Risk Factors Of Loneliness And Anxiety Symptoms During Movement Control Order Among University Students: A Comparative Analysis

COVID-19 is a dreadful virus with extreme infectiousness as it spreads through both direct and indirect methods, as long as human movement exists. Malaysia introduced the first phase of Movement Control Order (MCO) on 18th of March, following the success of other earlier countries such as China, Scotland and Eng-land. A number of prohibitions were enforced to minimize human movements in the country. This includes the temporary closure of all educational institutions. The closure of institutions results in the division of university students into two groups: (1) students who were stranded in their respective universities and (2) students who managed to return to their respective hometowns. Meanwhile, prevalence of mental health disturbances among university students was found to be high during MCO due to loneliness and constant pressure from chaotic situa-tion. Previous evidences from other population indicate different prevalence of loneliness across social groups such as gender, economic status and locality. This paper provides a comparative analysis to discover the risk factors of loneliness among university students. Results show that most university students exhibit moderate to high loneliness, however there is no significant difference of loneli-ness across gender and nationality. No significant difference of loneliness is iden-tified between students who were living in residential college during MCO and those who were staying out-campus. Initiatives to address loneliness issues in residential college is further discussed.

Routine, Cost-Effective SARS-CoV-2 Surveillance Testing Using Pooled Saliva Limits Viral Spread on a Residential College Campus

This study highlights the utility of routine testing for SARS-CoV-2 using pooled saliva while maintaining high sensitivity of detection (under 2,500 copies/ml) and rapid turnaround of high volume (up to 930 samples in 8 h by two technicians and one quantitative PCR [qPCR] machine). This pooled approach allowed us to test all residential students 1 to 2 times per week on our college campus during the spring of 2021 and flagged 83% of our semester positives.

Network assessment and modeling the management of an epidemic on a college campus with testing, contact tracing, and masking

There remains a great challenge to minimize the spread of epidemics, especially in high-density communities such as colleges and universities. This is particularly true on densely populated, residential college campuses. To construct class and residential networks data from a four-year, residential liberal arts college with 5539 students were obtained from SUNY College at Geneseo, a rural, residential, undergraduate institution in western NY, USA. Equal-sized random networks also were created for each day. Different levels of compliance with mask use (none to 100%), mask efficacy (50% to 100%), and testing frequency (daily, or every 2, 3, 7, 14, 28, or 105 days) were assessed. Tests were assumed to be only 90% accurate and positive results were used to isolate individuals. The effectiveness of contact tracing, and the effect of quarantining neighbors of infectious individuals, was tested. The structure of the college course enrollment and residence networks greatly influenced the dynamics of the epidemics, as compared to the random networks. In particular, average path lengths were longer in the college networks compared to random networks. Students in larger majors generally had shorter average path lengths than students in smaller majors. Average transitivity (clustering) was lower on days when students most frequently were in class (MWF). Degree distributions were generally large and right skewed, ranging from 0 to 719. Simulations began by inoculating twenty students (10 exposed and 10 infectious) with SARS-CoV-2 on the first day of the fall semester and ended once the disease was cleared. Transmission probability was calculated based on an R0 = 2.4. Without interventions epidemics resulted in most students becoming infected and lasted into the second semester. On average students in the college networks experienced fewer infections, shorter duration, and lower epidemic peaks when compared to the dynamics on equal-sized random networks. The most important factors in reducing case numbers were the proportion masking and the frequency of testing, followed by contact tracing and mask efficacy. The paper discusses further high-order interactions and other implications of non-pharmaceutical interventions for disease transmission on a residential college campus.

IoT-based Smart Garbage Monitoring

Smart Garbage Monitoring System using Arduino and Blynk is an IoT-based project that is integrated with cloud computing service. It was developed specifically to help janitors and residential college representative committee in a public university in Malaysia in monitoring the level of garbage in the dustbin through Blynk application, which makes it easy to empty the dustbin and optimize the garbage collection time. This project uses ultrasonic sensor to measure the level of garbage in the dustbin and the data can be viewed through mobile application, Blynk. It will also notify users through email once the garbage bin capacity is full. Other than notification function, Smart Garbage Monitoring System also consists of automatic open and close lid, track dustbin location and humidity level. The objectives of this project are to design the IoT system that detects the level of the garbage displayed in Blynk and an LCD attached to the dustbin and to validate the user acceptance test for the system. The development of Smart Garbage Monitoring System used the System Development Life Cycle (SDLC) by implementing the agile model as the methodology. A User Acceptance Testing was conducted with 20 respondents including students from the student representatives’ body and janitors. This testing aimed to determine the acceptance of the system by conducting an evaluation. The results of the User Acceptance Test shows that most of the participants agreed and verified that the system works for them and a suitable solution for their problems. Therefore, based on the features and functionalities offered by the Smart Garbage Monitoring System, it could be concluded that this system is beneficial to all the users, especially the janitors and students of the residential college.