A Chaotic or Orderly Digitalization?: Malaysia's Resilient Model for Sustainable Rural Tourism

Concerns about demographic changes, economic development, and community regeneration are universal across the developed world's rural regions. The tremendous trends of industrialization and urbanization have been slowly changing the economic and political standing of rural societies for over a century. Rural tourism, on the contrary, presents a picture of robust expansion. However, the crisis situation created by the onset of COVID-19 exacerbates the difficulty of sustaining rural tourism. The tourism industry is the most severely impacted by this crisis. Due to that, this paper indicates the importance of rural tourism activities in rural areas' economic growth in Malaysia both prior to and after the COVID-19 pandemic and provides an analysis in the field of rural tourism destinations. As a result, this research proposes an integrated empirical model for rural tourist sustainability (RTS), with community resource tourism (CRT) as a causal factor and technological development processes (TDP) as a mediator. The suggested integrated model serves to confirm this view, as the primary drivers of economic performance are a complex interaction of internal and external variables affecting rural tourism via digitization and the impact of technology use. Based on the current trends in demand for rural tourism, Malaysia is undoubtedly facing intense global competition and is poised to be the destination of choice for visitors. By examining the relationships between the various variables and utilizing the resources-based view and diffusion of innovation theories, this suggested integrated model would be able to affirm Malaysia's attractiveness as a rural tourist destination in the area. Additional research is required to better understand rural tourist market groups and other critical aspects that will assure the viability of agricultural-based rural economies and community development. © 2023 selection and editorial matter, Azizul Hassan and Nor Aida Abdul Rahman;individual chapters, the contributors.

A position statement and practical guide to the use of particulate filtering facepiece respirators (N95, FFP2, or equivalent) for South African health workers exposed to respiratory pathogens including Mycobacterium tuberculosis and SARS-CoV-2.

SUMMARY: Severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) is transmitted mainly by aerosol in particles <10 µm that can remain suspended for hours before being inhaled. Because particulate filtering facepiece respirators ('respirators'; e.g. N95 masks) are more effective than surgical masks against bio-aerosols, many international organisations now recommend that health workers (HWs) wear a respirator when caring for individuals who may have COVID-19. In South Africa (SA), however, surgical masks are still recommended for the routine care of individuals with possible or confirmed COVID-19, with respirators reserved for so-called aerosol-generating procedures. In contrast, SA guidelines do recommend respirators for routine care of individuals with possible or confirmed tuberculosis (TB), which is also transmitted via aerosol. In health facilities in SA, distinguishing between TB and COVID-19 is challenging without examination and investigation, both of which may expose HWs to potentially infectious individuals. Symptom-based triage has limited utility in defining risk. Indeed, significant proportions of individuals with COVID-19 and/or pulmonary TB may not have symptoms and/or test negative. The prevalence of undiagnosed respiratory disease is therefore likely significant in many general clinical areas (e.g. waiting areas). Moreover, a proportion of HWs are HIV-positive and are at increased risk of severe COVID-19 and death. RECOMMENDATIONS: Sustained improvements in infection prevention and control (IPC) require reorganisation of systems to prioritise HW and patient safety. While this will take time, it is unacceptable to leave HWs exposed until such changes are made. We propose that the SA health system adopts a target of 'zero harm', aiming to eliminate transmission of respiratory pathogens to all individuals in every healthcare setting. Accordingly, we recommend: the use of respirators by all staff (clinical and non-clinical) during activities that involve contact or sharing air in indoor spaces with individuals who: (i) have not yet been clinically evaluated; or (ii) are thought or known to have TB and/or COVID-19 or other potentially harmful respiratory infections;the use of respirators that meet national and international manufacturing standards;evaluation of all respirators, at the least, by qualitative fit testing; andthe use of respirators as part of a 'package of care' in line with international IPC recommendations. We recognise that this will be challenging, not least due to global and national shortages of personal protective equipment (PPE). SA national policy around respiratory protective equipment enables a robust framework for manufacture and quality control and has been supported by local manufacturers and the Department of Trade, Industry and Competition. Respirator manufacturers should explore adaptations to improve comfort and reduce barriers to communication. Structural changes are needed urgently to improve the safety of health facilities: persistent advocacy and research around potential systems change remain essential.

