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  1. Fulltext Electroencephalogram-based decoding cognitive states using convolutional neural network and likelihood ratio based score fusion
    Zafar R, Dass SC, Malik AS
    PLoS One, 2017;12(5):e0178410.
    PMID: 28558002 DOI: 10.1371/journal.pone.0178410
    Electroencephalogram (EEG)-based decoding human brain activity is challenging, owing to the low spatial resolution of EEG. However, EEG is an important technique, especially for brain-computer interface applications. In this study, a novel algorithm is proposed to decode brain activity associated with different types of images. In this hybrid algorithm, convolutional neural network is modified for the extraction of features, a t-test is used for the selection of significant features and likelihood ratio-based score fusion is used for the prediction of brain activity. The proposed algorithm takes input data from multichannel EEG time-series, which is also known as multivariate pattern analysis. Comprehensive analysis was conducted using data from 30 participants. The results from the proposed method are compared with current recognized feature extraction and classification/prediction techniques. The wavelet transform-support vector machine method is the most popular currently used feature extraction and prediction method. This method showed an accuracy of 65.7%. However, the proposed method predicts the novel data with improved accuracy of 79.9%. In conclusion, the proposed algorithm outperformed the current feature extraction and prediction method.
  2. Fulltext Correlation analysis of air pollutant index levels and dengue cases across five different zones in Selangor, Malaysia
    Thiruchelvam L, Dass SC, Zaki R, Yahya A, Asirvadam VS
    Geospat Health, 2018 05 07;13(1):613.
    PMID: 29772882 DOI: 10.4081/gh.2018.613
    This study investigated the potential relationship between dengue cases and air quality - as measured by the Air Pollution Index (API) for five zones in the state of Selangor, Malaysia. Dengue case patterns can be learned using prediction models based on feedback (lagged terms). However, the question whether air quality affects dengue cases is still not thoroughly investigated based on such feedback models. This work developed dengue prediction models using the autoregressive integrated moving average (ARIMA) and ARIMA with an exogeneous variable (ARIMAX) time series methodologies with API as the exogeneous variable. The Box Jenkins approach based on maximum likelihood was used for analysis as it gives effective model estimates and prediction. Three stages of model comparison were carried out for each zone: first with ARIMA models without API, then ARIMAX models with API data from the API station for that zone and finally, ARIMAX models with API data from the zone and spatially neighbouring zones. Bayesian Information Criterion (BIC) gives goodness-of-fit versus parsimony comparisons between all elicited models. Our study found that ARIMA models, with the lowest BIC value, outperformed the rest in all five zones. The BIC values for the zone of Kuala Selangor were -800.66, -796.22, and -790.5229, respectively, for ARIMA only, ARIMAX with single API component and ARIMAX with API components from its zone and spatially neighbouring zones. Therefore, we concluded that API levels, either temporally for each zone or spatio- temporally based on neighbouring zones, do not have a significant effect on dengue cases.
  3. Fulltext Assessing the dynamics and impact of COVID-19 vaccination on disease spread: A data-driven approach
    Waseel F, Streftaris G, Rudrusamy B, Dass SC
    Infect Dis Model, 2024 Jun;9(2):527-556.
