Work from home practices and employment relations during COVID-19 movement control order in Malaysia

The presence of COVID-19 movement control order (MCO) and working instructions from home contribute to a flexible working arrangement. Work from home (WFH) is likely part of the ‘new normal' as the global community continues to fight COVID-19. The question arises as to how the employment relations takes place while working from home due to MCO and whether technological facilities and smart devices have successfully created harmonious and effective employment relationships. This paper aims to explore how WFH during MCO can affect employment relations in Malaysia. Based on qualitative online survey data with 725 respondents and analyzed using the reflexive thematic analysis technique, this study found that four major elements influence employment relations, namely: Digital Transformation, Digital Communication, Support (institutional & social), and New Working Style. These elements can either strengthen and at the same time weaken existing employment relationships. The study also showed that the success of WFH on employment relations is a concept of relationships that exist based on trust and this is shown through a symbol of cooperation that is trusted on the commitment of work given. Therefore, it is the responsibility of each member of the organization to put in their utmost and maintain smooth and harmonious employment relations. Employees and employers with the support of the Government should join hands to overcome the challenges of COVID-19 and ensure that the new work style of WFH efficiently functions for the betterment of everyone, society, and the nation at large. © 2023 Conscientia Beam. All Rights Reserved.

Lesion Sterilization and Tissue Repair of Laterally Luxated Primary Mandibular Incisor: A Case Report

Lateral luxation injuries are common during childhood and in young adolescence. These injuries involve the surrounding tissues that could lead to extensive clinical treatment problems with a risk of pulpal complications such as pulp necrosis. A case of a healthy 4-year-old Malay boy visiting the paediatric dental clinic after seven months of laterally luxated injury on the lower right lateral incisor (tooth 82) was reported. The traumatic tooth was splinted by a private practitioner three days after the trauma, however, the splint dislodged less than 24-hour after placement. In view of the mother's fear of bringing her child to the dentist due to the COVID-19, no follow-up and further treatment were carried out. This has resulted in the development of discolouration and pulpal necrosis to the injured tooth. Lesion sterilization and tissue repair technique (LSTR) was performed. This report highlights the rare occurrence of lateral luxation injury on tooth 82 and the management of pulp necrosis as a complication via lesion sterilization and tissue repair technique due to the uncooperative behaviour of the child.Copyright © 2022, Faculty of Medicine, University of Malaya. All rights reserved.

Hedging strategies among financial markets: the case of green and brown assets.

Recognizing the growing importance of the green energy market-renewable energy stocks and bonds-and its classification as a viable financial asset, this paper examines hedging strategies with brown market instruments-gold, oil, bond and the composite S&P500-on the green energy markets. That is, we examine whether, and to what extent brown assets can provide a hedge for green assets, using variants of the multivariate GARCH framework (DCC, ADCC and GO-GARCH). Our dataset spans the period 01/12/2008 to 30/09/2021. To account for the influence of the COVID-19 pandemic, we split the dataset into two-pre-covid (1/12/2008-10/03/2020) and covid-era (11/03/202-30/09/2021). Two key findings emanate from our results: first, conventional bonds and stocks provide the most consistent hedge for investment in the green markets. Second, the results are sensitive to the state of the market-hedging effectiveness declined during the covid period in the green stock market. Among other things, it is recommended that investors include instruments of the green market in portfolio allocation.

Do oil shocks affect the green bond market?

This study examines the predictive power of oil shocks for the green bond markets. In line with this aim, we investigated the extent to which oil shocks could be used to accurately make in- and out-of-sample forecasts for green bond returns. Three striking findings emanated from our results: First, the three types of oil shock are reliable predictors for green bond indices. Second, the performances of the predictive models were consistent across the different forecasting horizons (i.e. H = 1 to H = 24). Third, our findings were sensitive to classifying the dataset into pre-COVID and COVID eras. For instance, the results confirmed that the predictive power of oil shocks declined during the crisis period. We also discuss some policy implications of this study's findings.

The behaviour of U.S. stocks to financial and health risks

This article examines the hedging effectiveness of U.S. stocks against uncertainties due to equity market (financial risk) and pandemics (health risk), including Covid‐19 pandemic. Consequently, we consider two categories of U.S. stocks—defensive and non‐defensive stocks drawn from 10 different sectors and distinctly analysed over two data samples—pre‐ and post‐Covid periods. We construct a predictive panel data model that simultaneously accounts for both heterogeneity and common correlated effects and also complementarily determine the predictive power of accounting for uncertainties in the valuation of U.S. stocks. We find that hedging effectiveness is driven by the types of stocks and measures of uncertainty. Defensive stocks provide a good hedge for pandemic‐induced uncertainty, and the hedging effectiveness is higher during calm market conditions as compared to turbulent conditions, while both categories lack hedging capability in the face of equity‐induced uncertainty. Finally, we find that the inclusion of uncertainty in the predictive model of U.S. stock returns improves its forecasts and this conclusion is robust to alternative measures of uncertainty and multiple forecast horizons.

