Leveraging data analytics to understand the relationship between restaurants' safety violations and COVID-19 transmission.

This paper leverages natural language processing, spatial analysis, and statistical analysis to examine the relationship between restaurants' safety violations and COVID-19 cases. We used location-based consumers' complaints data during the early stage of business reopening in Florida, USA. First, statistical analysis was conducted to examine the correlation between restaurants' safety violations and COVID-19 transmission. Second, a neural network-based deep learning model was developed to perform topic modeling based on consumers' complaints. Third, spatial modeling of the complaints' geographic distributions was performed to identify the hotspots of consumers' complaints and COVID-19 cases. The results reveal a positive relationship between consumers' complaints about restaurants' safety violations and COVID-19 cases. In particular, consumers' complaints about personal protection measures had the highest correlation with COVID-19 cases, followed by environmental safety measures. Our analytical methods and findings shed light on customers' behavioral shifts and hospitality businesses' adaptive practices during a pandemic.

Corrigendum to "Leveraging data analytics to understand the relationship between restaurants' safety violations and COVID-19 transmission" [Int. J. Hosp. Manag. 104 (2022) 103241].

[This corrects the article DOI: 10.1016/j.ijhm.2022.103241.].

AA-forecast: anomaly-aware forecast for extreme events.

Time series models often are impacted by extreme events and anomalies, both prevalent in real-world datasets. Such models require careful probabilistic forecasts, which is vital in risk management for extreme events such as hurricanes and pandemics. However, it's challenging to automatically detect and learn from extreme events and anomalies for large-scale datasets which often results in extra manual efforts. Here, we propose an anomaly-aware forecast framework that leverages the effects of anomalies to improve its prediction accuracy during the presence of extreme events. Our model has trained to extract anomalies automatically and incorporates them through an attention mechanism to increase the accuracy of forecasts during extreme events. Moreover, the framework employs a dynamic uncertainty optimization algorithm that reduces the uncertainty of forecasts in an online manner. The proposed framework demonstrated consistent superior accuracy with less uncertainty on three datasets with different varieties of anomalies over the current prediction models.