    PMID: 38525308 DOI: 10.1016/j.idm.2024.02.010
    The COVID-19 pandemic has significantly impacted global health, social, and economic situations since its emergence in December 2019. The primary focus of this study is to propose a distinct vaccination policy and assess its impact on controlling COVID-19 transmission in Malaysia using a Bayesian data-driven approach, concentrating on the year 2021. We employ a compartmental Susceptible-Exposed-Infected-Recovered-Vaccinated (SEIRV) model, incorporating a time-varying transmission rate and a data-driven method for its estimation through an Exploratory Data Analysis (EDA) approach. While no vaccine guarantees total immunity against the disease, and vaccine immunity wanes over time, it is critical to include and accurately estimate vaccine efficacy, as well as a constant vaccine immunity decay or wane factor, to better simulate the dynamics of vaccine-induced protection over time. Based on the distribution and effectiveness of vaccines, we integrated a data-driven estimation of vaccine efficacy, calculated at 75% for Malaysia, underscoring the model's realism and relevance to the specific context of the country. The Bayesian inference framework is used to assimilate various data sources and account for underlying uncertainties in model parameters. The model is fitted to real-world data from Malaysia to analyze disease spread trends and evaluate the effectiveness of our proposed vaccination policy. Our findings reveal that this distinct vaccination policy, which emphasizes an accelerated vaccination rate during the initial stages of the program, is highly effective in mitigating the spread of COVID-19 and substantially reducing the pandemic peak and new infections. The study found that vaccinating 57-66% of the population (as opposed to 76% in the real data) with a better vaccination policy such as proposed here is able to significantly reduce the number of new infections and ultimately reduce the costs associated with new infections. The study contributes to the development of a robust and informative representation of COVID-19 transmission and vaccination, offering valuable insights for policymakers on the potential benefits and limitations of different vaccination policies, particularly highlighting the importance of a well-planned and efficient vaccination rollout strategy. While the methodology used in this study is specifically applied to national data from Malaysia, its successful application to local regions within Malaysia, such as Selangor and Johor, indicates its adaptability and potential for broader application. This demonstrates the model's adaptability for policy assessment and improvement across various demographic and epidemiological landscapes, implying its usefulness for similar datasets from various geographical regions.
  4. Fulltext An Eigenspace approach for detecting multiple space-time disease clusters: Application to measles hotspots detection in Khyber-Pakhtunkhwa, Pakistan
    Ullah S, Daud H, Dass SC, Fanaee-T H, Khalil A
    PLoS One, 2018;13(6):e0199176.
    PMID: 29920540 DOI: 10.1371/journal.pone.0199176
    Identifying the abnormally high-risk regions in a spatiotemporal space that contains an unexpected disease count is helpful to conduct surveillance and implement control strategies. The EigenSpot algorithm has been recently proposed for detecting space-time disease clusters of arbitrary shapes with no restriction on the distribution and quality of the data, and has shown some promising advantages over the state-of-the-art methods. However, the main problem with the EigenSpot method is that it cannot be adapted to detect more than one spatiotemporal hotspot. This is an important limitation, since, in reality, we may have multiple hotspots, sometimes at the same level of importance. We propose an extension of the EigenSpot algorithm, called Multi-EigenSpot that is able to handle multiple hotspots by iteratively removing previously detected hotspots and re-running the algorithm until no more hotspots are found. In addition, a visualization tool (heatmap) has been linked to the proposed algorithm to visualize multiple clusters with different colors. We evaluated the proposed method using the monthly data on measles cases in Khyber-Pakhtunkhwa, Pakistan (Jan 2016- Dec 2016), and the efficiency was compared with the state-of-the-art methods: EigenSpot and Space-time scan statistic (SaTScan). The results showed the effectiveness of the proposed method for detecting multiple clusters in a spatiotemporal space.
  5. Fulltext Curvelet based automatic segmentation of supraspinatus tendon from ultrasound image: a focused assistive diagnostic method
    Gupta R, Elamvazuthi I, Dass SC, Faye I, Vasant P, George J, et al.
    Biomed Eng Online, 2014;13:157.
    PMID: 25471386 DOI: 10.1186/1475-925X-13-157
    Disorders of rotator cuff tendons results in acute pain limiting the normal range of motion for shoulder. Of all the tendons in rotator cuff, supraspinatus (SSP) tendon is affected first of any pathological changes. Diagnosis of SSP tendon using ultrasound is considered to be operator dependent with its accuracy being related to operator's level of experience.
  6. Decoding of visual information from human brain activity: A review of fMRI and EEG studies
    Zafar R, Malik AS, Kamel N, Dass SC, Abdullah JM, Reza F, et al.
