Real-time assessment of COVID-19 epidemic in Guangdong Province, China using mathematical models.

China government has relaxed the response measures of COVID-19 in early December 2022. In this report, we assessed the number of infections, the number of severe cases based on the current epidemic trend (October 22, 2022 to November 30, 2022) using a transmission dynamics model, called modified susceptible-exposed-infectious-removed (SEIR) to provide valuable information to ensure the medical operation of the healthcare system under the new situation. Our model showed that the present outbreak in Guangdong Province peaked during December 21, 2022 to December 25, 2022 with about 14.98 million new infections (95% CI: 14.23-15.73 million). The cumulative number of infections will reach about 70% of the province's population from December 24, 2022 to December 26, 2022. The number of existing severe cases is expected to peak during January 1, 2023 to January 5, 2023 with a peak number of approximately 101.45 thousand (95% CI: 96.38-106.52 thousand). In addition, the epidemic in Guangzhou which is the capital city of Guangdong Province is expected to have peaked around December 22, 2022 to December 23, 2022 with the number of new infections at the peak being about 2.45 million (95% CI: 2.33-2.57 million). The cumulative number of infected people will reach about 70% of the city's population from December 24, 2022 to December 25, 2022 and the number of existing severe cases is expected to peak around January 4, 2023 to January 6, 2023 with the number of existing severe cases at the peak being about 6.32 thousand (95% CI: 6.00-6.64 thousand). Predicted results enable the government to prepare medically and plan for potential risks in advance.

Predicting depression and anxiety of Chinese population during COVID-19 in psychological evaluation data by XGBoost.

BACKGROUND: Due to the onset of sudden stress, COVID-19 has greatly impacted the incidence of depression and anxiety. However, challenges still exist in identifying high-risk groups for depression and anxiety during COVID-19. Studies have identified how resilience and social support can be employed as effective predictors of depression and anxiety. This study aims to select the best combination of variables from measures of resilience, social support, and alexithymia for predicting depression and anxiety. METHODS: The eXtreme Gradient Boosting (XGBoost1) model was applied to a dataset including data on 29,841 participants that was collected during the COVID-19 pandemic. Discriminant analyses on groups of participants with depression (DE2), anxiety (AN3), comorbid depression and anxiety (DA4), and healthy controls (HC5), were performed. All variables were selected according to their importance for classification. Further, analyses were performed with selected features to determine the best variable combination. RESULTS: The mean accuracies achieved by three classification tasks, DE vs HC, AN vs HC, and DA vs HC, were 0.78, 0.77, and 0.89. Further, the combination of 19 selected features almost exhibited the same performance as all 56 variables (accuracies = 0.75, 0.75, and 0.86). CONCLUSIONS: Resilience, social support, and some demographic data can accurately distinguish DE, AN, and DA from HC. The results can be used to inform screening practices for depression and anxiety. Additionally, the model performance of a limited scale including only 19 features indicates that using a simplified scale is feasible.

Self-help cognitive behavioral therapy application for COVID-19-related mental health problems: A longitudinal trial.

BACKGROUND AND AIM: Recently, the availability and usefulness of mobile self-help mental health applications have increased, but few applications deal with COVID-19-related psychological problems. This study explored the intervention efficacy of a mobile application on addressing psychological problems related to COVID-19. METHODS: A longitudinal control trial involving 129 Chinese participants with depression symptoms was conducted through the mobile application "Care for Your Mental Health and Sleep during COVID-19" (CMSC) based on WeChat. Participants were divided into two groups: mobile internet cognitive behavioral therapy (MiCBT) and wait-list. The primary outcome was improvement in depression symptoms. Secondary outcomes included improvement in anxiety and insomnia. The MiCBT group received three self-help CBT intervention sessions in one week via CMSC. RESULTS: The MiCBT group showed significant improvement in depression and insomnia (allP < 0.05) compared with the wait-list group. Although both groups showed significant improvement in anxiety at the intervention's end, compared with the wait-list group, the MiCBT group had no significant advantage. Correlation analysis showed that improvement in depression and anxiety had a significant positive association with education level. Changes in insomnia were significantly negatively correlated with anxiety of COVID-19 at the baseline. CMSC was considered helpful (n=68, 81.9 %) and enjoyable (n=54, 65.9 %) in relieving depression and insomnia during the COVID-19 outbreak. CONCLUSIONS: CMSC is verified to be effective and convenient for improving COVID-19-related depression and insomnia symptoms. A large study with sufficient evidence is required to determine its continuous effect on reducing mental health problems during the pandemic.