An effective diagnostic system for identifying covid-19 and pneumonia diseases using machine learning algorithms

COVID-19, a pandemic disease causes a devastating effect on humans. The heavy loss of human lives triggers us to build an effective automated diagnostic system for identifying the disease with good accuracy. People get affected by this disease are increasing day by day to a greater extend. The availability of test kits is limited in numbers and its performance is not satisfactory. Hence the medical practitioners highly rely on radiological imaging. There is an immediate requirement for processing these enormous images that are generated daily to test the patient is infected with the disease or not. COVID-19 has similar characteristics with its related diseases such as viral pneumonia and bacterial pneumonia. Appropriately diagnosing the classes of diseases based on its severity is highly important in the medical domain. Our proposed system uses machine learning algorithms such Linear Support Vector Machine classifier and Logistic Regression classifier and provides remarkable results. The proposed system has experimented with 250 chest X-Rays from each classes of diseases such as COVID-19, viral pneumonia, bacterial pneumonia, and normal healthy subjects. The results are evaluated with performance measures such as Accuracy, Sensitivity, Specificity, F1-score, Positive Predictive Value (PPV), Negative Predictive Value (NPV), False Positive Rate (FPR), False Negative Rate (FNR), and Error rate. Logistic Regression gives accuracy 93.4%, Sensitivity 94.1%, Specificity 92.9%, False Positive Rate 7.1%, False Negative Rate 5.9%., Positive Predictive Value 91.4%, Negative Predictive Value 95.1% and Error rate 6.6% which is comparatively better than Linear Support Vector Machine classifier that shows Accuracy 91.6%, Sensitivity 93.1%, Specificity 90.5%, False Positive Rate 9.5%, False Negative Rate 6.9%, Positive Predictive Value 87.1% and Negative Predictive Value 95% and Error Rate 8.5%. © 2021, Universitatea de Vest Vasile Goldis din Arad. All rights reserved.

KN95 filtering facepiece respirators distributed in South Africa fail safety testing protocols.

BACKGROUND: Given the global shortage of N95 filtering facepiece respirators (FFP2 in Europe) during the COVID-19 pandemic, KN95 masks (Chinese equivalent of the N95 and FFP2) were imported and distributed in South Africa (SA). However, there are hardly any published independent safety data on KN95 masks. OBJECTIVES: To evaluate the seal, fit and filtration efficiency of several brands of KN95 masks marketed for widespread use in SA healthcare facilities, using standardised testing protocols. METHODS: The verifiability of manufacturer and technical details was first ascertained, followed by evaluation of the number of layers comprising the mask material. The testing protocol involved a directly observed positive and negative pressure user seal check, which if passed was followed by qualitative fit testing (sodium saccharin) in healthy laboratory or healthcare workers. Quantitative fit testing (3M) was used to validate the qualitative fit testing method. The filtration efficacy and integrity of the mask filter material were evaluated using a particle counter-based testing rig utilising aerosolised saline (expressed as filtration efficacy of 0.3 µm particles). Halyard FLUIDSHIELD 3 N95 and 3M 1860 N95 masks were used as controls. RESULTS: Twelve KN95 mask brands (total of 36 masks) were evaluated in 7 participants. The mask type and manufacturing details were printed on only 2/12 brands (17%) as per National Institute of Occupational Safety and Health and European Union regulatory requirements. There was considerable variability in the number of KN95 mask layers (between 3 and 6 layers in the 12 brands evaluated). The seal check pass rate was significantly lower in KN95 compared with N95 masks (1/36 (3%) v. 12/12 (100%); p<0.0001). Modification of the KN95 ear-loop tension using head straps or staples, or improving the facial seal using Micropore 3M tape, enhanced seal test performance in 15/36 KN95 masks evaluated (42%). However, none of these 15 passed downstream qualitative fit testing compared with the control N95 masks (0/15 v. 12/12; p<0.0001). Only 4/8 (50%) of the KN95 brands tested passed the minimum filtration requirements for an N95 mask (suboptimal KN95 filtration efficacy varied from 12% to 78%, compared with 56% for a surgical mask and >99% for the N95 masks at the 0.3 µm particle size). CONCLUSIONS: The KN95 masks tested failed the stipulated safety thresholds associated with protection of healthcare workers against airborne pathogens such as SARS-CoV-2. These preliminary data have implications for the regulation of masks and their distribution to healthcare workers and facilities in SA.