    J Integr Neurosci, 2015 Jun;14(2):155-68.
    PMID: 25939499 DOI: 10.1142/S0219635215500089
    Brain is the command center for the body and contains a lot of information which can be extracted by using different non-invasive techniques. Electroencephalography (EEG), Magnetoencephalography (MEG) and functional magnetic resonance imaging (fMRI) are the most common neuroimaging techniques to elicit brain behavior. By using these techniques different activity patterns can be measured within the brain to decode the content of mental processes especially the visual and auditory content. This paper discusses the models and imaging techniques used in visual decoding to investigate the different conditions of brain along with recent advancements in brain decoding. This paper concludes that it's not possible to extract all the information from the brain, however careful experimentation, interpretation and powerful statistical tools can be used with the neuroimaging techniques for better results.
  7. A study of decoding human brain activities from simultaneous data of EEG and fMRI using MVPA
    Zafar R, Kamel N, Naufal M, Malik AS, Dass SC, Ahmad RF, et al.
    Australas Phys Eng Sci Med, 2018 Sep;41(3):633-645.
    PMID: 29948968 DOI: 10.1007/s13246-018-0656-5
    Neuroscientists have investigated the functionality of the brain in detail and achieved remarkable results but this area still need further research. Functional magnetic resonance imaging (fMRI) is considered as the most reliable and accurate technique to decode the human brain activity, on the other hand electroencephalography (EEG) is a portable and low cost solution in brain research. The purpose of this study is to find whether EEG can be used to decode the brain activity patterns like fMRI. In fMRI, data from a very specific brain region is enough to decode the brain activity patterns due to the quality of data. On the other hand, EEG can measure the rapid changes in neuronal activity patterns due to its higher temporal resolution i.e., in msec. These rapid changes mostly occur in different brain regions. In this study, multivariate pattern analysis (MVPA) is used both for EEG and fMRI data analysis and the information is extracted from distributed activation patterns of the brain. The significant information among different classes is extracted using two sample t test in both data sets. Finally, the classification analysis is done using the support vector machine. A fair comparison of both data sets is done using the same analysis techniques, moreover simultaneously collected data of EEG and fMRI is used for this comparison. The final analysis is done with the data of eight participants; the average result of all conditions are found which is 65.7% for EEG data set and 64.1% for fMRI data set. It concludes that EEG is capable of doing brain decoding with the data from multiple brain regions. In other words, decoding accuracy with EEG MVPA is as good as fMRI MVPA and is above chance level.
  8. Fulltext Space-Time Clustering Characteristics of Tuberculosis in Khyber Pakhtunkhwa Province, Pakistan, 2015-2019
    Ullah S, Daud H, Dass SC, Fanaee-T H, Kausarian H, Khalil A
    Int J Environ Res Public Health, 2020 02 21;17(4).
    PMID: 32098247 DOI: 10.3390/ijerph17041413
    The number of tuberculosis (TB) cases in Pakistan ranks fifth in the world. The National TB Control Program (NTP) has recently reported more than 462,920 TB patients in Khyber Pakhtunkhwa province, Pakistan from 2002 to 2017. This study aims to identify spatial and space-time clusters of TB cases in Khyber Pakhtunkhwa province Pakistan during 2015-2019 to design effective interventions. The spatial and space-time cluster analyses were conducted at the district-level based on the reported TB cases from January 2015 to April 2019 using space-time scan statistics (SaTScan). The most likely spatial and space-time clusters were detected in the northern rural part of the province. Additionally, two districts in the west were detected as the secondary space-time clusters. The most likely space-time cluster shows a tendency of spread toward the neighboring districts in the central part, and the most likely spatial cluster shows a tendency of spread toward the neighboring districts in the south. Most of the space-time clusters were detected at the start of the study period 2015-2016. The potential TB clusters in the remote rural part might be associated to the dry-cool climate and lack of access to the healthcare centers in the remote areas.
