joël Mossong及其同事调查了8个欧洲国家的7290名参与者，确定了与控制病原体通过呼吸道或密切接触途径传播有关的人际接触模式。为了了解和预测传染病的影响，研究人员经常开发数学模型。这些假设情景的计算机模拟有助于决策者和其他人预测疾病出现的可能模式和后果，并制定干预措施以遏制疾病传播。无论是为传染病的爆发做准备还是控制现有的爆发，模型都可以帮助研究人员和决策者决定如何进行干预。例如，他们可能决定开发或储存疫苗或抗生素，资助疫苗接种或筛查项目，或开展健康促进运动，以帮助公民尽量减少与传染原的接触（例如，洗手、限制旅行或关闭学校）。呼吸道感染，包括普通感冒、流感和肺炎，是世界上最普遍的感染。人们已经做了大量的工作来模拟在各种情况下有多少人会受到呼吸系统疾病的影响，以及可以做些什么来限制后果。数学模型倾向于使用接触率（一个人每天遇到的其他人的数量）作为预测流行病结果的主要因素之一。过去，接触率不是基于直接观察，而是假设遵循某种模式，并根据血清学或病例通报数据等其他间接数据源进行校准。这项研究的目的是通过询问人们在一天中遇到的人来直接估计接触率。这使得研究人员可以更详细地研究不同的接触模式，例如不同人群（如年龄组）和不同社会环境中的接触模式。这对呼吸系统疾病尤其重要，这些疾病通过空气传播，并通过与受感染个体或表面的密切接触传播。研究人员想要研究人们的社会接触，以便更好地了解呼吸道感染是如何传播的。他们从8个欧洲国家（比利时、德国、芬兰、英国、意大利、卢森堡、荷兰和波兰）招募了7290人参与他们的研究。他们要求参与者填写日记，记录一天内的身体接触和非身体接触。身体接触包括亲吻或握手等互动。非身体接触是指没有皮肤接触的双向对话。参与者详细说明了每次接触的地点和持续时间。日记还包含了参与者和联系人的基本人口统计信息。他们发现，这7290名参与者在研究期间有97904个联系人，平均每人每天有13.4个联系人。接触者之间存在很大的多样性，这挑战了仅接触率就能提供传播动态全貌的观点。研究人员确定了不同类型的接触、接触的持续时间和混合模式。例如，儿童比成年人有更多的联系，而那些生活在大家庭中的人有更多的联系。工作日的日常接触次数比周日多。更强烈的接触（持续时间更长或更频繁）往往是身体接触。每天约有70%的联系持续时间超过一小时，而四分之三以前不认识的人的联系持续时间不到15分钟。虽然这八个国家的混合模式非常相似，但相同年龄的人倾向于相互混合。通过分析这些接触模式并应用数学和统计技术，研究人员创建了一个假设的呼吸道感染流行病初始阶段的模型。该模型表明，在最初的传播中，5至19岁的人将承受最高的呼吸道感染负担。模型中学龄儿童的高感染率是由于这些儿童与其他群体相比有大量的接触，并且倾向于在自己的年龄组内进行接触。这项工作提供了关于接触的见解，可以补充传统的测量方法，如接触率，接触率通常由家庭或工作场所的规模和交通统计数据产生。将接触模式纳入模型可以更深入地了解假设的呼吸道流行病在易感人群中的传播模式。了解社会联系的模式——在群体之间和群体内部，以及在不同的社会环境中——表明了个体之间的联系和融合是多么的多样化。作者总结说，身体接触传染源的最佳模型是考虑到密切接触者的社会网络及其模式。请通过本摘要的在线版本访问这些网站：doi:10.1371/journal.pmed.0050074。维基百科对流行病学数学模型中使用的假设进行了技术讨论（请注意，维基百科是一个免费的在线百科全书，任何人都可以编辑；有几种语言版本），为加拿大政府、联合王国健康保护局和美国卫生与公众服务部解释了大流行性流感的计划
Mathematical modelling of infectious diseases transmitted by the respiratory or close-contact route (e.g., pandemic influenza) is increasingly being used to determine the impact of possible interventions. Although mixing patterns are known to be crucial determinants for model outcome, researchers often rely on a priori contact assumptions with little or no empirical basis. We conducted a population-based prospective survey of mixing patterns in eight European countries using a common paper-diary methodology. 7,290 participants recorded characteristics of 97,904 contacts with different individuals during one day, including age, sex, location, duration, frequency, and occurrence of physical contact. We found that mixing patterns and contact characteristics were remarkably similar across different European countries. Contact patterns were highly assortative with age: schoolchildren and young adults in particular tended to mix with people of the same age. Contacts lasting at least one hour or occurring on a daily basis mostly involved physical contact, while short duration and infrequent contacts tended to be nonphysical. Contacts at home, school, or leisure were more likely to be physical than contacts at the workplace or while travelling. Preliminary modelling indicates that 5- to 19-year-olds are expected to suffer the highest incidence during the initial epidemic phase of an emerging infection transmitted through social contacts measured here when the population is completely susceptible. To our knowledge, our study provides the first large-scale quantitative approach to contact patterns relevant for infections transmitted by the respiratory or close-contact route, and the results should lead to improved parameterisation of mathematical models used to design control strategies. Surveying 7,290 participants in eight European countries, Joël Mossong and colleagues determine patterns of person-to-person contact relevant to controlling pathogens spread by respiratory or close-contact routes. To understand and predict the impact of infectious disease, researchers often develop mathematical models. These computer simulations of hypothetical scenarios help policymakers and others to anticipate possible patterns and consequences of the emergence of diseases, and to develop interventions to curb disease spread. Whether to prepare for an outbreak of infectious disease or to control an existing outbreak, models can help researchers and policy makers decide how to intervene. For example, they may decide to develop or stockpile vaccines or antibiotics, fund vaccination or screening programs, or mount health promotion campaigns to help citizens minimize their exposure to the infectious agent (e.g., handwashing, travel restrictions, or school closures). Respiratory infections, including the common cold, flu, and pneumonia, are some of the most prevalent infections in the world. Much work has gone into modeling how many people would be affected by respiratory diseases under various conditions and what can be done to limit the consequences. Mathematical models have tended to use contact rates (the number of other people that