Displaying publications 121 - 140 of 311 in total

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  1. Fulltext Optimal designs of the median run length based double sampling X chart for minimizing the average sample size
    Teoh WL, Khoo MB, Teh SY
    PLoS One, 2013;8(7):e68580.
    PMID: 23935873 DOI: 10.1371/journal.pone.0068580
    Designs of the double sampling (DS) X chart are traditionally based on the average run length (ARL) criterion. However, the shape of the run length distribution changes with the process mean shifts, ranging from highly skewed when the process is in-control to almost symmetric when the mean shift is large. Therefore, we show that the ARL is a complicated performance measure and that the median run length (MRL) is a more meaningful measure to depend on. This is because the MRL provides an intuitive and a fair representation of the central tendency, especially for the rightly skewed run length distribution. Since the DS X chart can effectively reduce the sample size without reducing the statistical efficiency, this paper proposes two optimal designs of the MRL-based DS X chart, for minimizing (i) the in-control average sample size (ASS) and (ii) both the in-control and out-of-control ASSs. Comparisons with the optimal MRL-based EWMA X and Shewhart X charts demonstrate the superiority of the proposed optimal MRL-based DS X chart, as the latter requires a smaller sample size on the average while maintaining the same detection speed as the two former charts. An example involving the added potassium sorbate in a yoghurt manufacturing process is used to illustrate the effectiveness of the proposed MRL-based DS X chart in reducing the sample size needed.
    Matched MeSH terms: Models, Statistical*
  2. A comparative study of count models: application to pedestrian-vehicle crashes along Malaysia federal roads
    Hosseinpour M, Pour MH, Prasetijo J, Yahaya AS, Ghadiri SM
    Traffic Inj Prev, 2013;14(6):630-8.
    PMID: 23859313 DOI: 10.1080/15389588.2012.736649
    The objective of this study was to examine the effects of various roadway characteristics on the incidence of pedestrian-vehicle crashes by developing a set of crash prediction models on 543 km of Malaysia federal roads over a 4-year time span between 2007 and 2010.
    Matched MeSH terms: Models, Statistical*
  3. Fulltext Hybrid support vector regression and autoregressive integrated moving average models improved by particle swarm optimization for property crime rates forecasting with economic indicators
    Alwee R, Shamsuddin SM, Sallehuddin R
    ScientificWorldJournal, 2013;2013:951475.
    PMID: 23766729 DOI: 10.1155/2013/951475
    Crimes forecasting is an important area in the field of criminology. Linear models, such as regression and econometric models, are commonly applied in crime forecasting. However, in real crimes data, it is common that the data consists of both linear and nonlinear components. A single model may not be sufficient to identify all the characteristics of the data. The purpose of this study is to introduce a hybrid model that combines support vector regression (SVR) and autoregressive integrated moving average (ARIMA) to be applied in crime rates forecasting. SVR is very robust with small training data and high-dimensional problem. Meanwhile, ARIMA has the ability to model several types of time series. However, the accuracy of the SVR model depends on values of its parameters, while ARIMA is not robust to be applied to small data sets. Therefore, to overcome this problem, particle swarm optimization is used to estimate the parameters of the SVR and ARIMA models. The proposed hybrid model is used to forecast the property crime rates of the United State based on economic indicators. The experimental results show that the proposed hybrid model is able to produce more accurate forecasting results as compared to the individual models.
    Matched MeSH terms: Models, Statistical*
  4. Fulltext Synthetic-type control charts for time-between-events monitoring
    Yen FY, Chong KM, Ha LM
    PLoS One, 2013;8(6):e65440.
    PMID: 23755231 DOI: 10.1371/journal.pone.0065440
    This paper proposes three synthetic-type control charts to monitor the mean time-between-events of a homogenous Poisson process. The first proposed chart combines an Erlang (cumulative time between events, Tr ) chart and a conforming run length (CRL) chart, denoted as Synth-Tr chart. The second proposed chart combines an exponential (or T) chart and a group conforming run length (GCRL) chart, denoted as GR-T chart. The third proposed chart combines an Erlang chart and a GCRL chart, denoted as GR-Tr chart. By using a Markov chain approach, the zero- and steady-state average number of observations to signal (ANOS) of the proposed charts are obtained, in order to evaluate the performance of the three charts. The optimal design of the proposed charts is shown in this paper. The proposed charts are superior to the existing T chart, Tr chart, and Synth-T chart. As compared to the EWMA-T chart, the GR-T chart performs better in detecting large shifts, in terms of the zero- and steady-state performances. The zero-state Synth-T4 and GR-Tr (r = 3 or 4) charts outperform the EWMA-T chart for all shifts, whereas the Synth-Tr (r = 2 or 3) and GR-T 2 charts perform better for moderate to large shifts. For the steady-state process, the Synth-Tr and GR-Tr charts are more efficient than the EWMA-T chart in detecting small to moderate shifts.
    Matched MeSH terms: Models, Statistical*
  5. Fulltext Statistical methods used to test for agreement of medical instruments measuring continuous variables in method comparison studies: a systematic review
    Zaki R, Bulgiba A, Ismail R, Ismail NA
    PLoS One, 2012;7(5):e37908.
    PMID: 22662248 DOI: 10.1371/journal.pone.0037908
    Accurate values are a must in medicine. An important parameter in determining the quality of a medical instrument is agreement with a gold standard. Various statistical methods have been used to test for agreement. Some of these methods have been shown to be inappropriate. This can result in misleading conclusions about the validity of an instrument. The Bland-Altman method is the most popular method judging by the many citations of the article proposing this method. However, the number of citations does not necessarily mean that this method has been applied in agreement research. No previous study has been conducted to look into this. This is the first systematic review to identify statistical methods used to test for agreement of medical instruments. The proportion of various statistical methods found in this review will also reflect the proportion of medical instruments that have been validated using those particular methods in current clinical practice.
