The COVID-19 pandemic, as the most widespread global public health event of the 21st century, has not only posed a significant threat to physical health but has also triggered an unprecedented mental health crisis. This ongoing public health emergency, characterized by disease threats, social isolation measures, and socioeconomic turmoil, has had a profound negative impact on the mental health of populations worldwide. Post-COVID-19 condition (often clinically termed Post-COVID-19 syndrome) refers to the broad spectrum of signs, symptoms, and conditions that continue or develop beyond the acute phase of SARS-CoV-2 infection. The related term Long COVID is frequently used in patient-led and public discourse to describe the often-debilitating, persistent experience of these symptoms. In this manuscript, we use “post-COVID-19” as the overarching term to encompass the long-term health consequences following infection, which aligns with our study’s focus on the population-wide mental health sequelae in the aftermath of the pandemic. This condition is a multisystem disease whose occurrence does not depend on the severity of the acute infection; even individuals with mild or asymptomatic initial infections may experience persistent symptoms research indicates that this syndrome not only manifests as physical symptoms, but is also significantly associated with the occurrence and development of mental health issues such as depression and anxiety symptoms (Castanares-Zapatero et al., 2022; Fernandez-De-Las-Penas et al., 2024).

The various challenges caused by the COVID-19 pandemic are undergoing complex changes. In the post-pandemic era, public health concerns have broadened beyond acute infection to include the long-term impact on health, psychological and social pressures, disruption to the order of life, and economic uncertainty (Di Gessa et al., 2022). These symptoms not only affect individuals’ daily lives, but also place a burden on families and society. For some time to come, the global economy will remain severe, employment pressures will remain high, and the long-term effects of COVID-19 will continue. The negative emotions of fear, anxiety, and tension that the public experiences as a result, and the negative perceptions and behaviors that these emotions trigger, are likely to persist (Prashar, 2023). Critically, the nature of perceived stress in this context has evolved—it is now characterized by chronic uncertainty and multi-domain threats (e.g., health, economic, social), which may fundamentally shape the contemporary mental health symptom network.

Research indicates a significant burden of mental health sequelae among COVID-19 survivors, including clinically significant anxiety, depressive symptoms, sleep disorders, and post-traumatic stress disorder (PTSD) (Nalbandian et al., 2021). A recent meta-analysis study published by the European Centre for Disease Prevention and Control (ECDC) provides specific prevalence estimates, showing that the standardized rates of clinically significant anxiety symptoms, depressive symptoms, and PTSD among patients with post-COVID-19 syndrome in community-based cohorts are 17.2, 17.3, and 20.6%, respectively (Taher et al., 2025). These clinical phenomena align with long-term follow-up studies of survivors of previous SARS and MERS coronavirus infections, suggesting that coronavirus infection may have specific pathophysiological effects on the central nervous system. This accumulating evidence strongly indicates that in the post-pandemic era, mental health issues have evolved into a persistent and significant public health challenge.

Psychological symptoms in patients with post-COVID-19 syndrome are highly interrelated and form complex presentations (Liu et al., 2024). Longitudinal tracking studies indicate that the availability, sources, and perceived adequacy of social support were substantially altered during the pandemic instance, physical distancing measures reduced access to in-person support from friends, family, and community networks, while simultaneously increasing reliance on digital communication channels. This transformation in the social support ecosystem not only directly affects the prognosis of individual mental health, but also has a profound impact on the resilience of economic and social development through social and psychological buffering mechanisms (Berdida et al., 2025; Ulum et al., 2025). A cohort study in the UK showed that changes in the accessibility of social support services were associated with a dose–response relationship with increased anxiety levels in the elderly population (Giebel et al., 2021). A study in China found that good social support can effectively alleviate psychological stress among community residents and healthcare workers (Pei et al., 2024).

Existing research indicates that there is a close correlation between an individual’s perceived level of stress in life and mental health issues. Prolonged high-stress conditions can have a negative impact on mental health (Collins et al., 2023; Aljawarneh et al., 2024). In related studies, scholars have explored the association between perceived life stress and symptoms of depression and anxiety in adult populations, finding a significant positive correlation between the two, i.e., the higher the level of perceived stress, the more pronounced the symptoms of depression and anxiety. This conclusion supports the view that prolonged exposure to stressful environments may exacerbate mental health problems (Zhang et al., 2024). Another study examined the impact of perceived stress on post-traumatic stress disorder (PTSD). The results showed that individuals with high levels of perceived stress were more likely to exhibit symptoms related to PTSD after experiencing a traumatic event (Brownlow et al., 2018).

