COVID-19 epidemic in Malaysia started as a small wave of 22 cases in January 2020 through imported cases. It was followed by a bigger wave mainly from local transmissions resulting in 651 cases. The following wave saw unexpectedly three digit number of daily cases following a mass gathering urged the government to choose a more stringent measure. A limited lock-down approach called Movement Control Order (MCO) was immediately initiated to the whole country as a way to suppress the epidemic trajectory. The lock-down causes a major socio-economic disruption thus the ability to forecast the infection dynamic is urgently required to assist the government on timely decisions. Limited testing capacity and limited epidemiological data complicate the understanding of the future infection dynamic of the COVID-19 epidemic.

This paper attempts to ascertain the impacts of population density on the spread and severity of COVID-19 in Malaysia. Besides describing the spatio-temporal contagion risk of the virus, ultimately, it seeks to test the hypothesis that higher population density results in exacerbated COVID-19 virulence in the community. The population density of 143 districts in Malaysia, as per data from Malaysia’s 2010 population census, was plotted against cumulative COVID-19 cases and infection rates of COVID-19 cases, which were obtained from Malaysia’s Ministry of Health official website. The data of these three variables were collected between 19 January 2020 and 31 December 2020. Based on the observations, districts that have high population densities and are highly inter-connected with neighbouring districts, whether geographically, socio-economically, or infrastructurally, tend to experience spikes in COVID-19 cases within weeks of each other. Using a parametric approach of the Pearson correlation, population density was found to have a moderately strong relationship to cumulative COVID-19 cases (p-value of 0.000 and R2 of 0.415) and a weak relationship to COVID-19 infection rates (p-value of 0.005 and R2 of 0.047). Consequently, we provide several non-pharmaceutical lessons, including urban planning strategies, as passive containment measures that may better support disease interventions against future contagious diseases.

Incorporation of pathway knowledge into microarray analysis has brought better biological interpretation of the analysis outcome. However, most pathway data are manually curated without specific biological context. Non-informative genes could be included when the pathway data is used for analysis of context specific data like cancer microarray data. Therefore, efficient identification of informative genes is inevitable. Embedded methods like penalized classifiers have been used for microarray analysis due to their embedded gene selection. This paper proposes an improved penalized support vector machine with absolute t-test weighting scheme to identify informative genes and pathways. Experiments are done on four microarray data sets. The results are compared with previous methods using 10-fold cross validation in terms of accuracy, sensitivity, specificity and F-score. Our method shows consistent improvement over the previous methods and biological validation has been done to elucidate the relation of the selected genes and pathway with the phenotype under study.

Microarray technology provides a way for researchers to measure the expression level of thousands of genes simultaneously in a single experiment. Due to the increasing amount of microarray data, the field of microarray data analysis has become a major topic among researchers. One of the examples of microarray data analysis is classification. Classification is the process of determining the classes for samples. The goal of classification is to identify the differentially expressed genes so that these genes can be used to predict the classes for new samples. In order to perform the tasks of classification of microarray data, classification software is required for effective classification and analysis of large-scale data. This paper reviews numerous classification software applications for gene expression data. In this paper, the reviewed software can be categorized into six supervised classification methods: Support Vector Machine, K-Nearest Neighbour, Neural Network, Linear Discriminant Analysis, Bayesian Classifier, and Random Forest. implementation of software that facilitates and eases the understanding of biological processes. InSrinivasan et al. [3],the most general definition of bioinformatics in addressing biological problems is discussed.Most biomedical researchers are looking for appropriate software which is not only can achieve high prediction accuracy but also includes a user friendly design in order to ease the implementation. Moreover, such softwareisvery useful if the source code is available.In addition, the software should be up-to-date with the related information to make sure that it is competitive with other software.In this paper, the classification software applications for six supervised classification methodsare reviewed. The six supervised classification methods include the Support Vector Machine (SVM), K-Nearest Neighbour (KNN), Neural Network (NN), Bayesian Classifier, Linear Discriminant Analysis (LDA), and Random Forest (RF). Furthermore, the sources of the software and web-based applications are listed as well. Software for Support Vector Machine (SVM) LIBSVMLIBSVM, a library for SVMs, was developed by Chang and Lin [4]. The main purpose of developing this software was to help users implementing SVM. This package supports three main learning tasks: classification, regression, and estimation of probability. For classification, it supports binary and multi-class classification. It also includes various formulations of SVM such as c-classification, v-classification, ∈-regression, and vregression. Other features include support for cross-validation for performance measurement, model selection, and solving of unbalanced data using weighted SVM. It is mainly implemented in C++ and Java but there are many extensions such as R, MATLAB, Python, and Perl that have been developed by Chang and Lin and others. Moreover, it also provides different kernel settings such as linear, polynomial, and radial basis functions. This package is mainly for Windows and Linux. SVMlightSVMlight was developed...

