Prevalence of anxiety, depressive and insomnia symptoms among the different groups of people during COVID-19 pandemic: An overview of systematic reviews and meta-analyses

Since the pandemic of the novel 2019 coronavirus disease (COVID-19), in addition to the harm caused by the disease itself, the psychological damage caused to the public by the pandemic is also a serious problem. The aim of our study was to summarize the systematic reviews/meta-analyses (SRs/MAs) of the prevalence of anxiety, depression and insomnia in different populations during the COVID-19 pandemic and to qualitatively evaluate these SRs/MAs. We searched the Cochrane Library, PubMed and Web of Science to obtain SRs/MAs related to anxiety, depression, and insomnia in different populations during the COVID-19 pandemic. The main populations we studied were healthcare workers (HCWs), college students (CSs), COVID-19 patients (CPs), and the general populations (GPs). A subgroup analysis was performed of the prevalence of psychological disorders. A total of 42 SRs/MAs (8,200,330 participants) were included in calculating and assessing the prevalence of anxiety, depression, and insomnia in these populations. The results of subgroup analysis showed that the prevalence of anxiety in different populations were: HCWs (20–44%), CSs (24–41%), CPs (15–47%), and GPs (22–38%). The prevalence of depression were: HCWs (22–38%), CSs (22–52%), CPs (38–45%), and GPs (16–35%), statistically significant differences between subgroups (p < 0.05). The prevalence of insomnia were: HCWs (28–45%), CSs (27–33%), CPs (34–48%), and GPs (28–35%), statistically significant differences between subgroups (p < 0.05). The comparison revealed a higher prevalence of psychological disorders in the CP group, with insomnia being the most pronounced. The methodological quality of the included SRs/MAs was then evaluated using AMSTAR 2 tool. The results of the methodological quality evaluation showed that 13 SRs/MAs were rated “medium,” 13 were rated “low,” and 16 were rated “very low.” Through the subgroup analysis and evaluation of methodological quality, we found a higher prevalence of insomnia than anxiety and depression among the psychological disorders occurring in different populations during the pandemic, but the sample size on insomnia is small and more high-quality studies are needed to complement our findings.

Prevalence and associated factors of depressive symptoms among the young adults during the post-epidemic period - Evidence from the first wave of COVID-19 in Hubei Province, China.

INTRODUCTION: China emerged from the first wave of COVID-19 in a short period of time and returned to normal economic and living order nationwide, making China's entry into the post-COVID-19 epidemic period since April 2020. However, the COVID-19 epidemic had a great impact on young adults' psychological status and may continue into the post-epidemic period. The enormous economic, employment and entrepreneurship pressures of this period may exacerbate this negative impact. This study investigated the depression status of the young adults and put forward the suggestions on how to strengthen the psychological crisis intervention and social security to cultivate the resilience of the young adults after major public health emergencies. METHODS: This study conducted a questionnaire survey to identify the prevalence of depressive symptoms and explore the associated factors of depressive symptoms among 1069 young adults in X City, Hubei province in September 2020. And the multistage stratified random sampling method was used for sampling. Depressive symptoms were measured using the 10-item version of the Center for Epidemiological Studies Depression Scale (CES-D-10). Descriptive statistics and logistic regression analysis were adopted for statistical analysis. RESULTS: 1069 respondents (67.68% male; mean age = 28.87 ± 4.18 years; age range = 18-35 years) were included in final analyses. About 25.9% of the respondents reported depressive symptoms (CES-D-10 score = 7.28 ± 3.85). Age, marital status, employment status, monthly disposable income, the cognition, experience and social relationship of the COVID-19 epidemic, and regional discrimination were significantly associated with depressive symptoms. Being male (P = 0.025), age of 25-29 years (P = 0.011), having a household size with 4-5 (P = 0.01) and more than 8 (P = 0.012) family members, a little pessimism about the prospect of COVID-19 epidemic prevention and control (P = 0.044), often (P = 0.018) or always (P = 0.009) participation in anti-epidemic volunteer work were likely to lead to depressive symptoms. CONCLUSIONS: In the post-COVID-19 epidemic period, the psychological status of young people is generally stable, but some of them are depressed. Life, work and mental stress affect the generation of depressive symptoms among the young adults.

Can Digital Transformation Promote the Rapid Recovery of Cities from the COVID-19 Epidemic? An Empirical Analysis from Chinese Cities.

BACKGROUND: Digital transformation has become a key intervention strategy for the global response to the COVID-19 epidemic, and digital technology is helping cities recover from the COVID-19 epidemic. However, the effects of urban digital transformation on the recovery from the COVID-19 epidemic still lack mechanism analyses and empirical testing. This study aimed to explain the theoretical mechanism of urban digital transformation on the recovery from the COVID-19 epidemic and to test its effectiveness using an empirical analysis. METHODS: This study, using a theoretical and literature-based analysis, summarizes the impact mechanisms of urban digital transformation on the recovery of cities from the COVID-19 epidemic. A total of 83 large- and medium-sized cities from China are included in the empirical research sample, covering most major cities in China. The ordinary least squares (OLS) method is adopted to estimate the effect of China's urban digitalization level on population attraction in the second quarter of 2020. RESULTS: The theoretical analysis found that urban digital transformation improves the ability of cities to recover from the COVID-19 epidemic by promoting social communication, collaborative governance, and resilience. The main findings of the empirical analysis show that the digital level of a city has a significant positive effect on urban population attraction (p < 0.001). CONCLUSIONS: A positive relationship was found between urban digital transformation and the rapid recovery of cities from the COVID-19 epidemic. Digital inventions for social communication, collaborative governance, and urban resilience are an effective way of fighting the COVID-19 emergency.