However, the phenomenology of perceived stress has been uniquely reconfigured in the post-COVID-19 context. Moving beyond transient or event-specific stressors, it is now often characterized by persistent, multifaceted worries about long-term health (e.g., “Long COVID” syndrome), economic precarity, and the enduring disruption of social rhythms and support networks (Jonsdottir et al., 2025). This confluence of threats fosters a state of chronic uncertainty and perceived uncontrollability, where anticipatory anxiety and feelings of helplessness become particularly salient.

In this context, a comprehensive and in-depth understanding of the current overall health status of the public is crucial for individual wellbeing, assessing the long-term impact of the pandemic, optimizing the allocation of public health resources, and building a resilient health support system (Al-Aly et al., 2024).

To achieve these aims, we employ network analysis as our methodological framework. This approach offers a powerful means for moving beyond linear associations to model the dynamic interplay among psychological factors. Network analysis has made significant progress in psychopathology research, providing a new perspective for understanding the internal mechanisms of complex psychological disorders. It has been applied to depression (Cai et al., 2024) to the study of anxiety (Zhang et al., 2024) and post-traumatic stress disorder (Williamson et al., 2024; Park and Kim, 2020), early applications in Western populations have been complemented by studies in East Asian contexts, which confirm both the universality and cultural nuance of symptom networks. For example, a network analysis of major depressive disorder in Korea identified core symptoms bridging depression and anxiety (Park and Kim, 2020).

Compared to traditional linear models, the core advantage of network analysis lies in its ability to construct symptom networks (Symptom Networks), thereby intuitively revealing the complex dynamic interactions among various psychological variables. A network analysis of COVID-19 recovers revealed that fatigue, cognitive dysfunction, and depressive mood constitute the core nodes of the symptom network, with these symptoms reinforcing each other through specific network pathways (Dong et al., 2025). This network perspective helps explain why some patients continue to experience mental health issues even after their physical symptoms have improved (Peng et al., 2023). The application of network analysis in COVID-19 mental health research also extends to optimizing intervention strategies. Network-based personalized intervention models can identify key symptom nodes and risk pathways within specific populations (Wu et al., 2025).

The growing literature on psychological networks in Asia underscores the importance of context. A multi-national Asian study of depressive disorders revealed shared and distinct symptom profiles across different countries, suggesting the influence of cultural and regional factors on network structures (Park et al., 2020). Furthermore, network comparisons between healthcare settings in India found that the “anxiety” pathway was more central for public healthcare attendees, while “somatic” symptoms were more prominent in private settings, highlighting how systemic and access-related factors can shape the presentation of distress (Sönmez et al., 2025). These findings collectively argue for the need to examine mental health networks within specific socioecological contexts, including healthcare systems and community environments.

While network studies in Asian contexts are emerging, key gaps persist in applying this approach to understand the post-pandemic mental health landscape in China. Existing research has made significant progress, but several key limitations remain. Studies employing network analysis to simultaneously model the interrelationships among perceived stress, social support, and general mental health in large community samples are scarce, particularly in the Chinese context. Most existing studies employ linear models to explore single-variable relationships, neglecting the complex network systems that these psychosocial factors may form and their potential feedback loop mechanisms. The moderating effects of demographic characteristics, such as urban–rural differences and socioeconomic income, on the network structure of psychological symptoms also require further analysis. Especially in China’s unique sociocultural context, these demographic variables may significantly alter the topological structure of psychological symptom networks by influencing resource access and perceptions of social support.

Therefore, this study aims to achieve two primary objectives, first, to construct and examine a network model delineating the interrelationships among perceived stress, social support, and general mental health symptoms in a large, national sample of Chinese adults during the post-COVID-19 era. Second, to investigate whether and how the structure of this psychosocial network varies across key demographic groups. Specifically, based on existing literature highlighting the differential impact of social determinants on mental health, we will compare network structures across: (1) gender (sexes), given known disparities in stress perception and symptom presentation; (2) income levels, considering the established link between economic strain and psychological distress; (3) urban–rural residence, accounting for differing social ecosystems and resource access; and (4) marital status, as social ties are a key component of our model.

In summary, this study employs network analysis to map the interrelationships among social support, perceived stress, and general mental health in a large Chinese adult sample. By examining network differences across key demographic groups, we aim to provide a contemporary structural blueprint of psychosocial factors. This model can serve as a valuable benchmark for future research and inform the development of precise, context-aware mental health strategies in the ongoing recovery period.