    Matched MeSH terms: Models, Statistical*
  6. The absorbed dose in femur exposed to diagnostic radiography
    Salehi Z, Yusoff AL
    Radiat Prot Dosimetry, 2013;154(3):396-9.
    PMID: 23012482 DOI: 10.1093/rpd/ncs239
    A femur phantom made of wax and a real human bone was used to study the dose during radiographical procedures. The depth dose inside the phantom was determined using DOSXYZnrc, a Monte Carlo simulation software. The results were verified with measurements using TLD-100H. It was found that for 2.5 mm aluminium filtered 84-kVp X-rays, the radiation dose in the bone reached 57 % higher than the surface dose, i.e. 3.23 mGy as opposed to 2.06 mGy at the surface. The use of real bone introduces variations in the bone density in the DOSXYZnrc model, resulting in a lower attenuation effect than expected from solid bone tissues.
    Matched MeSH terms: Models, Statistical*
  7. A modified binary particle swarm optimization for selecting the small subset of informative genes from gene expression data
    Mohamad MS, Omatu S, Deris S, Yoshioka M
    IEEE Trans Inf Technol Biomed, 2011 Nov;15(6):813-22.
    PMID: 21914573 DOI: 10.1109/TITB.2011.2167756
    Gene expression data are expected to be of significant help in the development of efficient cancer diagnoses and classification platforms. In order to select a small subset of informative genes from the data for cancer classification, recently, many researchers are analyzing gene expression data using various computational intelligence methods. However, due to the small number of samples compared to the huge number of genes (high dimension), irrelevant genes, and noisy genes, many of the computational methods face difficulties to select the small subset. Thus, we propose an improved (modified) binary particle swarm optimization to select the small subset of informative genes that is relevant for the cancer classification. In this proposed method, we introduce particles' speed for giving the rate at which a particle changes its position, and we propose a rule for updating particle's positions. By performing experiments on ten different gene expression datasets, we have found that the performance of the proposed method is superior to other previous related works, including the conventional version of binary particle swarm optimization (BPSO) in terms of classification accuracy and the number of selected genes. The proposed method also produces lower running times compared to BPSO.
    Matched MeSH terms: Models, Statistical*
  8. Single image signal-to-noise ratio estimation for magnetic resonance images
    Sim KS, Lai MA, Tso CP, Teo CC
    J Med Syst, 2011 Feb;35(1):39-48.
    PMID: 20703587 DOI: 10.1007/s10916-009-9339-9
    A novel technique to quantify the signal-to-noise ratio (SNR) of magnetic resonance images is developed. The image SNR is quantified by estimating the amplitude of the signal spectrum using the autocorrelation function of just one single magnetic resonance image. To test the performance of the quantification, SNR measurement data are fitted to theoretically expected curves. It is shown that the technique can be implemented in a highly efficient way for the magnetic resonance imaging system.
    Matched MeSH terms: Models, Statistical*
  9. Development of statistical models for predicting muscle and mental activities during repetitive precision tasks
    Zadry HR, Dawal SZ, Taha Z
    Int J Occup Saf Ergon, 2016 Sep;22(3):374-83.
    PMID: 27053140 DOI: 10.1080/10803548.2016.1150094
    This study was conducted to develop muscle and mental activities on repetitive precision tasks. A laboratory experiment was used to address the objectives. Surface electromyography was used to measure muscle activities from eight upper limb muscles, while electroencephalography recorded mental activities from six channels. Fourteen university students participated in the study. The results show that muscle and mental activities increase for all tasks, indicating the occurrence of muscle and mental fatigue. A linear relationship between muscle activity, mental activity and time was found while subjects were performing the task. Thus, models were developed using those variables. The models were found valid after validation using other students' and workers' data. Findings from this study can contribute as a reference for future studies investigating muscle and mental activity and can be applied in industry as guidelines to manage muscle and mental fatigue, especially to manage job schedules and rotation.
    Matched MeSH terms: Models, Statistical*
  10. A macroscopic approach to demography
    Smith JD
    J Math Biol, 2004 Jan;48(1):105-18.
    PMID: 14685774
    A canonical/lognormal model for human demography is established, specifying the net maternity function and the age distribution for mothers of new-borns using a single macroscopic parameter vector of dimension five. The age distribution of mothers is canonical, while the net maternity function normalizes to a lognormal density. Comparison of an actual population with the model serves to identify anomalies in the population which may be indicative of phase transitions or influences from levels outside the demographic. Tracking the time development of the parameter vector may be used to predict the future state of a population, or to interpolate for data missing from the record. In accordance with classical theoretical considerations of Backman, Prigogine, et al., it emerges that the logarithm of a mother's age is the most fundamental time variable for demographic purposes.
    Matched MeSH terms: Models, Statistical*
  11. Fulltext Forecasting daily confirmed COVID-19 cases in Malaysia using ARIMA models
    Singh S, Murali Sundram B, Rajendran K, Boon Law K, Aris T, Ibrahim H, et al.
    J Infect Dev Ctries, 2020 09 30;14(9):971-976.
    PMID: 33031083 DOI: 10.3855/jidc.13116
    INTRODUCTION: The novel coronavirus infection has become a global threat affecting almost every country in the world. As a result, it has become important to understand the disease trends in order to mitigate its effects. The aim of this study is firstly to develop a prediction model for daily confirmed COVID-19 cases based on several covariates, and secondly, to select the best prediction model based on a subset of these covariates.