  9. Decoding of visual activity patterns from fMRI responses using multivariate pattern analyses and convolutional neural network
    Zafar R, Kamel N, Naufal M, Malik AS, Dass SC, Ahmad RF, et al.
    J Integr Neurosci, 2017;16(3):275-289.
    PMID: 28891512 DOI: 10.3233/JIN-170016
    Decoding of human brain activity has always been a primary goal in neuroscience especially with functional magnetic resonance imaging (fMRI) data. In recent years, Convolutional neural network (CNN) has become a popular method for the extraction of features due to its higher accuracy, however it needs a lot of computation and training data. In this study, an algorithm is developed using Multivariate pattern analysis (MVPA) and modified CNN to decode the behavior of brain for different images with limited data set. Selection of significant features is an important part of fMRI data analysis, since it reduces the computational burden and improves the prediction performance; significant features are selected using t-test. MVPA uses machine learning algorithms to classify different brain states and helps in prediction during the task. General linear model (GLM) is used to find the unknown parameters of every individual voxel and the classification is done using multi-class support vector machine (SVM). MVPA-CNN based proposed algorithm is compared with region of interest (ROI) based method and MVPA based estimated values. The proposed method showed better overall accuracy (68.6%) compared to ROI (61.88%) and estimation values (64.17%).
  10. Fulltext A data driven change-point epidemic model for assessing the impact of large gathering and subsequent movement control order on COVID-19 spread in Malaysia
    Dass SC, Kwok WM, Gibson GJ, Gill BS, Sundram BM, Singh S
    PLoS One, 2021;16(5):e0252136.
    PMID: 34043676 DOI: 10.1371/journal.pone.0252136
    The second wave of COVID-19 in Malaysia is largely attributed to a four-day mass gathering held in Sri Petaling from February 27, 2020, which contributed to an exponential rise of COVID-19 cases in the country. Starting from March 18, 2020, the Malaysian government introduced four consecutive phases of a Movement Control Order (MCO) to stem the spread of COVID-19. The MCO was implemented through various non-pharmaceutical interventions (NPIs). The reported number of cases reached its peak by the first week of April and then started to reduce, hence proving the effectiveness of the MCO. To gain a quantitative understanding of the effect of MCO on the dynamics of COVID-19, this paper develops a class of mathematical models to capture the disease spread before and after MCO implementation in Malaysia. A heterogeneous variant of the Susceptible-Exposed-Infected-Recovered (SEIR) model is developed with additional compartments for asymptomatic transmission. Further, a change-point is incorporated to model disease dynamics before and after intervention which is inferred based on data. Related statistical analyses for inference are developed in a Bayesian framework and are able to provide quantitative assessments of (1) the impact of the Sri Petaling gathering, and (2) the extent of decreasing transmission during the MCO period. The analysis here also quantitatively demonstrates how quickly transmission rates fall under effective NPI implementation within a short time period. The models and methodology used provided important insights into the nature of local transmissions to decision makers in the Ministry of Health, Malaysia.
  11. Fulltext Determine neighboring region spatial effect on dengue cases using ensemble ARIMA models
    Thiruchelvam L, Dass SC, Asirvadam VS, Daud H, Gill BS
    Sci Rep, 2021 Mar 12;11(1):5873.
    PMID: 33712664 DOI: 10.1038/s41598-021-84176-y
    The state of Selangor, in Malaysia consist of urban and peri-urban centres with good transportation system, and suitable temperature levels with high precipitations and humidity which make the state ideal for high number of dengue cases, annually. This study investigates if districts within the Selangor state do influence each other in determining pattern of dengue cases. Study compares two different models; the Autoregressive Integrated Moving Average (ARIMA) and Ensemble ARIMA models, using the Akaike Information Criterion (AIC) and Bayesian Information Criterion (BIC) measurement to gauge their performance tools. ARIMA model is developed using the epidemiological data of dengue cases, whereas ensemble ARIMA incorporates the neighbouring regions' dengue models as the exogenous variable (X), into traditional ARIMA model. Ensemble ARIMA models have better model fit compared to the basic ARIMA models by incorporating neighbuoring effects of seven districts which made of state of Selangor. The AIC and BIC values of ensemble ARIMA models to be smaller compared to traditional ARIMA counterpart models. Thus, study concludes that pattern of dengue cases for a district is subject to spatial effects of its neighbouring districts and number of dengue cases in the surrounding areas.