a person encounters per day) as one of their main elements in predicting the outcomes of epidemics. In the past, contact rates were not based on direct observations, but were assumed to follow a certain pattern and calibrated against other indirect data sources such as serological or case notification data. This study aimed to estimate contact rates directly by asking people who they have met during the course of one day. This allowed the researchers to study in more detail different patterns of contacts, such as those between different groups of people (such as age groups) and in different social settings. This is particularly important for respiratory diseases, which are spread through the air and by close contact with an infected individual or surface. The researchers wanted to examine the social contacts that people have in order to better understand how respiratory infections might spread. They recruited 7,290 people from eight European countries (Belgium, Germany, Finland, Great Britain, Italy, Luxembourg, The Netherlands, and Poland) to participate in their study. They asked the participants to fill out a diary that documented their physical and nonphysical contacts for a single day. Physical contacts included interactions such as a kiss or a handshake. Nonphysical contacts were situations such as a two-way conversation without skin-to-skin contact. Participants detailed the location and duration of each contact. Diaries also contained basic demographic information about the participant and the contact. They found that these 7,290 participants had 97,904 contacts during the study, which averaged to 13.4 contacts per day per person. There was a great deal of diversity among the contacts, which challenges the idea that contact rates alone provide a complete picture of transmission dynamics. The researchers identified varied types of contacts, duration of contacts, and mixing patterns. For example, children had more contacts than adults, and those living in larger households had more contacts. Weekdays resulted in more daily contacts than Sundays. More intense contacts (of longer duration or more frequent) tended to be physical. Approximately 70% of contacts made on a daily basis lasted longer than an hour, whereas three-quarters of contacts with people who were not previously known lasted less than 15 minutes. While mixing patterns were very similar across the eight countries, people of the same age tended to mix with each other. Analyzing these contact patterns and applying mathematical and statistical techniques, the researchers created a model of the initial phase of a hypothetical respiratory infection epidemic. This model suggests that 5- to 19-year-olds will suffer the highest burden of respiratory infection during an initial spread. The high incidence of infection among school-aged children in the model results from these children having a large number of contacts compared to other groups and tending to make contacts within their own age group. This work provides insight about contacts that can be supplemental to traditional measurements such as contact rates, which are usually generated from household or workplace size and transportation statistics. Incorporating contact patterns into the model allowed for a deeper understanding of the transmission patterns of a hypothetical respiratory epidemic among a susceptible population. Understanding the patterning of social contacts—between and within groups, and in different social settings—shows how diverse contacts and mixing between individuals really are. Physical exposure to an infectious agent, the authors conclude, is best modeled by taking into account the social network of close contacts and its patterning. Please access these Web sites via the online version of this summary at doi:10.1371/journal.pmed.0050074.. Wikipedia has technical discussions on the assumptions used in mathematical models of epidemiology (note that Wikipedia is a free online encyclopedia that anyone can edit; available in several languages) Plans for pandemic influenza are explained for the Government of Canada, the United Kingdom's Health Protection Agency, and the United States Department of Health and Human Services
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DOI：
10.1098/rspb.1997.0131
发表时间：
1997-07-22
影响因子：
4.7
作者：
Edmunds, WJ;OCallaghan, CJ;Nokes, DJ
通讯作者：
Nokes, DJ
DOI：
10.1198/016214504000001754
发表时间：
2005-06-01
影响因子：
3.7
作者：
Farrington, CP;Whitaker, HJ
通讯作者：
Whitaker, HJ
影响因子：
--
作者：
FRY, J
通讯作者：
FRY, J
影响因子：
56.9
作者：
Lipsitch, M;Cohen, T;Murray, M
通讯作者：
Murray, M
影响因子：
2.3
作者：
Edmunds WJ;Kafatos G;Wallinga J;Mossong JR
通讯作者：
Mossong JR