基于社会网络理论的流感传播特征及防控措施效果评价
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摘要
研究目的
借助目前复杂网络及社会网络理论领域研究各种经典分析方法和指标,从动态和静态两个角度分析流感在人群接触网络中的传播特征,从微观行为与宏观结构两个层面构建基于社会网络的流感传播模型,利用计算机进行模拟和定量分析,提出预防控制措施和管理模式,从而有效地预防和控制流感在人群中的传播。
研究方法
1、调查学校青少年在社会网络中与其他人的接触情况和流感防控知识知晓情况。调查问卷内容主要包括：个人基本情况、日常生活中的接触行为、流感基本知识、态度、行为和需求。接触行为数据分年级和群组排列,计算不同群组活动变量的平均值(AVG)、变异系数(CV)等。本调查数据应用EPI DATA 3.1完成数据的录入和整理,运用EXCEL 2003和SPSS13.0进行统计分析。
2、调查发生流感疫情的湖北省随州市某小学四年级某班全体学生。调查问卷主要包括两部分内容：个人基本情况和学生间的接触行为。构建一个相对封闭的班级内部学生接触的整体网络,进行计算机录入分析,获取网络特征参数。用UCINET网络分析集成软件进行整体网络分析,接触行为特征数据采用EPI DATA3.1录入并核对,用EXCEL 2003和SPSS 13.0进行统计分析。
3、选取湖北省人口作为研究对象,构建个体和群体两个层面的模型。个体模型基于拓展后的经典SIR模型,群体模型是基于真实表现个体之间接触的社会接触网络(SCN)模型。仿真模拟开始于10万个人的社会接触网络和一个受感染的个体,使用InfluSim分析在没有提供疫苗的情况下,药物和非药物措施减轻流感大流行的效果。
研究结果
1、不同年级间各群组平均接触时间的分布比较均衡,没有比较大的起落变化。在列出的十个群组中,家庭群组具有最高的平均接触时间,其次是学校课堂群组。各年级级别中,小学生中接触层次比初中生和高中生高。但是各群组接触层次时间值的总和以高中年级最高,小学最低。特别是少数几个学生的接触层次时间值非常高,这些接触层次时间值很高的人有可能成为“超级传播者(super spreader)"。
2、流感发生前后班级内学生之间接触发生了很大变化,流感发生后网络结构整体上比流感发生前要松散得多。流感发生前班级内日常接触网络比流感发生后班级内部接触网络的网络距离小,聚类系数大,网络的分散程度小,凝聚程度高。流感发生前后各节点的度和相对中心度的均值分别为9.1和6.5,大部分节点的度有不同程度的减少。流感发生后班级内部接触网络中主要接触人数下降了2个；平均接触时间由每人每天2.6小时降为1.9小时,与主要接触者的接触时间总和由流感发生前的17.2小时下降为8.4小时。
3、没有干预措施时人群感染率为83.0%,采取药物治疗、社会距离和综合措施后感染率分别为69.0%、67.0%和54.0%。没有干预措施时流感流行引起死亡人数为111人,采取干预措施后,死亡数大大降低。不同干预措施对暴露者、隐性感染者和严重病例者人数也有一定影响。没有干预措施时流行持续时间为79天,采取治疗措施、社会距离和综合措施后,流感流行持续时间延长,增加率分别为19.0%、44.3%和75.9%。各年龄段的感染率也大大降低,分别降低到2.3%、5.7%、3.8%、3.3%、2.9%和1.8%。研究结论
1、应用SCN理论描述人群中接触行为的特征,找出对流感传播具有重要作用的群组或个体,有助于合理设计社会接触距离措施。并且,提高流感主要侵害对象的认知,能促进防控措施的顺利实施。
2、流感发生后班级内部整体网络结构变得松散了,个体节点的位置发生了一些变化,平均每个学生密切接触的学生人数减少,接触时间与接触层次时间值也显著下降,表明流感防控措施产生了一定效果。人群接触行为也发生了改变,有利于控制流感在人群中的快速传播。
3、在流感高速传播时,及时应用抗病毒药物(即使是有限的药品)和快速实施减少社会接触的措施将显著延缓流行高峰的到来,并且大幅度降低其高峰期的感染人数,降低流感流行的凶险程度。
1、应用社会接触网络(SCN)理论分析流感在人群中传播的特征,找到流感防控的重点关注对象,利于有针对性地采取防控措施,达到效益的最大化。
2、利用整体网络分析方法分析流感疫情发生前后接触网络拓扑结构和个体接触行为的变化,为定量评价不同流感防控干预措施的效果奠定基础。3、结合SIR和SCN构建合适的流感传播模型,模拟流感在人群中传播的过程,定量评价不同的流感防控干预措施的效果。
Objectives
In recent years, the complex network theory, as a profound tool for understanding the complex network system, analyze the qualitative and quantitative evolution to control the complex system. In this study, based on the current theory of complex networks and the field of social network, we analyzed the spreading characteristics of influenza both from the perspective of dynamic and static, constructed social behavior network model of influenza spread from micro-macro levels and evaluated quantitative effects of intervention measures.
