Face Mask Compliance and Complacency amongst Pakistan's Youth During the Third Wave of COVID-19

Objective: To assess the reasons for compliance with face mask usage amongst Pakistani youth. Study Design: Internet-based cross-sectional survey. Place and Duration of Study: Different cities of Pakistan, in April 2021. Methodology: Persons currently residing in Pakistan aged 18-29 were included in the study. The questionnaire included questions to identify self-perceptions of vulnerability to COVID-19, followed by questions about social situations where respondents wore facemasks. Results: A large number of (1,034,90%) respondents reported compliance with facemasks. Specifically, 1048(92%), 962(85%), and 850(75%) of respondents wore facemasks in healthcare facilities, marketplaces, or at work or school respectively. Nevertheless, when self-regulating, such as with family or around friends, compliance fell to 200(18%). Compliance turns to complacency when people are expected to self-regulate face mask usage. Females were more compliant than males (p<0.001). A large 779(68%) of compliant youth relied on information from Internet-based resources. Conclusion: The study correlates the reasons for compliance and complacency. In Pakistan, the most compliant youth get information from the Internet, particularly official websites and social media. © 2023, Army Medical College. All rights reserved.

The Use of an Incremental Learning Algorithm for Diagnosing COVID-19 from Chest X-ray Images

The new Coronavirus or simply Covid-19 causes an acute deadly disease. It has spread rapidly across the world, which has caused serious consequences for health professionals and researchers. This is due to many reasons including the lack of vaccine, shortage of testing kits and resources. Therefore, the main purpose of this study is to present an inexpensive alternative diagnostic tool for the detection of Covid-19 infection by using chest radiographs and Deep Convolutional Neural Network (DCNN) technique. In this paper, we have proposed a reliable and economical solution to detect COVID-19. This will be achieved by using X-rays of patients and an Incremental-DCNN (I-DCNN) based on ResNet-101 architec-ture. The datasets used in this study were collected from publicly available chest radiographs on medical repositories. The proposed I-DCNN method will help in diagnosing the positive Covid-19 patient by utilising three chest X-ray imagery groups, these will be: Covid-19, viral pneumonia, and healthy cases. Furthermore, the main contribution of this paper resides on the use of incremental learning in order to accommodate the detection system. This has high computational energy requirements, time consuming challenges, while working with large-scale and reg-ularly evolving images. The incremental learning process will allow the recognition system to learn new datasets, while keeping the convolutional layers learned pre-viously. The overall Covid-19 detection rate obtained using the proposed I-DCNN was of 98.70% which undeniably can contribute effectively to the detection of COVID-19 infection. © 2022, Brno University of Technology. All rights reserved.

A Priority Based Routing Protocol to Improve Quality of Service (QoS) in Wireless Body Area Network for COVID and Diabetic Patients

Use of electronic devices has increased many times in our daily life. It is used for many purposes including healthcare. Small sensor devices on patients’ body that reads patients’ physiological data and send those data to a remote server. Doctors and other healthcare professionals can view this data sitting at their home. Thus remote health monitoring is possible known as Wireless Body Area Networks (WBAN). Routing and providing seamless connectivity is a big challenge and a topic of research. In this work, a priority based routing protocol designed for WBAN has been developed where data has been classified into normal and emergency data. This routing protocol is especially applicable for COVID and diabetic patients. Normal data will be processed in cloud server but emergency data will be processed locally. Results obtained prove that our protocol is faster and also gives minimum delay. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

Knowledge, Attitude, and Practice (KAP) toward the Novel Coronavirus (COVID-19) Pandemic in a Saudi Population-Based Survey.

