Interactive social contagions and co-infections on complex networks.

What we are learning about the ubiquitous interactions among multiple social contagion processes on complex networks challenges existing theoretical methods. We propose an interactive social behavior spreading model, in which two behaviors sequentially spread on a complex network, one following the other. Adopting the first behavior has either a synergistic or an inhibiting effect on the spread of the second behavior. We find that the inhibiting effect of the first behavior can cause the continuous phase transition of the second behavior spreading to become discontinuous. This discontinuous phase transition of the second behavior can also become a continuous one when the effect of adopting the first behavior becomes synergistic. This synergy allows the second behavior to be more easily adopted and enlarges the co-existence region of both behaviors. We establish an edge-based compartmental method, and our theoretical predictions match well with the simulation results. Our findings provide helpful insights into better understanding the spread of interactive social behavior in human society.

Predicting the epidemic threshold of the susceptible-infected-recovered model.

Researchers have developed several theoretical methods for predicting epidemic thresholds, including the mean-field like (MFL) method, the quenched mean-field (QMF) method, and the dynamical message passing (DMP) method. When these methods are applied to predict epidemic threshold they often produce differing results and their relative levels of accuracy are still unknown. We systematically analyze these two issues-relationships among differing results and levels of accuracy-by studying the susceptible-infected-recovered (SIR) model on uncorrelated configuration networks and a group of 56 real-world networks. In uncorrelated configuration networks the MFL and DMP methods yield identical predictions that are larger and more accurate than the prediction generated by the QMF method. As for the 56 real-world networks, the epidemic threshold obtained by the DMP method is more likely to reach the accurate epidemic threshold because it incorporates full network topology information and some dynamical correlations. We find that in most of the networks with positive degree-degree correlations, an eigenvector localized on the high k-core nodes, or a high level of clustering, the epidemic threshold predicted by the MFL method, which uses the degree distribution as the only input information, performs better than the other two methods.

Association of heart valve calcification with cardiovascular outcomes in patients on maintenance hemodialysis / 南方医科大学学报

<p><b>OBJECTIVE</b>To investigate the impact of heart valve calcification (HVC) on cardiovascular outcomes in patients on maintenance hemodialysis (MHD).</p><p><b>METHODS</b>We enrolled 302 Chinese patients on MHD between 2009 and 2011 including 99 with HVC identified by echocardiography screening. All the patients were followed up for 2 years and survival analysis was performed with all-cause mortality, cardiovascular mortality and new onset cardiovascular events as the endpoints. Cox regression analysis was used for analyzing the impact of heart valve calcification on the cardiovascular outcomes of the patients.</p><p><b>RESULTS</b>The mean age of the total patients was 58.2∓15.0 years when receiving the initial MHD, and 53.6% were male patients. The overall mortality, cardiovascular mortality and new on-set cardiovascular events in HVC and non-HVC groups were 30.3% vs 16.3%, 22.2% vs 6.9%, and 48.5% vs 25.6%, respectively (P<0.05). Kaplan-Meier survival analysis showed a significant difference in all-cause mortality (P=0.006), cardiovascular mortality (P<0.001) and new-onset cardiovascular events (P<0.001) between HVC and non-HVC groups. After adjustment, Cox regression analysis identified HVC as a risk factor for increased all-cause mortality (HR=1.88; 95%CI: 1.11-3.19), cardiovascular mortality (HR=3.47, 95%CI: 1.76-6.84) and cardiovascular events (HR=1.64, 95% CI: 1.09-2.47).</p><p><b>CONCLUSIONS</b>HVC is an independent risk factor for increased cardiovascular mortality and new cardiovascular events in patients on MHD.</p>