An Autonomous IoT-based Contact Tracing Platform in a COVID-19 Patient Ward

Since 2020, the coronavirus disease (COVID-19) pandemic has had a substantial impact on all community sectors worldwide, particularly the health care sector. Healthcare workers (HCWs) are at risk of COVID-19 infection due to occupational exposure to infectious patients, visitors, and staff. Contact tracing of close physical interaction is an essential control measure, especially in hospitals, to prevent onward transmission during an outbreak event. In this article, we propose an IoT-based contact tracing system for subject identification, interaction tracking and data transmission in hospital wards. The system, based on Bluetooth Low Energy (BLE) devices, tracks the duration of interactions between different HCWs, and the time each HCW spends within the patient rooms using additional information from proximity sensors in the hallway or on the door frame of the patient room. The collected data are transferred via Long Range (LoRa) wireless technology and further analyzed to inform infection prevention activities. The suggested system’s performance is evaluated in a COVID-19 patient ward with both standard and negative pressure isolation rooms, and the current system’s capabilities and future research prospects are briefly discussed. IEEE

Perceived Stress Analysis of Undergraduate Students during COVID-19: A Machine Learning Approach

Stress is a state of mind when an individual experiences emotional or physical tensions originating from any event that results in frustration, anger, or nervousness. Unfortunately, since the inception of the COVID-19 pandemic, it has been massively witnessed among university students due to persistent usage of e-learning gears for the last two years. Due to the severity of the observed stress, accurate and early prediction and detection should play a pivotal role in treating a student. In this work, a questionnaire-based dataset on Jordanian students has been analyzed using the 5-point Likert Scale. One of the most widely used psychological instrument Perceived Stress Scale (PSS) is used to identify the stressrelated symptoms of the students. Based on the dataset, several machine learning (ML) algorithms were applied for regression and classification analysis by which mental stress has been predicted and classified. After simulation in Python, the ML regressors were evaluated through the performance metrics such as R2 Score, Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), Mean Percentage Absolute Error (MAPE), and ML classifiers were assessed by accuracy, precision, recall, and F1-Score. It is observed that Linear Regression (LR) performed the best among all the regression models whereas the Logistic Regression Classifier (LRC) portrayed the highest accuracy of 97.8% among all the classifiers. Therefore, ML-based stress analysis can significantly contribute to analyzing students' mental stress during COVID-19 in an automated manner.

Engineering Students' Readiness for Online Learning Amidst the COVID-19 Pandemic: Scale Validation and Lessons Learned from a Developing Country

The recent outbreak of the COVID-19 pandemic forced education institutes to shift to an internet-based online delivery mode. This unique situation accelerates a long-standing issue of digital inequality among the students in education and warrants a concentrated study to investigate students' readiness for learning in online environment. This study developed an instrument to meticulously measure the students' readiness for online learning in a pandemic situation. The proposed model consists of (a) motivation, (b) self-efficacy, and (c) situational factors. The proposed model was validated with the engineering students (for pilot study N = 68 and main study N = 988) from several universities in Bangladesh. To validate the underlying relationships between the latent constructs, an exploratory factor analysis (EFA) was performed followed by structural equation modelling (SEM) for the construct validity of the measurement model and to assess the model fit. The findings showed that besides motivation and self-efficacy, the situational factors describing the contextual dynamics emerging from the COVID-19 significantly influenced the student's online readiness. We argue that digital inequality is an important factor influencing student readiness for online learning.

Performance Assessment of Machine Learning Classifiers in Detecting Psychological Impact of Postgraduate Students due to COVID-19

Psychological health of postgraduate students has received significant attention with the emergence of COVID-19. However, the detection of these psychological disorders appears to be a prolonged process as rigorous clinical tests and diagnoses are involved. In this context, machine learning algorithms are capable of analyzing the hidden pattern of the data about students' mental health and classifying them into different levels. This study investigates the performances of these algorithms for detecting postgraduate students' psychological impact during this pandemic. An online survey dataset is employed from Mendeley data repository consisting of the responses of Malaysian students based on the questions of General Anxiety Disorder (GAD-7) and concerns about research progress, academic delay, and daily life. Among the classifiers, artificial neural network showcased the highest accuracy of 95.45% whereas Logistic Regression, Linear Kernel Support Vector Machine, and Gaussian Process exhibited accuracies of more than 90%.