BACKGROUND: The Coronavirus Disease 2019 (COVID-19) outbreak has affected all regions and countries with varying impacts based on infection rates and the associated fatalities. This study aimed to assess knowledge, attitude, and practices (KAP) toward the COVID-19 pandemic among Saudi Arabians. METHODS: The study utilized a cross-sectional research design. Web-based questionnaires' link was sent via emails and social media and sample was 5483 respondents. Purposive sampling ensured only those participants that met the inclusion criteria. Validity and reliability were checked. RESULTS: Most respondents, 67.9%, were aged between 18 and 35 years and highest level of education university. The findings based on the study objectives indicated a high level of knowledge about COVID-19, which indicated early detection can improve treatment by 4701 (85.7%), the disease can be treated at home 84.6%, the disease can be prevented and avoided when precautions are taken 96.8%. Moreover, 37.2% of the respondents still used herbal products to prevent and treat the disease, and 72.1% indicating immediate visit the physician when there are symptoms. CONCLUSION: Promoting public knowledge about COVID-19 by the Ministry of Health is paramount in defeating this disease. Providing more education and awareness for public to comply with WHO's recommendation is recommended.

A Hybrid Model based on mBA-ANFIS for COVID-19 Confirmed Cases Prediction and Forecast

In India, the first confirmed case of novel corona virus (COVID-19) was discovered on January 30, 2020. The number of confirmed cases is increasing day by day, and it crossed 21,53,010 on August 9, 2020. In this paper, a hybrid forecasting model has been proposed to determine the number of confirmed cases for upcoming 10 days based on the earlier confirmed cases found in India. The proposed model is based on adaptive neuro-fuzzy inference system (ANFIS) and mutation-based Bees Algorithm (mBA). The meta-heuristic Bees Algorithm (BA) has been modified applying 4 types of mutation, and mutation-based Bees Algorithm (mBA) is applied to enhance the performance of ANFIS by optimizing its parameters. Proposed mBA-ANFIS model has been assessed using COVID-19 outbreak dataset for India and USA, and the number of confirmed cases in the next 10 days in India has been forecasted. Proposed mBA-ANFIS model has been compared to standard ANFIS model as well as other hybrid models such as GA-ANFIS, DE-ANFIS, HS-ANFIS, TLBO-ANFIS, FF-ANFIS, PSO-ANFIS and BA-ANFIS. All these models have been implemented using Matlab 2015 with 10 iterations each. Experimental results show that the proposed model has achieved better performance in terms of Root Mean Square Error (RMSE), Mean Absolute Percentage Error (MAPE), Mean absolute error (MAE) and Normalized Root Mean Square Error (NRMSE). It has obtained RMSE of 1280.24, MAE of 685.68, MAPE of 6.24 and NRMSE of 0.000673 for India Data. Similarly, for USA the values are 4468.72, 3082.07, 6.1, and 0.000952 for RMSE, MAE, MAPE, and NRMSE, respectively. © 2021, The Institution of Engineers (India).

STEMI and COVID-19 Pandemic in Saudi Arabia.

The COVID-19 pandemic had significant impact on health care worldwide which has led to a reduction in all elective admissions and management of patients through virtual care. The purpose of this study is to assess changes in STEMI volumes, door to reperfusion, and the time from the onset of symptoms until reperfusion therapy, and in-hospital events between the pre-COVID-19 (PC) and after COVID-19 (AC) period. All acute ST-segment elevation myocardial infarction (STEMI) cases were retrospectively identified from 16 centers in the Kingdom of Saudi Arabia during the COVID-19 period from January 01 to April 30, 2020. These cases were compared to a pre-COVID period from January 01 to April 30, 2018 and 2019. One thousand seven hundred and eighty-five patients with a mean age 56.3 (SD ± 12.4) years, 88.3% were male. During COVID-19 Pandemic the total STEMI volumes was reduced (28%, n = 500), STEMI volumes for those treated with reperfusion therapy was reduced too (27.6%, n= 450). Door to balloon time < 90 minutes was achieved in (73.1%, no = 307) during 2020. Timing from the onset of symptoms to the balloon of more than 12 hours was higher during 2020 comparing to pre-COVID 19 years (17.2% vs <3%, respectively). There were no differences between the AC and PC period with respect to in-hospital events and the length of hospital stay. There was a reduction in the STEMI volumes during 2020. Our data reflected the standard of care for STEMI patients continued during the COVID-19 pandemic while demonstrating patients delayed presenting to the hospital.