Exploring customer concerns on service quality under the COVID-19 crisis: A social media analytics study from the retail industry

The COVID-19 pandemic has triggered a set of government policies and supermarket regulations, which affects customers' grocery shopping behaviours. However, the specific impact of COVID-19 on retailers at the customer end has not yet been addressed. Using text-mining techniques (i.e., sentiment analysis, topic modelling) and time series analysis, we analyse 161,921 tweets from leading UK supermarkets during the first COVID-19 lockdown. The results show the causes of sentiment change in each time series and how customer perception changes according to supermarkets’ response actions. Drawing on the social media crisis communication framework and Situational Crisis Communication theory, this study investigates whether responding to a crisis helps retail managers better understand their customers. The results uncover that customers experiencing certain social media interactions may evaluate attributes differently, resulting in varying levels of customer information collection, and grocery companies could benefit from engaging in social media crisis communication with customers. As new variants of COVID-19 keep appearing, emerging managerial problems put businesses at risk for the next crisis. Based on the results of text-mining analysis of consumer perceptions, this study identifies emerging topics in the UK grocery sector in the context of COVID-19 crisis communication and develop the sub-dimensions of service quality assessment into four categories: physical aspects, reliability, personal interaction, and policies. This study reveals how supermarkets could use social media data to better analyse customer behaviour during a pandemic and sustain competitiveness by upgrading their crisis strategies and service provision. It also sheds light on how future researchers can leverage the power of social media data with multiple text-mining methodologies.

Deep diagnostic agent forest (DDAF): A deep learning pathogen recognition system for pneumonia based on CT.

BACKGROUND: Even though antibiotics agents are widely used, pneumonia is still one of the most common causes of death around the world. Some severe, fast-spreading pneumonia can even cause huge influence on global economy and life security. In order to give optimal medication regimens and prevent infectious pneumonia's spreading, recognition of pathogens is important. METHOD: In this single-institution retrospective study, 2,353 patients with their CT volumes are included, each of whom was infected by one of 12 known kinds of pathogens. We propose Deep Diagnostic Agent Forest (DDAF) to recognize the pathogen of a patient based on ones' CT volume, which is a challenging multiclass classification problem, with large intraclass variations and small interclass variations and very imbalanced data. RESULTS: The model achieves 0.899 ± 0.004 multi-way area under curves of receiver (AUC) for level-I pathogen recognition, which are five rough groups of pathogens, and 0.851 ± 0.003 AUC for level-II recognition, which are 12 fine-level pathogens. The model also outperforms the average result of seven human readers in level-I recognition and outperforms all readers in level-II recognition, who can only reach an average result of 7.71 ± 4.10% accuracy. CONCLUSION: Deep learning model can help in recognition pathogens using CTs only, which might help accelerate the process of etiological diagnosis.

Effect of a Previous History of Antiretroviral Treatment on Clinical Picture of Patients with Co-infection of SARS-CoV-2 and HIV: A Preliminary Study.

OBJECTIVES: We aimed to explore the effect of antiretroviral treatment (ART) history on clinical characteristics of patients with co-infection of SARS-CoV-2 and HIV. METHODS: We retrospectively reviewed 20 patients with laboratory-confirmed co-infection of SARS-CoV-2 and HIV in a designated hospital. Patients were divided into medicine group (n = 12) and non-medicine group (n = 8) according to previous ART history before SARS-CoV-2 infection. RESULTS: The median age was 46.5 years and 15 (75%) were female. Ten patients had initial negative RT-PCR on admission, 5 of which had normal CT appearance and 4 were asymptomatic. Lymphocytes were low in 9 patients (45%), CD4 cell count and CD4/CD8 were low in all patients. The predominant CT features in 19 patients were multiple (42%) ground-glass opacities (58%) and consolidations (32%). Erythrocyte sedimentation rate (ESR) in the medicine group was significantly lower than that in the non-medicine group [median (interquartile range, IQR):14.0 (10.0-34.0) vs. 51.0 (35.8-62.0), P = 0.005]. Nineteen patients (95%) were discharged with a median hospital stay of 30 days (IQR, 26-30). CONCLUSIONS: Most patients with SARS-CoV-2 and HIV co-infection exhibited mild to moderate symptoms. The milder extent of inflammatory response to SARS-CoV-2 infection might be associated with a previous history of ART in HIV-infected patients.