| ObjectivesIn 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.Methods1. 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.Results1. 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.Conclusions1. 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.Innovation1. 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. |
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