Methods
1. Surveys were administered to students in an elementary, middle and high-school in Hubei Suizhou. The social contact network of a person was conceptualized as a set of groups to which they belong (e.g., households, classes). Each group composed of a sub-network of primary links representing the individuals that they contact. The size of the group, number of primary links, time spent in the group, and level of contact along each primary link (near, talking, touching) were characterized. We completed the data entry and analyzed them by EPI DATA 3.1, and used EXCEL 2003 and SPSS 13.0 for statistical analysis.
2. Surveys were administered to students in an elementary school class in Hubei Suizhou. A total of 68 students participated in the survey. The questionnaire consisted of two main parts:basic information and personal contact behaviors between classmates. The later included before and after the influenza epidemic situation. Participations filled out the questionnaire after the investigator explained the items. The students were exposed within the overall network for evaluating characteristic parameters of the network. The network parameters and the individual location parameters were characterized, such as density, distance, clustering coefficient, the node degree and the betweenness. The size of the group, number of primary links, time spent in the group, and level of contact along each primary link (near, talking, touching) were also characterized. We completed the data entry and analyzed them by EPI DATA 3.1, and used integrated software UCINET, EXCEL 2003 and SPSS13.0 for statistical analysis.
3. The model included an individual level, in which the risk of influenza virus infection and the dynamics of viral shedding were simulated according to age, treatment; and a community level, in which meetings between individuals were simulated on randomly generated graphs. We used data on real pandemics to calibrate some parameters of the model. The reference scenario assumed no vaccination, no use of antiviral drugs, and no preexisting herd immunity. We explored the impact of interventions such as treatment/prophylaxis with neuraminidase inhibitors, quarantine, and closure of schools or workplaces.
Results
1. Students, groups and public activity were highly heterogeneous. Groups with high potential for the transmission of influenza were households, school classes. Sports decreased and households and school classes increased in importance with grade level. Individual public activity events were also important but lost their importance when averaged over time. Students were highly assortative, interacting mainly within age class. A small number of individual students were identified as likely "super-spreaders".
2. The contact behaviors between classmates had taken place great changes before and after influenza occurred. The whole network was much more to loose for the emergence of influenza. Compared with daily life, contact network density and overall graph clustering coefficient after influenza epidemics decreased more or less. The degree centrality after influenza epidemics was lower than before with significant differences. Primary links, contact-hours and contact-level-hours after influenza epidemics were lower than before.
3. In the reference scenario, an explosive outbreak affected 83.0% of the population on average and lasted a mean of 79 days. Interventions aimed at reducing the number of meetings, combined with measures reducing individual transmissibility, would be partly effective. With treatment of the index patient, prophylaxis of household contacts, and confinement to home of all household members, would reduce the probability of an outbreak by 29.0%. Interventions would significantly reduce the frequency, size, and mean duration of outbreaks, but the benefit would depend markedly on the interval between identification of the first case.
Conclusions
1. Closing schools and keeping students at home during a pandemic would remove the transmission potential within these ages and could be effective at thwarting its spread within a community. Social contact networks characterized as groups and public activities with the time, level of contact and primary links within each, yields a comprehensive view, which if extended to all ages, would allow design of effective community containment for pandemic influenza.
2. Compared with daily life, characteristics of contact network structure and contact behavior after influenza epidemics were in favor of controlling influenza spreading, which indicated that intervention measures were effective. We should isolate patients and cure them in the early stage of influenza epidemics and give medical observation to close contact persons according to social network characteristics. Targeted social distancing methods should also been taken instantly.
3. When influenza spreading at high-speed, the timely application of antiviral drugs (even if limited drugs) and rapid implementation of measures to reduce social contacts would significantly delay the epidemic peak, significantly reduce the number of its peak infected person, and reduce the extent dangerous of influenza pandemic.
Innovation
1. It applied SCN theory to describe the spreading characteristics of influenza in the population. The result would help to find focus group, and help to take preventive and control measures targeted to reduce the impact of influenza on the population.
2. It applied the overall network analysis method to dynamically describe a relatively closed student group network. This was the foundation for quantitative evaluation the effect of different interventions to prevent and control influenza.
3. It builded appropriate influenza spreading models combined with SIR and the SCN theory. This helped to take quantitative evaluation the effects of different influenza control measures.