The application framework of big data technology in the COVID-19 epidemic emergency management in local government-a case study of Hainan Province, China.

BACKGROUND: As COVID-19 continues to spread globally, traditional emergency management measures are facing many practical limitations. The application of big data analysis technology provides an opportunity for local governments to conduct the COVID-19 epidemic emergency management more scientifically. The present study, based on emergency management lifecycle theory, includes a comprehensive analysis of the application framework of China's SARS epidemic emergency management lacked the support of big data technology in 2003. In contrast, this study first proposes a more agile and efficient application framework, supported by big data technology, for the COVID-19 epidemic emergency management and then analyses the differences between the two frameworks. METHODS: This study takes Hainan Province, China as its case study by using a file content analysis and semistructured interviews to systematically comprehend the strategy and mechanism of Hainan's application of big data technology in its COVID-19 epidemic emergency management. RESULTS: Hainan Province adopted big data technology during the four stages, i.e., migration, preparedness, response, and recovery, of its COVID-19 epidemic emergency management. Hainan Province developed advanced big data management mechanisms and technologies for practical epidemic emergency management, thereby verifying the feasibility and value of the big data technology application framework we propose. CONCLUSIONS: This study provides empirical evidence for certain aspects of the theory, mechanism, and technology for local governments in different countries and regions to apply, in a precise, agile, and evidence-based manner, big data technology in their formulations of comprehensive COVID-19 epidemic emergency management strategies.

Digital Contact Tracing Based on a Graph Database Algorithm for Emergency Management During the COVID-19 Epidemic: Case Study.

BACKGROUND: The COVID-19 epidemic is still spreading globally. Contact tracing is a vital strategy in epidemic emergency management; however, traditional contact tracing faces many limitations in practice. The application of digital technology provides an opportunity for local governments to trace the contacts of individuals with COVID-19 more comprehensively, efficiently, and precisely. OBJECTIVE: Our research aimed to provide new solutions to overcome the limitations of traditional contact tracing by introducing the organizational process, technical process, and main achievements of digital contact tracing in Hainan Province. METHODS: A graph database algorithm, which can efficiently process complex relational networks, was applied in Hainan Province; this algorithm relies on a governmental big data platform to analyze multisource COVID-19 epidemic data and build networks of relationships among high-risk infected individuals, the general population, vehicles, and public places to identify and trace contacts. We summarized the organizational and technical process of digital contact tracing in Hainan Province based on interviews and data analyses. RESULTS: An integrated emergency management command system and a multi-agency coordination mechanism were formed during the emergency management of the COVID-19 epidemic in Hainan Province. The collection, storage, analysis, and application of multisource epidemic data were realized based on the government's big data platform using a centralized model. The graph database algorithm is compatible with this platform and can analyze multisource and heterogeneous big data related to the epidemic. These practices were used to quickly and accurately identify and trace 10,871 contacts among hundreds of thousands of epidemic data records; 378 closest contacts and a number of public places with high risk of infection were identified. A confirmed patient was found after quarantine measures were implemented by all contacts. CONCLUSIONS: During the emergency management of the COVID-19 epidemic, Hainan Province used a graph database algorithm to trace contacts in a centralized model, which can identify infected individuals and high-risk public places more quickly and accurately. This practice can provide support to government agencies to implement precise, agile, and evidence-based emergency management measures and improve the responsiveness of the public health emergency response system. Strengthening data security, improving tracing accuracy, enabling intelligent data collection, and improving data-sharing mechanisms and technologies are directions for optimizing digital contact tracing.

An Improved Method for the Fitting and Prediction of the Number of COVID-19 Confirmed Cases Based on LSTM

New coronavirus disease (COVID-19) has constituted a global pandemic and has spread to most countries and regions in the world. Through understanding the development trend of confirmed cases in a region, the government can control the pandemic by using the corresponding policies. However, the common traditional mathematical differential equations and population prediction models have limitations for time series population prediction, and even have large estimation errors. To address this issue, we propose an improved method for predicting confirmed cases based on LSTM (Long -Short Term Memory) neural network. This work compares the deviation between the experimental results of the improved LSTM prediction model and the digital prediction models (such as Logistic and Hill equations) with the real data as reference. Furthermore, this work uses the goodness of fitting to evaluate the fitting effect of the improvement. Experiments show that the proposed approach has a smaller prediction deviation and a better fitting effect. Compared with the previous forecasting methods, the contributions of our proposed improvement methods are mainly in the following aspects: 1) we have fully considered the spatiotemporal characteristics of the data, rather than single standardized data. 2) the improved parameter settings and evaluation indicators are more accurate for fitting and forecasting. 3) we consider the impact of the epidemic stage and conduct reasonable data processing for different stage.