The Association Between Quarantine Duration and Psychological Outcomes, Social Distancing, and Vaccination Intention During the Second Outbreak of COVID-19 in China.

Objectives: To examine the association between quarantine duration and psychological outcomes, social distancing, as well as vaccination intention during the second outbreak of COVID-19 in China. Methods: A cross-sectional online survey was conducted in January 2021. Participants were invited to complete the measurement of quarantine duration, social distancing, psychological distress, wellbeing (WHO-5), and vaccination intention. Multiple linear regression and logistic regression were performed to examine the relationship between quarantine duration and psychological distress, wellbeing, social distancing, and vaccination intention. Results: Of the 944 participants, 17.2% of the participants experienced quarantine. Quarantine for 1-7 days increased the social distancing (ß = 2.61 95% confidence interval (CI) 1.90-3.33) and vaccination intention (OR = 2.16 95% CI 1.22-3.82). Quarantine for >7 days was associated with the increased social distancing (ß = 3.00 95% CI 2.37-3.64) and psychological distress (ß = 1.03 95% CI 0.22-1.86), and decreased wellbeing (ß = 1.27 95% CI 0.29-2.26). Conclusion: Longer quarantine duration showed increased social distancing, increased psychological distress, and decreased wellbeing. Quarantine for 1-7 days was associated with increased vaccination intention.

The Association Between Quarantine Duration and Psychological Outcomes, Social Distancing, and Vaccination Intention During the Second Outbreak of COVID-19 in China

Objectives: To examine the association between quarantine duration and psychological outcomes, social distancing, as well as vaccination intention during the second outbreak of COVID-19 in China. Methods: A cross-sectional online survey was conducted in January 2021. Participants were invited to complete the measurement of quarantine duration, social distancing, psychological distress, wellbeing (WHO-5), and vaccination intention. Multiple linear regression and logistic regression were performed to examine the relationship between quarantine duration and psychological distress, wellbeing, social distancing, and vaccination intention. Results: Of the 944 participants, 17.2% of the participants experienced quarantine. Quarantine for 1–7 days increased the social distancing (β = 2.61 95% confidence interval (CI) 1.90–3.33) and vaccination intention (OR = 2.16 95% CI 1.22–3.82). Quarantine for >7 days was associated with the increased social distancing (β = 3.00 95% CI 2.37–3.64) and psychological distress (β = 1.03 95% CI 0.22–1.86), and decreased wellbeing (β = 1.27 95% CI 0.29–2.26). Conclusion: Longer quarantine duration showed increased social distancing, increased psychological distress, and decreased wellbeing. Quarantine for 1–7 days was associated with increased vaccination intention.

An efficient mixture of deep and machine learning models for COVID-19 diagnosis in chest X-ray images.

A newly emerged coronavirus (COVID-19) seriously threatens human life and health worldwide. In coping and fighting against COVID-19, the most critical step is to effectively screen and diagnose infected patients. Among them, chest X-ray imaging technology is a valuable imaging diagnosis method. The use of computer-aided diagnosis to screen X-ray images of COVID-19 cases can provide experts with auxiliary diagnosis suggestions, which can reduce the burden of experts to a certain extent. In this study, we first used conventional transfer learning methods, using five pre-trained deep learning models, which the Xception model showed a relatively ideal effect, and the diagnostic accuracy reached 96.75%. In order to further improve the diagnostic accuracy, we propose an efficient diagnostic method that uses a combination of deep features and machine learning classification. It implements an end-to-end diagnostic model. The proposed method was tested on two datasets and performed exceptionally well on both of them. We first evaluated the model on 1102 chest X-ray images. The experimental results show that the diagnostic accuracy of Xception + SVM is as high as 99.33%. Compared with the baseline Xception model, the diagnostic accuracy is improved by 2.58%. The sensitivity, specificity and AUC of this model reached 99.27%, 99.38% and 99.32%, respectively. To further illustrate the robustness of our method, we also tested our proposed model on another dataset. Finally also achieved good results. Compared with related research, our proposed method has higher classification accuracy and efficient diagnostic performance. Overall, the proposed method substantially advances the current radiology based methodology, it can be very helpful tool for clinical practitioners and radiologists to aid them in diagnosis and follow-up of COVID-19 cases.