借助目前复杂网络及社会网络理论领域研究各种经典分析方法和指标,从动态和静态两个角度分析流感在人群接触网络中的传播特征,从微观行为与宏观结构两个层面构建基于社会网络的流感传播模型,利用计算机进行模拟和定量分析,提出预防控制措施和管理模式,从而有效地预防和控制流感在人群中的传播。
研究方法
1、调查学校青少年在社会网络中与其他人的接触情况和流感防控知识知晓情况。调查问卷内容主要包括：个人基本情况、日常生活中的接触行为、流感基本知识、态度、行为和需求。接触行为数据分年级和群组排列,计算不同群组活动变量的平均值(AVG)、变异系数(CV)等。本调查数据应用EPI DATA 3.1完成数据的录入和整理,运用EXCEL 2003和SPSS13.0进行统计分析。
2、调查发生流感疫情的湖北省随州市某小学四年级某班全体学生。调查问卷主要包括两部分内容：个人基本情况和学生间的接触行为。构建一个相对封闭的班级内部学生接触的整体网络,进行计算机录入分析,获取网络特征参数。用UCINET网络分析集成软件进行整体网络分析,接触行为特征数据采用EPI DATA3.1录入并核对,用EXCEL 2003和SPSS 13.0进行统计分析。
3、选取湖北省人口作为研究对象,构建个体和群体两个层面的模型。个体模型基于拓展后的经典SIR模型,群体模型是基于真实表现个体之间接触的社会接触网络(SCN)模型。仿真模拟开始于10万个人的社会接触网络和一个受感染的个体,使用InfluSim分析在没有提供疫苗的情况下,药物和非药物措施减轻流感大流行的效果。
研究结果
1、不同年级间各群组平均接触时间的分布比较均衡,没有比较大的起落变化。在列出的十个群组中,家庭群组具有最高的平均接触时间,其次是学校课堂群组。各年级级别中,小学生中接触层次比初中生和高中生高。但是各群组接触层次时间值的总和以高中年级最高,小学最低。特别是少数几个学生的接触层次时间值非常高,这些接触层次时间值很高的人有可能成为“超级传播者(super spreader)"。
2、流感发生前后班级内学生之间接触发生了很大变化,流感发生后网络结构整体上比流感发生前要松散得多。流感发生前班级内日常接触网络比流感发生后班级内部接触网络的网络距离小,聚类系数大,网络的分散程度小,凝聚程度高。流感发生前后各节点的度和相对中心度的均值分别为9.1和6.5,大部分节点的度有不同程度的减少。流感发生后班级内部接触网络中主要接触人数下降了2个；平均接触时间由每人每天2.6小时降为1.9小时,与主要接触者的接触时间总和由流感发生前的17.2小时下降为8.4小时。
3、没有干预措施时人群感染率为83.0%,采取药物治疗、社会距离和综合措施后感染率分别为69.0%、67.0%和54.0%。没有干预措施时流感流行引起死亡人数为111人,采取干预措施后,死亡数大大降低。不同干预措施对暴露者、隐性感染者和严重病例者人数也有一定影响。没有干预措施时流行持续时间为79天,采取治疗措施、社会距离和综合措施后,流感流行持续时间延长,增加率分别为19.0%、44.3%和75.9%。各年龄段的感染率也大大降低,分别降低到2.3%、5.7%、3.8%、3.3%、2.9%和1.8%。研究结论
1、应用SCN理论描述人群中接触行为的特征,找出对流感传播具有重要作用的群组或个体,有助于合理设计社会接触距离措施。并且,提高流感主要侵害对象的认知,能促进防控措施的顺利实施。
2、流感发生后班级内部整体网络结构变得松散了,个体节点的位置发生了一些变化,平均每个学生密切接触的学生人数减少,接触时间与接触层次时间值也显著下降,表明流感防控措施产生了一定效果。人群接触行为也发生了改变,有利于控制流感在人群中的快速传播。
3、在流感高速传播时,及时应用抗病毒药物(即使是有限的药品)和快速实施减少社会接触的措施将显著延缓流行高峰的到来,并且大幅度降低其高峰期的感染人数,降低流感流行的凶险程度。
1、应用社会接触网络(SCN)理论分析流感在人群中传播的特征,找到流感防控的重点关注对象,利于有针对性地采取防控措施,达到效益的最大化。
2、利用整体网络分析方法分析流感疫情发生前后接触网络拓扑结构和个体接触行为的变化,为定量评价不同流感防控干预措施的效果奠定基础。3、结合SIR和SCN构建合适的流感传播模型,模拟流感在人群中传播的过程,定量评价不同的流感防控干预措施的效果。
Objectives
In recent years, the complex network theory, as a profound tool for understanding the complex network system, analyze the qualitative and quantitative evolution to control the complex system. In this study, based on the current theory of complex networks and the field of social network, we analyzed the spreading characteristics of influenza both from the perspective of dynamic and static, constructed social behavior network model of influenza spread from micro-macro levels and evaluated quantitative effects of intervention measures.