Prediction of development of disease among contacts of COVID-19 patients

Objectives Coronavirus disease 2019 (COVID-19) is a very progressive disease that concerns everyone regarding its infection and spread. This study predicted infection among people who came in contact with COVID-19-infected patients. The authors observed many categories of patients to get the best results. The aim of this work was to study the contact status of patients with COVID-19 in Sharkia Governorate, Egypt, and to assess the criteria of contacts being infected. Patients and methods This is a retrospective cohort study that used the data of Zagazig University Hospitals in Sharkia Governorate, reviewing people who came in contact with COVID-19 cases for appearance of symptoms, so that the authors can predict (later on) the probability of contacts getting infected. Results Of the 101 contacts, 49 were positive according to symptoms, 27 were positive according to computed topography (CT), and 38 were positive according to laboratory investigations, so contacts who were getting infected were according to the definition of suspect and probable cases. Overall, the suspect and probable contacts were 61, and about 27 (of 68 contacts who did CT from our sample) were moderate and severe cases. Conclusion Contact cases with comorbidity and old age were more liable to get infected and to be more serious cases than young age with no comorbidity. Of all contacts (101), 42 had comorbidities and about 31 of them had the disease.

Face Mask Detection using Deep Learning and Transfer Learning Models

The fast spread of Coronavirus (COVID-19) has created a global health crisis. The World Health organization (WHO) released several recommendations to help in preventing the spread of coronavirus. Wearing a mask in crowded venues is the most appropriate protective practice against COVID-19, according to the WHO. Keeping a close eye on people in public places is next to impossible, so identifying face masks becomes critical in the fight against COVID-19. Medical image analysis and classification are two areas where deep learning has lately become one of the widely used ways for enhancing performance. It can be utilized quite efficaciously to identify individuals who are not wearing a mask. The utilization of transfer learning models as a deep learning technique is also on the rise, and they work quite well. In our study, we used a total number of 7235 data images from an online dataset for face mask detection, which is done with two deep learning models and one transfer learning model. The deep learning-based CNN model using MaxPooling operation and AveragePooling operation achieved the accuracy of 95.78% and 95.36% respectively. Contradictory to that, the transfer learning-based MobileNetV2 model achieved an accuracy of 99.10%. © 2022 IEEE.

COVID-19 Pandemic and the Safe Haven Property of Bitcoin

The commentaries in the literature point out that cryptocurrencies, particularly Bitcoin, provide safe haven features to investors. The advent of COVID-19 pandemic offers a perfect opportunity to test this hypothesis. This study tries to validate this claim by examining the safe haven prowess of Bitcoin against measures of uncertainty (VIX, EPU, and Oil Shock). We further make a comparison between pre-and post-COVID-19 analyses. Results confirm that prior to COVID-19, Bitcoin was able to maintain its widely acknowledged characteristics. However, the post COVID-19 announcement upturned the tides previously identified.

Use of Ivermectin as a Potential Chemoprophylaxis for COVID-19 in Egypt: A Randomized Clinical Trial

Introduction: The rate of secondary attacks of SARS-COV-2 is high among household close contacts. Social distancing, isolation and infection control measures are important for preventing exposure to infection, but insufficient. Aim: The study aimed to evaluate possible role of oral ivermectin as a chemoprophylaxis in asymptomatic family close contacts with COVID-19 patients. Materials and Methods: A prospective interventional randomised open label-controlled study was conducted (registered at clinicaltrials. gov;NCT04422561) during June and July 2020. Two arms were designed according to use of ivermectin. In ivermectin arm, contacts received ivermectin according to Body Weight (BW) on day of the diagnosis of their index case. The nonintervention group received no treatment. Both groups were followed-up for two weeks for development of symptoms suggestive of COVID-19. Results: Ivermectin group included 203 contacts (to 52 index cases) aged 39.75 +/- 14.94 years;52.2% were males. Nonintervention group included 101 contacts (to a total of 24 index cases) aged 37.69 +/- 16.96 years, 49.5% were males. Fifteen contacts (7.4%) developed COVID-19 in the ivermectin arm compared to 59 (58.4%) in the nonintervention arm (P <0.001). The protection rate for ivermectin was more prominent in contacts aged less than 60-year-old (6.2% infected compared to 58.7% if no treatment). Ivermectin in the protection against SARS-CoV-2 infection had an OR of 12.533 and 11.445 (compared to nontreatment) in both univariate and multivariate models, respectively. Side effects of ivermectin were reported in 5.4%;they were mild. Conclusion: Ivermectin is suggested to be a promising, effective and safe chemoprophylactic drug in management of COVID-19.

Assessing the safe haven property of the gold market during COVID-19 pandemic.

This study examines the safe haven prowess of gold against some exogenous shocks due to the COVID-19 pandemic. We further make a comparison of our findings with those obtained for the period before it. Our results confirm the potential of gold market to serve as a safe haven during the pandemic albeit with a higher effectiveness before the pandemic. Further results suggest that gold consistently offers better safe haven properties than the US stocks as well as other precious metals like Silver, Palladium and Platinum regardless of the period. Finally, we find that the predictive model that accounts for uncertainties outperforms the benchmark model that ignores the same both for the in- and out-of-sample forecast analyses.