By integrating traditional statistical methods with network analysis techniques, this study identifies key intervention targets (high-strength centrality nodes), uncovers symptom transmission pathways (bridge edge analysis), and determines group-specific patterns (multi-group network comparisons). It provides new evidence on the network-mediated role of social support systems in alleviating perceived stress and maintaining mental health, as well as the influence of different demographic characteristics on network structure, thereby shedding light on the complex mechanisms underlying mental health in the post-pandemic era (van Borkulo et al., 2023). The network structure identified in our study delineates the associative landscape of mental health constructs in the post-pandemic era. In this model, the anxiety/insomnia node (GHA) displayed the highest strength centrality, indicating it was the most statistically interconnected node and showed strong partial correlations with both somatic and depressive symptoms. This pattern of association aligns with multiple studies reporting the co-occurrence of anxiety and depression symptoms among individuals with long COVID (Kleine et al., 2024; Henning et al., 2025). Our model further suggests that symptoms such as fatigue and insomnia (captured within GHA) are closely linked to a broader network of psychological and somatic distress, pointing to a potential symptom cluster that warrants further investigation. This observation is consistent with an Italian study which found that women reported higher levels of anxiety and depression and poorer quality of life across multiple psychosocial dimensions post-pandemic (Perlini et al., 2024). Importantly, sleep disturbances like insomnia have been strongly associated with elevated psychological distress in various populations (Kupferschmitt et al., 2025), which may help explain why symptoms of anxiety and insomnia (GHA) occupied such a central position in our network model.

PLC was significantly negatively correlated with social support, representing the strongest negative association observed. This pattern aligns with the well-established inverse relationship between perceived social support and perceived stress reported in the literature (Wang et al., 2025). This strong negative bridge gains specific, practical relevance in the post-COVID-19 context. When individuals face pervasive uncertainty and threats perceived as uncontrollable (e.g., long-term health risks, economic volatility), our model indicates that higher perceived social support is strongly associated with lower levels of perceived helplessness (PLC). This finding is consistent with multiple studies (Moisoglou et al., 2024) which propose that the association between social support and psychological outcomes may involve pathways related to altered stress perception or coping (Moisoglou et al., 2024).

Furthermore, the associations between social support and other variables showed gender-specific patterns in our network model, which aligns with the well-documented gender disparities in mental health we referenced in our aims. Among women, the edge between social support and depressive symptoms was stronger, indicating a tighter statistical coupling in our sample. Conversely, for men, the edge between feelings of helplessness (PLC) and social support was more pronounced. These patterns align with the existing literature on gender and mental health we referenced in our aims. The stronger link between stress and depression in women corroborates findings that women’s stress perceptions often show stronger associations with internalizing symptoms (Xiong et al., 2020). Similarly, women took on more caregiving roles during the pandemic and faced higher psychological stress (Collins et al., 2021). From the perspective of social role theory, women traditionally bear more family caregiving responsibilities. During the pandemic, this role division was further reinforced, which was closely linked to dual work-family stress for women (Zhang and Ma, 2020). Thus, our observed gender-specific network patterns may correspond to these differing psychosocial contexts. It is important to note that gender differences in network structure may be influenced by unmeasured confounding variables, and these findings should be interpreted with appropriate caution.

The symptom network model for individuals with low income was characterized by more pronounced edges linking anxiety and depressive symptoms, as well as stronger connections between feelings of tension and perceived loss of control, providing a network-level correlate to the established link between economic strain and psychological distress. This observed pattern of tighter symptom interconnectivity aligns with the broader literature demonstrating a consistent association between economic strain and elevated psychological distress. Furthermore, the stronger association observed between social dysfunction and physical symptoms in the low-income group may reflect a context in which multiple vulnerabilities—such as potential barriers to accessing healthcare—are simultaneously present. This network pattern suggests that, within this population’s reported experience, somatic health concerns and impairments in daily functioning are more closely interrelated.

Viewed in conjunction with existing evidence, the distinct, more densely interconnected symptom network observed in low-income populations in our study underscores the potential limitation of isolated, symptom-focused approaches and supports the rationale for integrated, multi-dimensional intervention strategies. The network pattern characterized by stronger anxiety-depression and social dysfunction-physical symptom links provides a systemic rationale for addressing economic and social determinants alongside psychological distress. For instance, our finding of tighter anxiety-depression connectivity aligns with experimental evidence showing that direct economic support, such as guaranteed income, can reduce psychological distress (West and Castro, 2023). Similarly, the stronger link between social dysfunction and physical symptoms highlights the need to address barriers to healthcare access, as disparities in mental health outcomes have been shown to widen during crises like the COVID-19 pandemic, particularly for lower-income families (Cho et al., 2024). Therefore, public health strategies aiming to improve mental health in this population could consider integrating economic interventions and facilitating healthcare access as foundational components of a comprehensive approach, rather than as adjuncts to conventional psychological support alone.