Methods
1. Surveys were administered to students in an elementary, middle and high-school in Hubei Suizhou. The social contact network of a person was conceptualized as a set of groups to which they belong (e.g., households, classes). Each group composed of a sub-network of primary links representing the individuals that they contact. The size of the group, number of primary links, time spent in the group, and level of contact along each primary link (near, talking, touching) were characterized. We completed the data entry and analyzed them by EPI DATA 3.1, and used EXCEL 2003 and SPSS 13.0 for statistical analysis.
2. Surveys were administered to students in an elementary school class in Hubei Suizhou. A total of 68 students participated in the survey. The questionnaire consisted of two main parts:basic information and personal contact behaviors between classmates. The later included before and after the influenza epidemic situation. Participations filled out the questionnaire after the investigator explained the items. The students were exposed within the overall network for evaluating characteristic parameters of the network. The network parameters and the individual location parameters were characterized, such as density, distance, clustering coefficient, the node degree and the betweenness. The size of the group, number of primary links, time spent in the group, and level of contact along each primary link (near, talking, touching) were also characterized. We completed the data entry and analyzed them by EPI DATA 3.1, and used integrated software UCINET, EXCEL 2003 and SPSS13.0 for statistical analysis.
3. The model included an individual level, in which the risk of influenza virus infection and the dynamics of viral shedding were simulated according to age, treatment; and a community level, in which meetings between individuals were simulated on randomly generated graphs. We used data on real pandemics to calibrate some parameters of the model. The reference scenario assumed no vaccination, no use of antiviral drugs, and no preexisting herd immunity. We explored the impact of interventions such as treatment/prophylaxis with neuraminidase inhibitors, quarantine, and closure of schools or workplaces.
Results
1. Students, groups and public activity were highly heterogeneous. Groups with high potential for the transmission of influenza were households, school classes. Sports decreased and households and school classes increased in importance with grade level. Individual public activity events were also important but lost their importance when averaged over time. Students were highly assortative, interacting mainly within age class. A small number of individual students were identified as likely "super-spreaders".
2. The contact behaviors between classmates had taken place great changes before and after influenza occurred. The whole network was much more to loose for the emergence of influenza. Compared with daily life, contact network density and overall graph clustering coefficient after influenza epidemics decreased more or less. The degree centrality after influenza epidemics was lower than before with significant differences. Primary links, contact-hours and contact-level-hours after influenza epidemics were lower than before.
3. In the reference scenario, an explosive outbreak affected 83.0% of the population on average and lasted a mean of 79 days. Interventions aimed at reducing the number of meetings, combined with measures reducing individual transmissibility, would be partly effective. With treatment of the index patient, prophylaxis of household contacts, and confinement to home of all household members, would reduce the probability of an outbreak by 29.0%. Interventions would significantly reduce the frequency, size, and mean duration of outbreaks, but the benefit would depend markedly on the interval between identification of the first case.
Conclusions
1. Closing schools and keeping students at home during a pandemic would remove the transmission potential within these ages and could be effective at thwarting its spread within a community. Social contact networks characterized as groups and public activities with the time, level of contact and primary links within each, yields a comprehensive view, which if extended to all ages, would allow design of effective community containment for pandemic influenza.
2. Compared with daily life, characteristics of contact network structure and contact behavior after influenza epidemics were in favor of controlling influenza spreading, which indicated that intervention measures were effective. We should isolate patients and cure them in the early stage of influenza epidemics and give medical observation to close contact persons according to social network characteristics. Targeted social distancing methods should also been taken instantly.
3. When influenza spreading at high-speed, the timely application of antiviral drugs (even if limited drugs) and rapid implementation of measures to reduce social contacts would significantly delay the epidemic peak, significantly reduce the number of its peak infected person, and reduce the extent dangerous of influenza pandemic.
Innovation
1. It applied SCN theory to describe the spreading characteristics of influenza in the population. The result would help to find focus group, and help to take preventive and control measures targeted to reduce the impact of influenza on the population.
2. It applied the overall network analysis method to dynamically describe a relatively closed student group network. This was the foundation for quantitative evaluation the effect of different interventions to prevent and control influenza.
3. It builded appropriate influenza spreading models combined with SIR and the SCN theory. This helped to take quantitative evaluation the effects of different influenza control measures.
引文
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