The relationship between dimensions of empathy and symptoms of depression among university students during the COVID-19 pandemic: A network analysis.

Background: The relationship between different dimensions of empathy and individual symptoms of depression during the COVID-19 pandemic remains unclear, despite the established link between empathy and depression. The network analysis offers a novel framework for visualizing the association between empathy and depression as a complex system consisting of interacting nodes. In this study, we investigated the nuanced associations between different dimensions of empathy and individual symptoms of depression using a network model during the pandemic. Methods: 1,177 students completed the Chinese version of the Interpersonal Reactivity Index (IRI), measuring dimensions of empathy, and the Chinese version of the Patient Health Questionnaire-9 (PHQ-9), measuring symptoms of depression. First, we investigated the nuanced associations between different dimensions of empathy and individual depressive symptoms. Then, we calculated the bridge expected influence to examine how different dimensions of empathy may activate or deactivate the symptoms of depression cluster. Finally, we conducted a network comparison test to explore whether network characteristics such as empathy-depression edges and bridge nodes differed between genders. Results: First, our findings showed that personal distress was positively linked to symptoms of depression. These symptoms involved psychomotor agitation or retardation (edge weight = 0.18), sad mood (edge weight = 0.12), trouble with concentrating (edge weight = 0.11), and guilt (edge weight = 0.10). Perspective-taking was found to be negatively correlated with trouble with concentrating (edge weight = -0.11). Empathic concern was negatively associated with suicidal thoughts (edge weight = -0.10) and psychomotor agitation or retardation (edge weight = -0.08). Fantasy was not connected with any symptoms of depression. Second, personal distress and empathic concern were the most positive and negative influential nodes that bridged empathy and depression (values of bridge expected influence were 0.51 and -0.19 and values of predictability were 0.24 and 0.24, respectively). The estimates of the bridge expected influence on the nodes were adequately stable (correlation stability coefficient = 0.75). Finally, no sex differences in the studied network characteristics were observed. Conclusions: This study applied network analysis to reveal potential pathways between different dimensions of empathy and individual symptoms of depression. The findings supported the existing theoretical system and contribute to the theoretical mechanism. We have also made efforts to suggest interventions and preventions based on personal distress and empathic concern, the two most important dimensions of empathy for depressive symptoms. These efforts may help Chinese university students to adopt better practical methods to overcome symptoms of depression during the COVID-19 pandemic.

The relations between different components of intolerance of uncertainty and symptoms of depression during the COVID-19 pandemic: A network analysis.

Background: The relations between depression and intolerance of uncertainty (IU) have been extensively investigated during the COVID-19 pandemic. However, there is a lack of understanding on how each component of IU may differentially affect depression symptoms and vice versa. The current study used a network approach to reveal the component-to-symptom interplay between IU and depression and identify intervention targets for depression during the COVID-19 pandemic. Methods: A total of 624 college students participated in the current study. An IU-Depression network was estimated using items from the 12-item Intolerance of Uncertainty Scale and the Patient Health Questionnaire-9. We examined the network structure, node centrality, and node bridge centrality to identify component-to-symptom pathways, central nodes, and bridge nodes within the IU-Depression network. Results: Several distinct pathways (e.g., "Frustration when facing uncertainty" and "Feelings of worthlessness") emerged between IU and Depression. "Fatigue" and "Frustration when facing uncertainty" were identified as the central nodes in the estimated network. "Frustration when facing uncertainty," "Psychomotor agitation/retardation," and "Depressed or sad mood" were identified as bridging nodes between the IU and Depression communities. Conclusion: By delineating specific pathways between IU and depression and highlighting the influential role of "Frustration when facing uncertainty" in maintaining the IU-Depression co-occurrence, current findings may inform targeted prevention and interventions for depression during the COVID-19 pandemic.

Peak fraction of infected in epidemic spreading for multi-community networks

One of the most effective strategies to mitigate the global spreading of a pandemic (e.g. coronavirus disease 2019) is to shut down international airports. From a network theory perspective, this is since international airports and flights, essentially playing the roles of bridge nodes and bridge links between countries as individual communities, dominate the epidemic spreading characteristics in the whole multi-community system. Among all epidemic characteristics, the peak fraction of infected, I-ma(x), is a decisive factor in evaluating an epidemic strategy given limited capacity of medical resources but is seldom considered in multi-community models. In this article, we study a general two-community system interconnected by a fraction r of bridge nodes and its dynamic properties, especially I-max, under the evolution of the susceptibleinfected-recovered model. Comparing the characteristic time scales of different parts of the system allows us to analytically derive the asymptotic behaviour of I-max with r, as r -> 0, which follows different power-law relations in each regime of the phase diagram. We also detect crossovers when I-max changes from one power law to another, crossing different power-law regimes as driven by r. Our results enable a better prediction of the effectiveness of strategies acting on bridge nodes, denoted by the power-law exponent epsilon(I) as in I-max proportional to r(1/epsilon I).