Network comparison tests revealed differences in the structure of psychological symptom networks between urban and rural populations, a comparison grounded in literature on differing social ecosystems. The network for urban residents was characterized by significantly stronger connections between anxiety and depressive symptoms. In contrast, the network for rural residents featured stronger connections between somatic symptoms and social dysfunction. These distinct topological patterns suggest that the organization and interrelation of mental health issues may differ across living environments. The urban pattern, with its emphasis on internalizing symptoms, may correspond to contexts with higher social-evaluative demands and complex social interactions. Conversely, the rural pattern, emphasizing the somatic-social dysfunction link, is consistent with cross-cultural observations on distress expression in close-knit, agricultural communities (West and Castro, 2023).

These network differences align with known variations in social support ecosystems. Regarding social support systems, meta-analytic evidence indicates that urban residents’ mental health levels are significantly positively correlated with the heterogeneity of their social networks, while the correlation with network size is weaker, reflecting the unique distribution pattern of urban social capital (Huang, 2022). Conversely, rural residents’ mental health primarily benefits from emotional support provided by large, homogeneous social networks, a finding that supports the differentiated expression of the social buffering hypothesis across different social environments (Stawski et al., 2008). Collectively, our network findings and this supporting literature advocate for contextually tailored public health approaches. For urban settings, interventions might prioritize accessible stress management and emotional regulation tools to address internalizing symptoms. Rural programs, however, would benefit from integrating mental health screening and education into primary care settings to de-stigmatize psychological distress, while leveraging existing strong community networks for support delivery. Future research should further explore the underlying social determinants of these urban–rural differences and their implications for mental health service policies.

Several limitations of this study warrant consideration and directly inform the interpretation of our findings. First, the cross-sectional design and the use of an online convenience sample constrain the generalizability of our results and preclude any causal inference. Although our sample captured geographic and key demographic diversity, it likely over-represents younger, digitally connected individuals and may not fully represent the broader Chinese population. Future research should validate these network structures using probability-based sampling.

The most critical limitation stems from the cross-sectional nature of our data. The estimated network reflects conditional associations at a single time point and does not elucidate temporal or causal relationships. Consequently, while anxiety symptoms (GHA) displayed the highest strength centrality in our model, this statistical property does not establish them as a mechanistic or clinical ‘core’ driver of the network. We cannot determine whether anxiety influences other symptoms, is influenced by them, or if the relationships are bidirectional. Therefore, all interpretations regarding the ‘centrality’ of anxiety should be strictly framed as hypotheses about potential dynamic importance, to be tested in longitudinal designs. The connections identified here constitute a foundational map of interrelated symptoms. We strongly advocate for future research employing longitudinal designs, such as panel networks or N-of-1 time-series studies, to investigate temporal precedence and potential causal pathways.

The scope of this study requires clear methodological positioning. Our cross-sectional design cannot establish causality or isolate specific pandemic effects from pre-existing symptom patterns. Thus, our primary aim was not causal inference but to characterize the architecture of mental health networks within the post-pandemic context—a period shaped by collective trauma and chronic stressors. This provides a contemporary snapshot of how psychosocial factors interrelate, offering a benchmark for future research and identifying potential intervention targets within the current system. Looking forward, longitudinal network studies are needed to investigate temporal dynamics, particularly whether central symptoms (e.g., anxiety) actively influence the network over time. Furthermore, network-informed interventions—such as targeting highly connected symptoms or strengthening protective links like social support—should be empirically tested in randomized trials. Fourth, our sample exhibited a significant gender imbalance (84.6% male), which reflects the demographic composition of our initial sampling frame but limits the generalizability of findings, particularly for women. Although the permutation tests used for network comparisons are robust to unequal sample sizes (van Borkulo et al., 2023), the precision of network parameter estimates (e.g., edge weights) for the smaller female subgroup is naturally lower. Future studies should endeavor to recruit more balanced samples to confirm and extend our gender-specific findings Finally, methodological advances including multi-method assessments and more diverse sampling will enhance the validity and generalizability of future network models in this field.

This study employed a network analysis approach to map the complex associations between social support, perceived stress, and mental health in the post-pandemic era. The findings identify anxiety symptoms as a central hub within the psychological network and illustrate a strong protective association between social support and reduced feelings of helplessness. Crucially, our analysis revealed significant heterogeneity in these network structures across demographic groups. Specifically, networks were characterized by stronger stress-depression connections in women, tighter connections between anxiety and depression symptoms in low-income groups, and distinct patterns shaped by urban–rural residence and marital status.

Collectively, these findings move beyond documenting the mere presence of symptoms and provide a novel, structural blueprint for understanding the psychological sequelae of long COVID. They underscore the necessity of moving away from one-size-fits-all interventions toward precision public health strategies. Future mental health initiatives should be tailored to specific populations, focusing on key leverage points such as mitigating anxiety symptoms and bolstering social support, while being critically informed by moderating socio-ecological contexts including socioeconomic status, living environment, and gender roles.