COVID-19 suicides in Pakistan, dying off not COVID-19 fear but poverty? - the forthcoming economic challenges for a developing country. (Special issue on immunopsychiatry of COVID-19 pandemic.)

Suicide increment during and afterwards a pandemic is highly common. The objective of the article was to report the prevalence and epidemiology of COVID-19 suicide cases in Pakistan for the first time. Most of the suicides occur due to: lockdown-related economic recession, fear of infection, and lockdown-related unemployment greatly aggravates the life-threatening situation.

Clinical Presentation, Management and In-Hospital Outcome of COVID-19 among Non-Healthcare Personnel and Healthcare Personnel in a Tertiary Cardiac Care Hospital

The huge numbers of non-healthcare personnel (non-HCP) who get infected by corona virus disease 2019 (COVID-19) not only paralyze health care systems but also put health care personnel (HCP) at potential risk globally. Objective of the study was to compare the Healthcare personnel (HCP) and non-HCP COVID-19 cases. This prospective observational study was carried out in National Heart Foundation Hospital and Research Institute of Bangladesh from March 08, 2020 to July 20, 2020. During this study period all admitted non-HCP who subsequently was diagnosed as COVID-19 positive by rRT-PCR and HCP of this hospital, who experienced fever or respiratory symptoms or came in close contact with COVID-19 patients at home or their workplace and become COVID-19 positive, were included. Out of 320 infected patients, 181(56.6%) patients were non-HCP and 139(43.4%) were HCP. Non-HCP were older than HCP (Mean age: 52.95+/-13.82 years vs. 34.08+/-11.11 years;p=0.001). Non-HCP were predominantly male and HCP were predominantly female (73.5% vs. 41% & 26.5% vs. 59%;p=0.001). Non-HCP had more risk factors and co-morbidities than HCP (p=0.001). Typical symptoms of COVID-19 such as fever and cough were prevalent in HCP. More aggressive treatment was required for non-HCP. Non-HCP had more severe disease and higher case fatality rate (9.4% vs. 0.7%;p=0.001) than HCP. Disease severity (OR 0.03, 95% CI 0.01-0.15) and DM (OR 0.09, 95% CI 0.01-0.94) were the independent predictor of mortality. Non-HCP was older in age, predominantly male and had more co-morbidities than HCP. Typical symptoms of COVID-19 were prevalent in HCP. Non-HCP had more severe disease and higher case fatality rate than HCP.

Protecting Health Care Workers from Infectious Diseases using Physical Proximity Networks (PPN)

The COVID-19 pandemic is a major global health threat, and Health Care Workers (HCWs) may have an increased risk of infection through occupational exposure. In the case of hospital outbreaks, contact tracing of close physical interaction needs to be performed. In this article, we propose an IoT-connected contact tracing system based on Bluetooth Low Energy (BLE) beacons for subject identification and data transmission. The proposed system consists of BLE receivers, BLE wearable tags, an edge gateway and a cloud server. The system records interaction information such as entering/exiting time of an HCW to isolation rooms in the hospital. The collected data will be further analyzed to inform infection prevention policies. The performance of the proposed system is assessed through qualitative and quantitative experimental results. Finally, the capabilities of the current system and future research directions are briefly discussed.

Protecting Health Care Workers from Infectious Diseases using Physical Proximity Networks (PPN)

The COVID-19 pandemic is a major global health threat, and Health Care Workers (HCWs) may have an increased risk of infection through occupational exposure. In the case of hospital outbreaks, contact tracing of close physical interaction needs to be performed. In this article, we propose an IoT-connected contact tracing system based on Bluetooth Low Energy (BLE) beacons for subject identification and data transmission. The proposed system consists of BLE receivers, BLE wearable tags, an edge gateway and a cloud server. The system records interaction information such as entering/exiting time of an HCW to isolation rooms in the hospital. The collected data will be further analyzed to inform infection prevention policies. The performance of the proposed system is assessed through qualitative and quantitative experimental results. Finally, the capabilities of the current system and future research directions are briefly discussed. © 2020 IEEE.