Does the website credibility of online travel agencies (OTAs) affect customers' evaluation and future intentions?

In an uncertain and escalating risk period resulting from the prolonged pandemic crisis, this study aimed to identify the dimensional nature of online travel agencies' (OTAs) website credibility, and empirically investigate the effects of its components on attitude and behavioral intentions. This study was conducted by collecting 559 questionnaires from mainland Chinese OTA users in the middle of the COVID-19 pandemic. The data analyses showed that OTA website credibility comprised six components. Other proposed paths, with the exception of four, were significant at the .05 or .001 level. Interestingly, the paths between content credibility and attitude toward the OTA and between content credibility and loyalty to the OTA were not significant. However, overall, it was confirmed that OTA website credibility determined attitude toward the OTA and loyalty to the OTA, which led to behavioral intention.

Savable but Lost Lives when ICU Is Overloaded: a Model from 733 Patients in Epicenter Wuhan, China

Coronavirus Disease 2019 (COVID-19) causes a sudden turnover to bad at some checkpoints and thus needs the intervention of intensive care unit (ICU). This resulted in urgent and large needs of ICUs posed great risks to the medical system. Estimating the mortality of critical in-patients who were not admitted into the ICU will be valuable to optimize the management and assignment of ICU. Retrospective, 733 in-patients diagnosed with COVID-19 at a local hospital (Wuhan, China), as of March 18, 2020. Demographic, clinical and laboratory results were collected and analyzed using machine learning to build a predictive model. Considering the shortage of ICU beds at the beginning of disease emergence, we defined the mortality for those patients who were predicted to be in needing ICU care yet they did not as Missing-ICU (MI)-mortality. To estimate MI-mortality, a prognostic classification model was built to identify the in-patients who may need ICU care. Its predictive accuracy was 0.8288, with an AUC of 0.9119. On our cohort of 733 patients, 25 in-patients who have been predicted by our model that they should need ICU, yet they did not enter ICU due to lack of shorting ICU wards. Our analysis had shown that the MI-mortality is 41%, yet the mortality of ICU is 32%, implying that enough bed of ICU in treating patients in critical conditions.

Machine Learning to Predict ICU Admission, ICU Mortality and Survivors' Length of Stay among COVID-19 Patients: Toward Optimal Allocation of ICU Resources

COVID-19 causes burdens to the ICU. Evidence-based planning and optimal allocation of the scarce ICU resources is urgently needed but remains unaddressed. This study aims to identify variables and test the accuracy to predict the need for ICU admission, death despite ICU care, and among survivors, length of ICU stay, before patients were admitted to ICU. Retrospective data from 733 in-patients confirmed with COVD-19 in Wuhan, China, as of March 18, 2020. Demographic, clinical and laboratory were collected and analyzed using machine learning to build the predictive models. The built machine learning model can accurately assess ICU admission, length of ICU stay, and mortality in COVID-19 patients toward optimal allocation of ICU resources. The prediction can be done by using the clinical data collected within 1-15 days before the actual ICU admission. Lymphocyte absolute value involved in all prediction tasks with a higher AUC.

[Comparison of heart failure and COVID-19 in chest CT features and clinical characteristics].

Objective: To identify the characteristics including clinical features and pulmonary computed tomography (CT) features of heart failure and COVID-19. Methods: This study was a retrospective study. A total of 7 patients with heart failure and 12 patients with COVID-19 in the Second Xiangya Hospital of Central South University between December 1, 2019 and February 15, 2020 were enrolled. The baseline clinical and imaging features of the two groups were statistically analyzed. Results: There was no significant difference in age and sex between the two groups(both P>0.05), but the incidence of epidemiological contact history, fever or respiratory symptoms in the COVID-19 group was significantly higher than that in the heart failure group (12/12 vs. 0, P<0.001; 12/12 vs. 4/7, P=0.013). While the proportion of cardiovascular diseases and impaired cardiac function was significantly less than that of the heart failure group(2/12 vs.7/7, P<0.001；0 vs.7/7, P<0.001). For imaging features, both groups had ground-glass opacity and thickening of interlobular septum, but the ratio of central and gradient distribution was higher in patients with heart failure than that in patients with COVID-19 (4/7 vs. 1/12, P=0.04). In heart failure group, the ratio of the expansion of pulmonary veins was also higher (3/7 vs. 0，P=0.013), and the lung lesions can be significantly improved after effective anti-heart failure treatment. Besides, there were more cases with rounded morphology in COVID-19 group（9/12 vs. 2/7, P=0.048）. Conclusions: More patients with COVID-19 have epidemiological history and fever or respiratory symptoms. There are significant differences in chest CT features, such as enlargement of pulmonary veins, lesions distribution and morphology between heart failure and COVID-19.