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One of network epidemiology's central assumptions is that the contact structure over which infectious diseases propagate can be represented as a static network. However, contacts are highly dynamic, changing at many time scales. In this paper, we investigate conceptually simple methods to construct static graphs for network epidemiology from temporal contact data. We evaluate these methods on empirical and synthetic model data. For almost all our cases, the network representation that captures most relevant information is a so-called exponential-threshold network. In these, each contact contributes with a weight decreasing exponentially with time, and there is an edge between a pair of vertices if the weight between them exceeds a threshold. Networks of aggregated contacts over an optimally chosen time window perform almost as good as the exponential-threshold networks. On the other hand, networks of accumulated contacts over the entire sampling time, and networks of concurrent partnerships, perform worse. We discuss these observations in the context of the temporal and topological structure of the data sets. [ABSTRACT FROM AUTHOR]

The dynamics of infectious diseases that are spread through direct contact have been proven to depend on the strength of community structure or modularity within the underlying network. It has been recently shown that weighted networks with similar modularity values may exhibit different mixing styles regarding the number of connections among communities and their respective weights. However, the effect of mixing style on epidemic behavior was still unclear. In this paper, we simulate the spread of disease within networks with different mixing styles: a dense-weak style (i.e., many edges among the communities with small weights) and a sparse-strong style (i.e., a few edges among the communities with large weights). Simulation results show that, with the same modularity: 1) the mixing style significantly influences the epidemic size, speed, pattern and immunization strategy; 2) the increase of the number of communities amplifies the effect of the mixing style; 3) when the mixing style changes from sparse-strong to dense-weak, there is a ‘saturation point’, after which the epidemic size and pattern become stable. We also provide a mean-field solution of the epidemic threshold and size on weighted community networks with arbitrary external and internal degree distribution. The solution explains the effect of the second moment of the degree distribution, and a symmetric effect of internal and external connections (incl. degree distribution and weight). Our study has both potential significance for designing more accurate metrics for the community structure and exploring diffusion dynamics on metapopulation networks. [ABSTRACT FROM AUTHOR]

Background: Social support has been widely known as a protective factor for the emotional well-being (EWB) of older adults, but less studies have investigated the roles of different sources of social support (i.e., family and friend support) on different facets of EWB (i.e., positive affect and negative affect) simultaneously. Methodology and Findings: In this study, the associations between family/friend support and positive/negative affect were investigated in a sample of 700 Chinese elderly. The EWB and social support were measured with a 12-item affective wordlist (Kahneman et al., 2004) and a self-prepared questionnaire. The results showed that (1) the order of contact frequency and mutual support followed a hierarchical order from spouse, children, to friends; (2) zero-order correlations of both family support and friend support were associated with more positive affect and less negative affect; and when compared with the relative role of family and friend support, (3) spouse (children if spouse is not available) support had greater contribution on decreasing negative affect, while friend support had greater influence on increasing positive affect, even after controlling the demographic, self-rated health and life events variables. Conclusion: Family and friend support play different roles on the two facets of EWB of the elderly. These results were better explained in light of the task specificity model rather than the hierarchical compensatory model. Moreover, positive affect may be enhanced by friend support (based on personal interests and selectable) rather than family support (bonded by kinship and not selectable), which added evidences to the socioemotional selectivity theory. [ABSTRACT FROM AUTHOR]

Suicide is not only an individual phenomenon, but it is also influenced by social and environmental factors. With the high suicide rate and the abundance of social media data in South Korea, we have studied the potential of this new medium for predicting completed suicide at the population level. We tested two social media variables (suicide-related and dysphoria-related weblog entries) along with classical social, economic and meteorological variables as predictors of suicide over 3 years (2008 through 2010). Both social media variables were powerfully associated with suicide frequency. The suicide variable displayed high variability and was reactive to celebrity suicide events, while the dysphoria variable showed longer secular trends, with lower variability. We interpret these as reflections of social affect and social mood, respectively. In the final multivariate model, the two social media variables, especially the dysphoria variable, displaced two classical economic predictors – consumer price index and unemployment rate. The prediction model developed with the 2-year training data set (2008 through 2009) was validated in the data for 2010 and was robust in a sensitivity analysis controlling for celebrity suicide effects. These results indicate that social media data may be of value in national suicide forecasting and prevention. [ABSTRACT FROM AUTHOR]