In Malaysia, terraced housing hardly provides thermal comfort to the occupants. More often than not, mechanical cooling, which is an energy consuming component, contributes to outdoor heat dissipation that leads to an urban heat island effect. Alternatively, encouraging natural ventilation can eliminate heat from the indoor environment. Unfortunately, with static outdoor air conditioning and lack of windows in terraced houses, the conventional ventilation technique does not work well, even for houses with an air well. Hence, this research investigated ways to maximize natural ventilation in terraced housing by exploring the air well configurations. By adopting an existing single storey terraced house with an air well, located in Kuching, Sarawak, the existing indoor environmental conditions and thermal performance were investigated and monitored using scientific equipment, namely HOBO U12 air temperature and air humidity, the HOBO U12 anemometer and the Delta Ohm HD32.3 Wet Bulb Globe Temperature meter. For this parametric study, the DesignBuilder software was utilized. The field study illustrated that there is a need to improve indoor thermal comfort. Thus, the study further proposes improvement strategies to the existing case study house. The proposition was to turn the existing air well into a solar chimney taking into account advantages of constant and available solar radiation for stack ventilation. The results suggest that the enhanced air well was able to improve the indoor room air velocity and reduce air temperature. The enhanced air well with 3.5 m height, 1.0 m air gap width, 2.0 m length was able to induce higher air velocity. During the highest air temperature hour, the indoor air velocity in existing test room increased from 0.02 m/s in the existing condition to 0.29 m/s in the hottest day with 2.06 °C air temperature reduction. The findings revealed that the proposed air well could enhance the thermal and ventilation performance under the Malaysia tropical climate.

Clustering techniques can group genes based on similarity in biological functions. However, the drawback of using clustering techniques is the inability to identify an optimal number of potential clusters beforehand. Several existing optimization techniques can address the issue. Besides, clustering validation can predict the possible number of potential clusters and hence increase the chances of identifying biologically informative genes. This paper reviews and provides examples of existing methods for clustering genes, optimization of the objective function, and clustering validation. Clustering techniques can be categorized into partitioning, hierarchical, grid-based, and density-based techniques. We also highlight the advantages and the disadvantages of each category. To optimize the objective function, here we introduce the swarm intelligence technique and compare the performances of other methods. Moreover, we discuss the differences of measurements between internal and external criteria to validate a cluster quality. We also investigate the performance of several clustering techniques by applying them on a leukemia dataset. The results show that grid-based clustering techniques provide better classification accuracy; however, partitioning clustering techniques are superior in identifying prognostic markers of leukemia. Therefore, this review suggests combining clustering techniques such as CLIQUE and k-means to yield high-quality gene clusters.

When gene expression data are too large to be processed, they are transformed into a reduced representation set of genes. Transforming large-scale gene expression data into a set of genes is called feature extraction. If the genes extracted are carefully chosen, this gene set can extract the relevant information from the large-scale gene expression data, allowing further analysis by using this reduced representation instead of the full size data. In this paper, we review numerous software applications that can be used for feature extraction. The software reviewed is mainly for Principal Component Analysis (PCA), Independent Component Analysis (ICA), Partial Least Squares (PLS), and Local Linear Embedding (LLE). A summary and sources of the software are provided in the last section for each feature extraction method.

Background: In this work, we presented a Susceptible-Infected-Removed (SIR) epidemiological model of COVID-19 epidemic in Malaysia post- and pre-Movement Control Order (MCO). The proposed SIR model was fitted to confirmed COVID-19 cases from the official press statements to closely reflect the observed epidemic trend in Malaysia. The proposed model is aimed to provide an accurate predictive information for decision makers in assessing the public health and social measures related to COVID-19 epidemic. Methods: The SIR model was fitted to the data by minimizing a weighted loss function; the sum of the residual sum of squares (RSS) of infected, removed and total cases. Optimized beta (β), gamma (γ) parameter values and the starting value of susceptible individuals (N) were obtained. Results: The SIR model post-MCO indicates the peak of infection on 10 April 2020, less than 100 active cases by 8 July 2020, less than 10 active cases by 29 August 2020, and close to zero daily new case by 22 July 2020, with a total of 6562 infected cases. In the absence of MCO, the model predicts the peak of infection on 1 May 2020, less than 100 active cases by 14 February 2021, less than 10 active cases by 26 April 2021 and close to zero daily new case by 6 October 2020, with a total of 1.6 million infected cases. Conclusion: The results suggest that the present MCO has significantly reduced the number of susceptible population and the total number of infected cases. The method to fit the SIR model used in this study was found to be accurate in reflecting the observed data. The method can be used to predict the epidemic trend of COVID-19 in other countries.