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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: In Sub-Saharan Africa, policy changes have begun to pave the way for community distribution of injectable contraceptives but sustaining such efforts remains challenging. Combining social marketing with community-based distribution provides an opportunity to recover some program costs and compensate workers with proceeds from contraceptive sales. This paper proposes a model for increasing access to injectable contraceptives in rural settings by using community-based distributers as social marketing agents and incorporating financing systems to improve sustainability. Methods: This intervention was implemented in three districts of the Central Zone of Tigray, Ethiopia and program data has been collected from November 2011 through October 2012. A total of 137 Community Based Reproductive Health Agents (CBRHAs) were trained to provide injectable contraceptives and were provided with a loan of 25 injectable contraceptives from a drug revolving fund, created with project funds. The price of a single dose credited to a CBRHA was 3 birr ($0.17) and they provide injections to women for 5 birr ($0.29), determined with willingness-to-pay data. Social marketing was used to create awareness and generate demand. Both quantitative and qualitative methods were used to examine important feasibility aspects of the intervention. Results: Forty-four percent of CBRHAs were providing family planning methods at the time of the training and 96% believed providing injectable contraceptives would improve their services. By October 2012, 137 CBRHAs had successfully completed training and provided 2541 injections. Of total injections, 47% were provided to new users of injectable contraceptives. Approximately 31% of injections were given for free to the poorest women, including adolescents. Conclusions: Insights gained from the first year of implementation of the model provide a framework for further expansion in Tigray, Ethiopia. Our experience highlights how program planners can tailor interventions to match family planning preferences and create more sustainable contraceptive service provision with greater impact. [ABSTRACT FROM AUTHOR]

We formulated a network-based model to understand how risk behavior change in conjunction with failure of prophylactic interventions can lead to unintended outcomes where “less (intervention) is more (effective).” Our model captures the distinction between one- and two-sided risk behavior change. In one-sided situations (e.g. influenza/H1N1) it is sufficient for either individual in an interaction to exhibit risk behavior change whereas in two-sided situations (e.g. AIDS/HIV) it is necessary for both individuals in the interaction to exhibit risk behavior change, for a potential transmission of the disease. A central discovery is that this phenomenon occurs at differing levels of intervention coverage depending upon the “sidedness” of the interaction. We find that for one-sided interactions, sufficiently high vaccination coverage is necessary for mitigating the effects of risk behavior; for two-sided interactions, it is essential to combine prophylactic treatments with programs aimed at reducing risky behavior. Furthermore, again dependent on the “sidedness,” targeting highly connected nodes can be strictly worse than uniformly random interventions at the same level of coverage. [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]