Identifying central and bridge symptoms between fear of COVID-19 and depression: a network analysis using FCV-19S and PHQ-9.

BACKGROUND: The COVID-19 pandemic triggered a global mental health crisis, with fear and depression emerging as two of the most prevalent psychological responses. However, how specific symptoms of fear and depression interact within individuals remains unclear. Symptom-level network analysis provides a novel framework for clarifying these relationships and identifying actionable intervention targets. METHODS: Network analysis was used to examine item-level associations between fear of COVID-19 (assessed using the Fear of COVID-19 Scale [FCV-19S]) and depression (assessed using the Patient Health Questionnaire-9 [PHQ-9]) among a large sample of Bangladeshi adults (N = 3388; 51.8% female; mean age = 30.1 years). Networks were estimated using EBICglasso with polychoric correlations. Centrality and bridge metrics were computed, and network comparison tests assessed invariance across gender and occupational groups. RESULTS: The estimated symptom network showed the strongest within-domain connection in both fear and depression clusters, as well as meaningful cross-domain associations, particularly between sleep-related symptoms. The most central symptoms were "feeling down or hopeless", "low energy", and "clammy hands". "Sleep due to COVID worry" and "feeling down or hopeless" were key bridge symptoms. The network demonstrated excellent stability (CS-coefficient = 0.75) and showed no significant differences in overall network structure or global strength across gender and occupational groups, although nominal edge- and node-level differences were observed. CONCLUSIONS: The present study provides the first comprehensive, symptom-level map of the interplay between COVID-19 fear and depression. The findings identify sleep and affective symptoms as highly connected nodes that warrant further investigation as potential intervention targets.