Development of COVID-19 Health-Risk Assessment and Self-Evaluation (CHaSe): a health screening system for university students and staff during the movement control order (MCO)

COVID-19 has triggered a global health crisis. Death from severe respiratory failure and symptoms, including fever, dry cough, sore throat, anosmia, and gastrointestinal disturbances, has been attributed to the disease. Development of screening and diagnosis methods prove to be challenging due to shared clinical features between COVID-19 and other pathologies, such as Middle Eastern respiratory syndrome, severe acute respiratory syndrome, and common colds. This study aims to develop a comprehensive one-stop online public health screening system based on clinical and epidemiological criteria. The immediate target populations are the university students and staff of University Sultan Zainal Abidin and the civil servants of the Malaysian Ministry of Science, Technology, and Innovation. Forty-nine (49) clinical and epidemiological factors associated with COVID-19 were identified and prioritized based on their prevalence via rigorous review of the literature and vetting sessions. A pilot study of 200 volunteers was conducted to assess the extent of risk mitigation of COVID-19 infection among the university students and civil servants using the prototyped model. Consequently, twelve (12) clinical parameters were identified and validated by the medical experts as essential variables for COVID-19 risk-screening. The updated model was then revalidated via real mass-screening of 5000 resulting in the final adopted CHaSe system. Principal component analysis (PCA) was used to confirm the weightage of risk level toward COVID-19 to procures the optimal accuracy, reliability, and efficiency of this system. Twelve (12) factor loadings accountable for 58.287% of the clinical symptoms and clinical history variables with forty-nine (49) parameters of COVID-19 were identified through PCA. The variables of the clinical and epidemiological aspects identified are the C6 (History of joining high-risk gathering (where confirmed cases had been recorded), CH11 [History of contact with confirmed cases (close contact)], CH13 [Duration of exposure with confirmed cases (minutes)] with substantial positive factors of 0.7053, 0.706 and 0.5086, respectively. The contribution toward high-risk infection of COVID-19 was firmly attributable to the variables CH14 [Last contact with confirmed cases (days)], CH13 [Duration of exposure with confirmed cases (minutes)], and S1 (Age). The revalidated PCA for 5000 respondents also yielded twelve significant PCs with a cumulative variance of 58.288%. Importantly, the medical experts have revalidated the CHaSe system for accuracy of all clinical aspects (clinical symptoms and clinical history) and epidemiological links to COVID-19 infection. After revalidating the model for 5000 respondents, the PC variance for PC1, PC2, PC3, and PC4 was 27.36%, 11.79%, 10.347%, and 8.785%, respectively, with the cumulative explanation of 58.288% in data variability. The level of risks detected using the CHaSe system toward COVID-19 provides optimal accuracy, reliability, and efficiency to conduct mass-screening of students and government servants for COVID-19 infection.

Estimation of rainwater harvesting by the reflectance of the purity index of rainfall

Rainwater harvesting is an effective alternative practice, particularly within urban regions, during periods of water scarcity and dry weather. The collected water is mostly utilized for non-potable household purposes and irrigation. However, due to the increase in atmospheric pollutants, the quality of rainwater has gradually decreased. This atmospheric pollution can damage the climate, natural resources, biodiversity, and human health. In this study, the characteristics and physicochemical properties of rainfall were assessed using a qualitative approach. The three-year (2017-2019) data on rainfall in Peninsular Malaysia were analysed via multivariate techniques. The physicochemical properties of the rainfall yielded six significant factors, which encompassed 61.39% of the total variance as a result of industrialization, agriculture, transportation, and marine factors. The purity of rainfall index (PRI) was developed based on subjective factor scores of the six factors within three categories: good, moderate, and bad. Of the 23 variables measured, 17 were found to be the most significant, based on the classification matrix of 98.04%. Overall, three different groups of similarities that reflected the physicochemical characteristics were discovered among the rain gauge stations: cluster 1 (good PRI), cluster 2 (moderate PRI), and cluster 3 (bad PRI). These findings indicate that rainwater in Peninsular Malaysia was suitable for non-potable purposes.