CNFRD: A Few-Shot Rumor Detection Framework via Capsule Network for COVID-19

In recent years, COVID-19 has become the hottest topic. Various issues, such as epidemic transmission routes and preventive measures, have "occupied” several online social media platforms. Many rumors about COVID-19 have also arisen, causing public anxiety and seriously affecting normal social order. Identifying a rumor at its very inception is crucial to reducing the potential harm of its evolution to society as a whole. However, epidemic rumors provide limited signal features in the early stage. In order to identify rumors with data sparsity, we propose a few-shot learning rumor detection model based on capsule networks (CNFRD), utilizing the metric learning framework and the capsule network to detect the rumors posted during unexpected epidemic events. Specifically, we constructively use the capsule network neural layer to summarize the historical rumor data and obtain the generalized class representation based on the historical rumor data samples. Besides, we calculate the distance between the epidemic rumor sample and the historical rumor class-wise representation according to the metric module. Finally, epidemic rumors are discriminated against according to the nearest neighbor principle. The experimental results prove that the proposed method can achieve higher accuracy with fewer epidemic rumor samples. This approach provided 88.92% accuracy on the Chinese rumor dataset and 87.07% accuracy on the English rumor dataset, which improved by 7% to 23% over existing approaches. Therefore, the CNFRD model can identify epidemic rumors in COVID-19 as early as possible and effectively improve the performance of rumor detection.

Update on the COVID-19 Vaccine Research Trends: A Bibliometric Analysis.

BACKGROUND: The coronavirus disease 2019 (COVID-19) pandemic is ravaging the world. To date, there are no standard therapies available to cure the disease. Consequently, research on COVID-19 vaccines is booming. This report aimed to assess the research trends of the global COVID-19 vaccines. METHODS: The relevant publications on the COVID-19 vaccines were searched in the Web of Science Core Collection Database (WOSCC) database from December 2019 to 11 August 2021. The VOSviewer1.6.16 was used to assess the co-authorship, co-occurrence, citation of countries, institutions, authors, journals, and hotspot keywords. The HistCiteTM (http://www.histcite.com/) software was used to calculate the total local citation score (TLCS) and total global citation score (TGCS) of each variable and generate the citation historiography graph of COVID-19 vaccine development using the citation time series analysis method. RESULTS: A total of 5070 studies authored by 21,151 researchers and published by 1364 different journals were eventually included in this study. The bulk of the retrieved studies were original articles (n = 2401, 47.36%). Among these studies, 1204 (23.75%) were published in 2020. A total of 3863 (76.19%) were published in 2021 and 4295 (84.71%) were open access. The highest number of studies was conducted in the USA, followed by England, China, and Germany. The main partners of the USA were China, England, and Canada. The University of Maryland (TLCS: 1618, TGCS: 2991) and Prof. Ugur Sahin from the University Medical Center of the Johannes Gutenberg University (TLCS: 1397, TGCS: 2407) were the most cited institution and author, respectively. The vaccines featured the highest number of papers, with 294 publications (TLCS: 0, TGCS: 1226). The most cited journal was the New England Journal of Medicine (TLCS: 3310, TGCS: 5914), with an impact factor (IF) of 91.245. The related topics included the following six aspects: attitudes towards vaccination, immunoinformatics analysis, clinical research, effectiveness and side effects, and the public management of vaccines. The timing diagram revealed that the research hotspots focused on the side effects of vaccines and public attitude towards vaccination. CONCLUSION: This novel comprehensive bibliometric analysis can help researchers and non-researchers to rapidly identify the potential partners, landmark studies, and research topics within their domains of interest. Through this study, we hope to provide more data to combat the COVID-19 pandemic.

Notes from the Field: Effectiveness of Prevention and Control Measures for Imported COVID-19 in Guangming District of Shenzhen.

Novel coronavirus disease 2019 (COVID-19) was present in most provinces of China after January 2020. We implemented a surveillance and screening strategy that included early detection of laboratory-confirmed COVID-19 cases and people who were exposed to the disease in Guangming District of Shenzhen. Separate targeted treatment and management strategies were applied to confirmed and suspected cases. From January 23 to March 13, 2020, we found 12 suspected cases, and 11 were confirmed as positive. Although eight of the 11 confirmed cases were family-aggregated, they were all imported cases with common exposure, which did not further cause local community transmission, and no medical staff were infected. After February 14, when the last case was confirmed, there were no newly confirmed cases for 28 consecutive days under the strict outbreak control. The targeted and whole-society involved prevention and control measures prevented the spread of the disease in a very short time and provided a strong guarantee for the orderly recovery of returning to work and social activities.

Avoiding health worker infection and containing the coronavirus disease 2019 pandemic: Perspectives from the frontline in Wuhan.

Coronavirus disease 2019 (COVID-19), which is caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), has caused great public concern worldwide due to its high rates of infectivity and pathogenicity. The Chinese government responded in a timely manner, alleviated the dilemma, achieved a huge victory and lockdown has now been lifted in Wuhan. However, the outbreak has occurred in more than 200 other countries. Globally, as of 9:56 am CEST on 19 May 2020, there have been 4,696,849 confirmed cases of COVID-19, including 315,131 deaths, reported to Word Health Organization (WHO). The spread of COVID-19 overwhelmed the healthcare systems of many countries and even crashed the fragile healthcare systems of some. Although the situation in each country is different, health workers play a critical role in the fight against COVID-19. In this review, we highlight the status of health worker infections in China and other countries, especially the causes of infection in China and the standardised protocol to protect health workers from the perspective of an anaesthesiologist, in the hope of providing references to reduce medical infections and contain the COVID-19 epidemic.

Mobile field hospitals, an effective way of dealing with COVID-19 in China: sharing our experience.

During the COVID-19 outbreak, China made great progress in controlling the epidemic, and the number of confirmed and suspected cases continues to decrease thanks to the various efforts employed. Mobile field hospitals have played a huge role in the centralized management of patients and they have effectively reduced transmission. This article describes some of our experiences operating mobile field hospitals in order to provide a reference and to better inform countries that are dealing with this crisis.