  12. Fulltext Working Memory Performance under a Negative Affect Is More Susceptible to Higher Cognitive Workloads with Different Neural Haemodynamic Correlates
    Feng YX, Kiguchi M, Ung WC, Dass SC, Mohd Hani AF, Tang TB, et al.
    Brain Sci, 2021 Jul 15;11(7).
    PMID: 34356169 DOI: 10.3390/brainsci11070935
    The effect of stress on task performance is complex, too much or too little stress negatively affects performance and there exists an optimal level of stress to drive optimal performance. Task difficulty and external affective factors are distinct stressors that impact cognitive performance. Neuroimaging studies showed that mood affects working memory performance and the correlates are changes in haemodynamic activity in the prefrontal cortex (PFC). We investigate the interactive effects of affective states and working memory load (WML) on working memory task performance and haemodynamic activity using functional near-infrared spectroscopy (fNIRS) neuroimaging on the PFC of healthy participants. We seek to understand if haemodynamic responses could tell apart workload-related stress from situational stress arising from external affective distraction. We found that the haemodynamic changes towards affective stressor- and workload-related stress were more dominant in the medial and lateral PFC, respectively. Our study reveals distinct affective state-dependent modulations of haemodynamic activity with increasing WML in n-back tasks, which correlate with decreasing performance. The influence of a negative effect on performance is greater at higher WML, and haemodynamic activity showed evident changes in temporal, and both spatial and strength of activation differently with WML.
  13. Fulltext Effectiveness of the movement control measures during the third wave of COVID-19 in Malaysia
    Md Zamri ASS, Singh S, Ghazali SM, Herng LC, Dass SC, Aris T, et al.
    Epidemiol Health, 2021 Sep 23.
    PMID: 34607399 DOI: 10.4178/epih.e2021073
    Objectives: Since March 2020, several phases of the movement control measures were instituted in Malaysia to break the COVID-19 chain of transmission. In this study, we developed Susceptible-Exposed-Infected-Recovered (SEIR) models to examine the effects of the various movement control phases on the disease transmissibility and case trends during the third COVID-19 wave in Malaysia.

    Methods: Three SEIR models were developed using the R programming software ODIN interface based on COVID-19 case data from 1 September 2020 to 29 March 2021. The models were validated and subsequently used to provide forecasts of daily cases from 14 October 2020 to 29 March 2021 based on three movement control phases.

    Results: We found that the R values had reduced by 59.1% from an initial high of 2.2 during the Nationwide Recovery Movement Control Order (RMCO) to 0.9 during the Movement Control Order (MCO) and Conditional MCO (CMCO) phases. In addition, the observed cumulative and daily highest cases were much lower compared to the forecast cumulative and daily highest cases at 64.4% to 98.9% and 68.8% to 99.8%, respectively.

    Conclusion: We conclude that the movement control measures were able to progressively reduce the R values during the COVID-19 pandemic. In addition, more stringent movement control measures such as the MCO and CMCO were effective in reducing the R values and case numbers further during the third wave of COVID-19 outbreak in Malaysia due to their higher stringency levels compared to the Nationwide RMCO.

  14. Fulltext Forecasting COVID-19 Case Trends Using SARIMA Models during the Third Wave of COVID-19 in Malaysia
    Tan CV, Singh S, Lai CH, Zamri ASSM, Dass SC, Aris TB, et al.
    Int J Environ Res Public Health, 2022 Jan 28;19(3).
