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Background: Fatigue is a common symptom of long COVID syndrome. Compared to male survivors, females have a higher incidence of post-COVID fatigue. Therefore, long-term follow-up is necessary to understand which groups of females are more vulnerable to post-COVID fatigue., Methods: This is a nested case-control study of female COVID-19 survivors who were discharged from two designated hospitals in Wuhan, China in 2020, and received 2-year follow-up from March 1 to April 6, 2022. All patients completed the Checklist Individual Strength-subscale subjective fatigue (CIS-fatigue), a chronic obstructive pulmonary disease (COPD) assessment test (CAT), and the Hospital Anxiety and Depression Scale (HADS; including the HADS-Anxiety [HADS-A] and the HADS-Depression [HADS-D]). Individuals with CIS-fatigue scores of 27 or higher were classified as cases. The risk factors for fatigue was analysed with multivariable logistic regression analysis., Results: A total of 899 female COVID-19 survivors were enrolled for analysis, including 47 cases and 852 controls. Compared with controls, cases had higher CAT, HADS-A and HADS-D scores, and showed a higher prevalence of symptoms, including anxiety (cases vs. controls, 44.7% vs. 4.0%, p < 0.001), chest tightness (21.2% vs. 2.3%, p < 0.001), dyspnoea (19.1% vs. 0.8%, p < 0.001) and so on. In multivariable logistic regression analysis, age (OR, 1.03; 95% CI, 1.01-1.06; p = 0.02) and cerebrovascular disease (OR, 11.32; 95% CI, 2.87-43.00; p < 0.001) were risk factors for fatigue. Fatigue had a statistically significant moderate correlation with depression (r = 0.44, p < 0.001), but not with CAT ≥ 10., Conclusion: Female COVID-19 patients who had cerebrovascular disease and older age have higher risk of fatigue. Patients with fatigue have higher CAT scores, and are more likely to have concurrent depression., (© 2023. The Author(s).)

Importance: Relatively little is known about the persistence of symptoms in patients with COVID-19 for more than 1 year after their acute illness., Objective: To assess the health outcomes among hospitalized COVID-19 survivors over 2 years and to identify factors associated with increased risk of persistent symptoms., Design, Setting, and Participants: This was a longitudinal cohort study of patients who survived COVID-19 at 2 COVID-19-designated hospitals in Wuhan, China, from February 12 to April 10, 2020. All patients were interviewed via telephone at 1 year and 2 years after discharge. The 2-year follow-up study was conducted from March 1 to April 6, 2022. Statistical analysis was conducted from April 20 to May 5, 2022. The severity of disease was defined by World Health Organization guideline for COVID-19., Exposures: COVID-19., Main Outcomes and Measures: The main outcome was symptom changes over 2 years after hospital discharge. All patients completed a symptom questionnaire for evaluation of symptoms, along with a chronic obstructive pulmonary disease assessment test (CAT) at 1-year and 2-year follow-up visits., Results: Of 3988 COVID-19 survivors, a total of 1864 patients (median [IQR] age, 58.5 [49.0-68.0] years; 926 male patients [49.7%]) were available for both 1-year and 2-year follow-up visits. The median (IQR) time from discharge to follow-up at 2 years was 730 (719-743) days. At 2 years after hospital discharge, 370 patients (19.8%) still had symptoms, including 224 (12.0%) with persisting symptoms and 146 (7.8%) with new-onset or worsening of symptoms. The most common symptoms were fatigue, chest tightness, anxiety, dyspnea, and myalgia. Most symptoms resolved over time, but the incidence of dyspnea showed no significant change (1-year vs 2-year, 2.6% [49 patients] vs 2.0% [37 patients]). A total of 116 patients (6.2%) had CAT total scores of at least 10 at 2 years after discharge. Patients who had been admitted to the intensive care unit had higher risks of persistent symptoms (odds ratio, 2.69; 95% CI, 1.02-7.06; P = .04) and CAT scores of 10 or higher (odds ratio, 2.83; 95% CI, 1.21-6.66; P = .02)., Conclusions and Relevance: In this cohort study, 2 years after hospital discharge, COVID-19 survivors had a progressive decrease in their symptom burden, but those with severe disease during hospitalization, especially those who required intensive care unit admission, had higher risks of persistent symptoms. These results are related to the original strain of the virus, and their relevance to infections with the Omicron variant is not known.

Based on mobile positioning big data, this study analyzes the quantity of visitation changes of leisure tourism facilities under the impact of COVID-19, which is a direct way to evaluate the impact of COVID-19 on the leisure tourism industry. The quantity of visitation to the four types of leisure facilities (scenic spots, shopping services, sports services, science and cultural education services) in the three Chinese cities (Beijing, Shanghai, and Qingdao) decreased by 63.06% and 44.74% respectively on the Tomb Sweeping Day and the Labor Day in 2020 compared with 2019. The biggest decreasing was in science and cultural education services, followed by scenic spots and sports services, and finally shopping services. The curve of hourly visitations to leisure facilities in 2020 shows a smooth feature, indicating that the public has reduced the risk of pandemic through peak shifting leisure. The analysis of influencing factors shows that the influence of the floor area on visitation is different in different phases. From Tomb Sweeping Day to Labour Day in 2020, the influence of area on leisure behaviour changes from negative to positive. This paper provides a technical framework for assessing the impact of crisis event, and expands the protection motivation theory. [ABSTRACT FROM AUTHOR]

The COVID-19 pandemic has had a significant impact on the world, highlighting the importance of the accurate prediction of infection numbers. Given that the transmission of SARS-CoV-2 is influenced by temporal and spatial factors, numerous researchers have employed neural networks to address this issue. Accordingly, we propose a whale optimization algorithm–bidirectional long short-term memory (WOA-BILSTM) model for predicting cumulative confirmed cases. In the model, we initially input regional epidemic data, including cumulative confirmed, cured, and death cases, as well as existing cases and daily confirmed, cured, and death cases. Subsequently, we utilized the BILSTM as the base model and incorporated WOA to optimize the specific parameters. Our experiments employed epidemic data from Beijing, Guangdong, and Chongqing in China. We then compared our model with LSTM, BILSTM, GRU, CNN, CNN-LSTM, RNN-GRU, DES, ARIMA, linear, Lasso, and SVM models. The outcomes demonstrated that our model outperformed these alternatives and retained the highest accuracy in complex scenarios. In addition, we also used Bayesian and grid search algorithms to optimize the BILSTM model. The results showed that the WOA model converged fast and found the optimal solution more easily. Thus, our model can assist governments in developing more effective control measures. [ABSTRACT FROM AUTHOR]