ABSTRACT
The regularity and demand predictions of shared cycling are very necessary and challenging for the management and development of urban pedestrian and bicycle traffic. The bicycle-sharing system has the problem of spatial and temporal demand fluctuations and presents a very complex nonlinear regularity. The demand for shared bicycles is affected by many factors, including time, space, weather and the situation of COVID-19. This study proposes a new bicycle-sharing demand forecasting model (USTARN) based on the impact of COVID-19, which combines urban computing and spatiotemporal attention residual network. USTARN consists of two parts. In the first part, a spatiotemporal attention residual network model is established to learn the temporal correlation and spatial correlation of shared bicycle demand. The temporal characteristic branches of each spatial small region are trained, respectively, to predict the shared bicycle demand in batches in different regions and periods according to the historical data. In order to improve the prediction accuracy of the model, the second part of the model adjusts and redistributes the prediction results of the first part by learning other information of the city, such as the severity of COVID-19, weather, temperature, wind speed and holidays. It can predict the demand for shared bicycles in different urban areas in different periods and different severities of COVID-19. This study uses the order data of shared bicycles during the period of COVID-19 in 2020 obtained from the open data platform of Shenzhen municipal government as verification, analyzes the spatiotemporal regularity of the system demand and discusses the impact of the number of newly diagnosed patients and the daily minimum temperature on the demand for shared bicycles. The results show that USTARN can fully reflect time, space, the epidemic situation, weather and temperature, and the prediction results of the impact of wind speed and other factors on the demand for shared bicycles are better than the classical methods.
ABSTRACT
BACKGROUND: The world is currently struggling with the Coronavirus disease 2019 (COVID-19) pandemic. Dietary supplements (DSs) and herbal medicine provide a potentially convenient and accessible method for its recovery, but direct evidence is limited. OBJECTIVE: This study aims to investigate the effectiveness of DSs and herbs in patients with COVID-19. METHODS: A systematic literature search was conducted in multiple electronic English and Chinese databases. Randomized controlled trials (RCTs) involving DSs or herbal medicine interventions on patients with COVID-19 from November 2019 to February 2021 were included. Data was extracted, summarized and critically examined. RESULTS: Out of 9402 records identified in the initial search, twelve RCTs were included in this review. Risk of bias of these RCTs was deemed high. Most of the trials were of low methodologic quality. Nine studies showed herbal supplements were beneficial to the recovery of COVID-19 patients; zinc sulfate could shorten the duration of loss of smell but not total recovery from COVID-19. No severe adverse events were reported. CONCLUSION: Herbal supplements may help patients with COVID-19, zinc sulfate is likely to shorten the duration of olfactory dysfunction. DS therapy and herbal medicine appear to be safe and effective adjuvant therapies for patients with COVID-19. These results must be interpreted with caution due to the overall low quality of the included trials. More well-designed RCTs are needed in the future.