Real-Time Mask Identification for COVID-19: An Edge-Computing-Based Deep Learning Framework.

During the outbreak of the Coronavirus disease 2019 (COVID-19), while bringing various serious threats to the world, it reminds us that we need to take precautions to control the transmission of the virus. The rise of the Internet of Medical Things (IoMT) has made related data collection and processing, including healthcare monitoring systems, more convenient on the one hand, and requirements of public health prevention are also changing and more challengeable on the other hand. One of the most effective nonpharmaceutical medical intervention measures is mask wearing. Therefore, there is an urgent need for an automatic real-time mask detection method to help prevent the public epidemic. In this article, we put forward an edge computing-based mask (ECMask) identification framework to help public health precautions, which can ensure real-time performance on the low-power camera devices of buses. Our ECMask consists of three main stages: 1) video restoration; 2) face detection; and 3) mask identification. The related models are trained and evaluated on our bus drive monitoring data set and public data set. We construct extensive experiments to validate the good performance based on real video data, in consideration of detection accuracy and execution time efficiency of the whole video analysis, which have valuable application in COVID-19 prevention.

Using social media to mine and analyze public opinion related to COVID-19 in China

The outbreak of Corona Virus Disease 2019 (COVID-19) is a grave global public health emergency. Nowadays, social media has become the main channel through which the public can obtain information and express their opinions and feelings. This study explored public opinion in the early stages of COVID-19 in China by analyzing Sina-Weibo (a Twitter-like microblogging system in China) texts in terms of space, time, and content. Temporal changes within one-hour intervals and the spatial distribution of COVID-19-related Weibo texts were analyzed. Based on the latent Dirichlet allocation model and the random forest algorithm, a topic extraction and classification model was developed to hierarchically identify seven COVID-19-relevant topics and 13 sub-topics from Weibo texts. The results indicate that the number of Weibo texts varied over time for different topics and sub-topics corresponding with the different developmental stages of the event. The spatial distribution of COVID-19-relevant Weibo was mainly concentrated in Wuhan, Beijing-Tianjin-Hebei, the Yangtze River Delta, the Pearl River Delta, and the Chengdu-Chongqing urban agglomeration. There is a synchronization between frequent daily discussions on Weibo and the trend of the COVID-19 outbreak in the real world. Public response is very sensitive to the epidemic and significant social events, especially in urban agglomerations with convenient transportation and a large population. The timely dissemination and updating of epidemic-related information and the popularization of such information by the government can contribute to stabilizing public sentiments. However, the surge of public demand and the hysteresis of social support demonstrated that the allocation of medical resources was under enormous pressure in the early stage of the epidemic. It is suggested that the government should strengthen the response in terms of public opinion and epidemic prevention and exert control in key epidemic areas, urban agglomerations, and transboundary areas at the province level. In controlling the crisis, accurate response countermeasures should be formulated following public help demands. The findings can help government and emergency agencies to better understand the public opinion and sentiments towards COVID-19, to accelerate emergency responses, and to support post-disaster management.

Association of insomnia disorder with sociodemographic factors and poor mental health in COVID-19 inpatients in China.

PURPOSE: To examine insomnia disorder and its association with sociodemographic factors and poor mental health in 2019 novel coronavirus (COVID-19) inpatients in Wuhan, China. DESIGN: and Methods: A total of 484 COVID-19 inpatients in Wuhan Tongji Hospital were selected and interviewed with standardized assessment tools. Insomnia disorder was measured by the Chinese version of the Insomnia Severity Index (ISI-7), a total score of 8 or more was accepted as the threshold for diagnosing insomnia disorder. RESULTS: The prevalence of insomnia disorder in the whole sample was 42.8%. Binary logistic regression analysis revealed that female gender, younger age, and higher fatigue and anxiety severity were more likely to experience insomnia disorder. CONCLUSION: Given the high rate of insomnia disorder status among COVID-19 inpatients in Wuhan, China, and its negative effects, follow-up assessments and appropriate psychological interventions for insomnia disorder are needed in this population.

Rapid review for the anti-coronavirus effect of remdesivir.

The outbreak of SARS-CoV-2 rapidly spread across China and worldwide. Remdesivir had been proposed as a promising option for treating coronavirus disease 2019 (COVID-19). We provided a rapid review to critically assess the potential anti-coronavirus effect of remdesivir on COVID-19 and other coronaviruses based on the most up-to-date evidence. Even though remdesivir was proposed as a promising option for treating COVID-19 based on laboratory experiments and reports from compassionate use, its safety and effect in humans requires high-quality evidence from well-designed and adequately-powered clinical trials for further clarification.