ABSTRACT
The prediction system EpiSIX was used to study the COVID-19 epidemic in mainland China between November 2022 and January 2023, based on reported data from December 9, 2022, to January 30, 2023, released by The Chinese Center for Disease Control and Prevention on February 1, 2023. Three kinds of reported data were used for model fitting: the daily numbers of positive nucleic acid tests and deaths, and the daily number of hospital beds taken by COVID-19 patients. It was estimated that the overall infection rate was 87.54% and the overall case fatality rate was 0.078%-0.116% (median 0.100%). Assuming that a new COVID-19 epidemic outbreak would start in March or April of 2023, induced by a slightly more infectious mutant strain, we predicted a possible large rebound between September and October 2023, with a peak demand of between 800,000 and 900,000 inpatient beds. If no such new outbreak was induced by other variants, then the current COVID-19 epidemic course in mainland China would remain under control until the end of 2023. However, it is suggested that the necessary medical resources be prepared to manage possible COVID-19 epidemic emergencies in the near future, especially for the period between September and October 2023.
ABSTRACT
This study explores Generation Z hotel employees' perception of and behavioral intention to use advanced information technologies (ITs) before and during COVID-19. The relationship between perception and intention moderated by COVID-19 is investigated. The technology acceptance model (TAM) and innovation diffusion theory (IDT) are integrated, and compatibility is added as an additional construct to conduct this longitudinal study. Data were collected in Hong Kong in April 2019 (pre-pandemic stage) and March 2021 (pandemic stage). Regression analysis, t-test, Cochran's Q test, and correspondence analysis are applied. Follow-up in-person interviews are conducted to validate the results. The results reveal significant differences between the pre-pandemic and pandemic stages and the moderating effect of COVID-19 on the relationship between the perception of and behavioral intention to use advanced ITs. This study addresses the gap in hospitality research by integrating TAM and IDT to examine Generation Z hotel employees' behavioral intention to use advanced ITs.
ABSTRACT
OBJECTIVES: To develop predictive criteria for COVID-19-associated cytokine storm (CS), a severe hyperimmune response that results in organ damage in some patients infected with COVID-19. We hypothesised that criteria for inflammation and cell death would predict this type of CS. METHODS: We analysed 513 hospitalised patients who were positive for COVID-19 reverse transcriptase PCR and for ground-glass opacity by chest high-resolution CT. To achieve an early diagnosis, we analysed the laboratory results of the first 7 days of hospitalisation. We implemented logistic regression and principal component analysis to determine the predictive criteria. We used a 'genetic algorithm' to derive the cut-offs for each laboratory result. We validated the criteria with a second cohort of 258 patients. RESULTS: We found that the criteria for macrophage activation syndrome, haemophagocytic lymphohistiocytosis and the HScore did not identify the COVID-19 cytokine storm (COVID-CS). We developed new predictive criteria, with sensitivity and specificity of 0.85 and 0.80, respectively, comprising three clusters of laboratory results that involve (1) inflammation, (2) cell death and tissue damage, and (3) prerenal electrolyte imbalance. The criteria identified patients with longer hospitalisation and increased mortality. These results highlight the relevance of hyperinflammation and tissue damage in the COVID-CS. CONCLUSIONS: We propose new early predictive criteria to identify the CS occurring in patients with COVID-19. The criteria can be readily used in clinical practice to determine the need for an early therapeutic regimen, block the hyperimmune response and possibly decrease mortality.