Interpretable tourism volume forecasting with multivariate time series under the impact of COVID-19

This study proposes a novel interpretable framework to forecast the daily tourism volume of Jiuzhaigou Valley, Huangshan Mountain, and Siguniang Mountain in China under the impact of COVID-19 by using multivariate time-series data, particularly historical tourism volume data, COVID-19 data, the Baidu index, and weather data. For the first time, epidemic-related search engine data is introduced for tourism demand forecasting. A new method named the composition leading search index–variational mode decomposition is proposed to process search engine data. Meanwhile, to overcome the problem of insufficient interpretability of existing tourism demand forecasting, a new model of DE-TFT interpretable tourism demand forecasting is proposed in this study, in which the hyperparameters of temporal fusion transformers (TFT) are optimized intelligently and efficiently based on the differential evolution algorithm. TFT is an attention-based deep learning model that combines high-performance forecasting with interpretable analysis of temporal dynamics, displaying excellent performance in forecasting research. The TFT model produces an interpretable tourism demand forecast output, including the importance ranking of different input variables and attention analysis at different time steps. Besides, the validity of the proposed forecasting framework is verified based on three cases. Interpretable experimental results show that the epidemic-related search engine data can well reflect the concerns of tourists about tourism during the COVID-19 epidemic.

Research on the Public Opinion Guidance Mechanism of Major Public Health Incidents

Public opinion guidance plays a crucial role in the management of major public health incidents, and thus, exploring its mechanism is conducive to the comprehensive governance of social security. This study conducts a case study on the anti-pandemic public opinion guidance and analyzes the public opinion representation and the internal mechanism of public opinion guidance in the context of the COVID-19 in China. The findings suggest that the public opinion on the COVID-19 manifested a three-stage progressive and stable tendency and witnessed the strength of China, specifically, benefiting from the systematic and complete integration and release mechanism for anti-pandemic information, the three-dimensional mechanism for the dissemination of knowledge related to pandemic prevention and health, the innovative disclosure mechanism for precise information, and diversified channels for international public opinion guidance. The guidance mechanism proposed in this study provides significant suggestions for the public opinion guidance of global major public health incidents in future.

Risk Factors and Outcomes of Acute Kidney Injury in Critically Ill Patients With Coronavirus Disease 2019 (preprint)

Background: Coronavirus disease 2019 (COVID-19) has emerged as a major global health threat with a great number of deaths worldwide. Acute kidney injury (AKI) is a common complication in patients admitted to the intensive care unit. We aimed toassess the incidence, risk factors and in-hospital outcomes of AKI in COVID-19 patients admitted to intensive care unitMethods: we conducted a retrospective observational study in intensive care unit of Tongji hospital, which was assigned responsibility for the treatments of severe COVID-19 patients by Wuhan government. The AKI was defined and staged based onKidney Disease: Improving Global Outcomes (KDIGO) criteria. Mild AKI was defined as stage 1, and severe AKI was defined as stage 2 or stage 3. We used logistic regression analysis to evaluate AKI risk factors and the association between AKI and in-hospital mortality.Results: A total of 150 patients with COVID-19 were included in our study. The median age of patients was 70 (interquartile range, 60-80) years and 62.7% were male. 70 (46.7%) patients developed AKI during hospitalization, corresponding to the 17.3% in stage 1 and 9.3% in stage 2 and 20.0% in stage 3, respectively. Compared to patients without AKI, patients with AKI had higher proportion of mechanical ventilation mortality and higher in-hospital mortality. 95.5% patients with severe AKI received mechanical ventilation and in-hospital mortality was up to 79.5%. Severe AKI was independently associated with high in-hospital mortality (OR: 4.30; 95% CI: 1.83-10.10). Logistic regression analysis demonstrated that high serum interleukin-6 (OR: 2.54; 95%CI: 1.00-6.42) and interleukin-10 (OR: 3.02; 95%CI: 1.17-7.82) were risk factors for severe AKI development.Conclusions: severe AKI was associated with high in-hospital mortality and inflammatory response may play a role in AKI development in critically ill patients with COVID-19.