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Journal of Scientometric Research ; 11(1):47-54, 2022.
Article in English | Web of Science | ID: covidwho-1897066


This study aims to analyze the dynamics of the published articles and preprints of Covid-19 related literature from different scientific databases and sharing platforms. The PubMed, ScienceDirect, and ResearchGate (RG) databases were under consideration in this study over a specific time. Analyses were carried out on the number of publications as (a) function of time (day), (b) journals and (c) authors. Doubling time of the number of publications was analyzed for PubMed "all articles" and ScienceDirect published articles. Analyzed databases were (1A) PubMed (01/12/2019-12/06/2020) "all_articles" (16) PubMed Review articles) and (1C) PubMed Clinical Trials (2) ScienceDirect all publications (01/12/2019- 25/05/2020) (3) RG (Article, Pre Print, Technical Report) (15/04/2020 - 30/4/2020). Total publications in the observation period for PubMed, ScienceDirect, and RG were 23000, 5898 and 5393 respectively. The average number of publications/day for PubMed, ScienceDirect and RG were 70.0 +/- 128.6, 77.6 +/- 125.3 and 255.6 +/- 205.8 respectively. PubMed shows an avalanche in the number of publications around May 10, the number of publications jumped from 6.0 +/- 8.4/day to 282.5 +/- 110.3/ day. The average doubling time for PubMed, ScienceDirect, and RG was 10.3 +/- 4 days, 20.6 days, and 2.3 +/- 2.0 days respectively. The average number of publications per author for PubMed, ScienceDirect, and RG was 1.2 +/- 1.4, 1.3 +/- 0.9, and 1.1 +/- 0.4 respectively. Subgroup analysis, PubMed review articles mean review <0 vertical bar 17 +/- 17 vertical bar 77> days: and reducing at a rate of -0.21 days (count)/day. The number of publications related to the COVID-19 until now is huge and growing very fast with time. It is essential to rationalize and limit the publications.

IEEE Access ; 8: 188538-188551, 2020.
Article in English | MEDLINE | ID: covidwho-1528294


In the early months of the COVID-19 pandemic with no designated cure or vaccine, the only way to break the infection chain is self-isolation and maintaining the physical distancing. In this article, we present a potential application of the Internet of Things (IoT) in healthcare and physical distance monitoring for pandemic situations. The proposed framework consists of three parts: a lightweight and low-cost IoT node, a smartphone application (app), and fog-based Machine Learning (ML) tools for data analysis and diagnosis. The IoT node tracks health parameters, including body temperature, cough rate, respiratory rate, and blood oxygen saturation, then updates the smartphone app to display the user health conditions. The app notifies the user to maintain a physical distance of 2 m (or 6 ft), which is a key factor in controlling virus spread. In addition, a Fuzzy Mamdani system (running at the fog server) considers the environmental risk and user health conditions to predict the risk of spreading infection in real time. The environmental risk conveys from the virtual zone concept and provides updated information for different places. Two scenarios are considered for the communication between the IoT node and fog server, 4G/5G/WiFi, or LoRa, which can be selected based on environmental constraints. The required energy usage and bandwidth (BW) are compared for various event scenarios. The COVID-SAFE framework can assist in minimizing the coronavirus exposure risk.

Bangladesh Medical Research Council Bulletin ; 46(3):161-167, 2020.
Article in English | Scopus | ID: covidwho-1417070


Background: Coronavirus pandemic has become the leading cause of disability and death throughout the world. Nurses have a pivotal role in managing the COVID-19 patients across the globe including Bangladesh. Objective: The study was aimed to assess the level of nurses' knowledge, attitude and practice with regard to readiness for providing nursing care for COVID-19 patients at COVID-19 dedicated hospitals in Dhaka. Method: This is a cross-sectional descriptive study. Data were collected from July, to September, 2020 using a self-administered questionnaire. A total of 384 nurses were selected from four corona dedicated hospitals in Dhaka. Nurses' readiness was measured by using paper and pencil questionnaire. Personal and professional characteristics knowledge, attitudes and practice related to COVID-19 questionnaires were used. Data were analysed using descriptive statistics, independent t-test, one way ANOVA and Pearson's Product-Moment Correlation-Coefficient. Results: Findings showed that nurses had moderate level of knowledge (M = 34.34.15, SD = 2.98), attitude (M = 27.58, SD = 3.45) and practice (M = 13.12, SD = 1.78) respectively. A significant negative correlation found between knowledge and attitude (r = -.178, p = .000). However, no significant relationship was to be found between attitude and practice and knowledge and practice regarding nurses' readiness to provide nursing care to COVID-19 patients. Statistical analysis showed that nurses working at Kurmitola General Hospital (F = 9.47, p = .000) had better practice than those of other hospitals. Senior staff nurses did better practice (F = 21.765, p = .000) than those of other nurses. Conclusion: Nurses achieved moderate level of knowledge, attitude and practice. There was negative correlation between knowledge and attitude of nurses. In-service education programme can be developed and conducted to increase knowledge, attitude and practice regarding nurses readiness for caring of COVID-19 patients. © 2020 Bangladesh Medical Research Council. All rights reserved.