    PMID: 35162523 DOI: 10.3390/ijerph19031504
    With many countries experiencing a resurgence in COVID-19 cases, it is important to forecast disease trends to enable effective planning and implementation of control measures. This study aims to develop Seasonal Autoregressive Integrated Moving Average (SARIMA) models using 593 data points and smoothened case and covariate time-series data to generate a 28-day forecast of COVID-19 case trends during the third wave in Malaysia. SARIMA models were developed using COVID-19 case data sourced from the Ministry of Health Malaysia's official website. Model training and validation was conducted from 22 January 2020 to 5 September 2021 using daily COVID-19 case data. The SARIMA model with the lowest root mean square error (RMSE), mean absolute percentage error (MAE) and Bayesian information criterion (BIC) was selected to generate forecasts from 6 September to 3 October 2021. The best SARIMA model with a RMSE = 73.374, MAE = 39.716 and BIC = 8.656 showed a downward trend of COVID-19 cases during the forecast period, wherein the observed daily cases were within the forecast range. The majority (89%) of the difference between the forecasted and observed values was well within a deviation range of 25%. Based on this work, we conclude that SARIMA models developed in this paper using 593 data points and smoothened data and sensitive covariates can generate accurate forecast of COVID-19 case trends.
  15. Fulltext Tracking the early depleting transmission dynamics of COVID-19 with a time-varying SIR model
    Law KB, Peariasamy KM, Gill BS, Singh S, Sundram BM, Rajendran K, et al.
    Sci Rep, 2020 12 10;10(1):21721.
    PMID: 33303925 DOI: 10.1038/s41598-020-78739-8
    The susceptible-infectious-removed (SIR) model offers the simplest framework to study transmission dynamics of COVID-19, however, it does not factor in its early depleting trend observed during a lockdown. We modified the SIR model to specifically simulate the early depleting transmission dynamics of COVID-19 to better predict its temporal trend in Malaysia. The classical SIR model was fitted to observed total (I total), active (I) and removed (R) cases of COVID-19 before lockdown to estimate the basic reproduction number. Next, the model was modified with a partial time-varying force of infection, given by a proportionally depleting transmission coefficient, [Formula: see text] and a fractional term, z. The modified SIR model was then fitted to observed data over 6 weeks during the lockdown. Model fitting and projection were validated using the mean absolute percent error (MAPE). The transmission dynamics of COVID-19 was interrupted immediately by the lockdown. The modified SIR model projected the depleting temporal trends with lowest MAPE for I total, followed by I, I daily and R. During lockdown, the dynamics of COVID-19 depleted at a rate of 4.7% each day with a decreased capacity of 40%. For 7-day and 14-day projections, the modified SIR model accurately predicted I total, I and R. The depleting transmission dynamics for COVID-19 during lockdown can be accurately captured by time-varying SIR model. Projection generated based on observed data is useful for future planning and control of COVID-19.
  16. Fulltext The effects of super spreading events and movement control measures on the COVID-19 pandemic in Malaysia
    Herng LC, Singh S, Sundram BM, Zamri ASSM, Vei TC, Aris T, et al.
    Sci Rep, 2022 02 09;12(1):2197.
    PMID: 35140319 DOI: 10.1038/s41598-022-06341-1
    This paper aims to develop an automated web application to generate validated daily effective reproduction numbers (Rt) which can be used to examine the effects of super-spreading events due to mass gatherings and the effectiveness of the various Movement Control Order (MCO) stringency levels on the outbreak progression of COVID-19 in Malaysia. The effective reproduction number, Rt, was estimated by adopting and modifying an Rt estimation algorithm using a validated distribution mean of 3.96 and standard deviation of 4.75 with a seven-day sliding window. The Rt values generated were validated using thea moving window SEIR model with a negative binomial likelihood fitted using methods from the Bayesian inferential framework. A Pearson's correlation between the Rt values estimated by the algorithm and the SEIR model was r = 0.70, p 
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