Role of social networks in shaping disease transmission during a community outbreak of 2009 H1N1 pandemic influenza
Role of social networks in shaping disease transmission during a community outbreak of 2009 H1N1 pandemic influenza
复制标题
DOI：
10.1073/pnas.1008895108
复制
发表时间：
2011-02-15
影响因子：
11.1
通讯作者：
Swerdlow, David
中科院分区：
文献类型：
--
作者：
Cauchemez, Simon;Bhattarai, Achuyt;Swerdlow, David
由于缺乏家庭外传播的详细数据和适当的统计方法，评估不同社会网络对呼吸道疾病传播的影响一直受到限制。在这里，我们从从一所小学开始并在宾夕法尼亚州一个半乡村社区传播的H1N1大流行(Pdm)流感爆发期间收集的数据中，我们量化了社交网络对流感传播的影响。我们建立了一个传输模型，通过马尔可夫链蒙特卡罗抽样从数据中估计出参数。坐在病例旁边或成为病例的玩伴并不会显著增加感染的风险；但学校按班级和年级的结构对传播有很大影响。有证据表明，男孩比女孩更有可能将流感传染给其他男孩(反之亦然)，这与观察到的玩伴之间的混合相似。我们还调查了在暴发的特定日子是否存在异常高的传播。学校较晚关闭(即27%的学生已经有症状时)对传播没有显著影响。学龄儿童(6-18岁)促进了流感在家庭中的传播和传播，但只有大约五分之一的18岁病例是由学龄儿童家庭成员感染的。这项分析显示了明确定义的社交网络对流感传播的影响程度，揭示了地方之间的强烈互动，以及学校、社区和家庭之间来回传播的浪潮。
Evaluating the impact of different social networks on the spread of respiratory diseases has been limited by a lack of detailed data on transmission outside the household setting as well as appropriate statistical methods. Here, from data collected during a H1N1 pandemic (pdm) influenza outbreak that started in an elementary school and spread in a semirural community in Pennsylvania, we quantify how transmission of influenza is affected by social networks. We set up a transmission model for which parameters are estimated from the data via Markov chain Monte Carlo sampling. Sitting next to a case or being the playmate of a case did not significantly increase the risk of infection; but the structuring of the school into classes and grades strongly affected spread. There was evidence that boys were more likely to transmit influenza to other boys than to girls (and vice versa), which mimicked the observed assortative mixing among playmates. We also investigated the presence of abnormally high transmission occurring on specific days of the outbreak. Late closure of the school (i.e., when 27% of students already had symptoms) had no significant impact on spread. School-aged individuals (6-18 y) facilitated the introduction and spread of influenza in households, but only about one in five cases aged >18 y was infected by a school-aged household member. This analysis shows the extent to which clearly defined social networks affect influenza transmission, revealing strong between-place interactions with back-and-forth waves of transmission between the school, the community, and the household.