    METHODOLOGY: This study was conducted using daily confirmed cases of COVID-19 collected from the official Ministry of Health, Malaysia (MOH) and John Hopkins University websites. An Autoregressive Integrated Moving Average (ARIMA) model was fitted to the training data of observed cases from 22 January to 31 March 2020, and subsequently validated using data on cases from 1 April to 17 April 2020. The ARIMA model satisfactorily forecasted the daily confirmed COVID-19 cases from 18 April 2020 to 1 May 2020 (the testing phase).

    RESULTS: The ARIMA (0,1,0) model produced the best fit to the observed data with a Mean Absolute Percentage Error (MAPE) value of 16.01 and a Bayes Information Criteria (BIC) value of 4.170. The forecasted values showed a downward trend of COVID-19 cases until 1 May 2020. Observed cases during the forecast period were accurately predicted and were placed within the prediction intervals generated by the fitted model.

    CONCLUSIONS: This study finds that ARIMA models with optimally selected covariates are useful tools for monitoring and predicting trends of COVID-19 cases in Malaysia.

    Matched MeSH terms: Models, Statistical*
  12. Evaluation of model-based methods in estimating respiratory mechanics in the presence of variable patient effort
    Redmond DP, Chiew YS, Major V, Chase JG
    Comput Methods Programs Biomed, 2019 Apr;171:67-79.
    PMID: 27697371 DOI: 10.1016/j.cmpb.2016.09.011
    Monitoring of respiratory mechanics is required for guiding patient-specific mechanical ventilation settings in critical care. Many models of respiratory mechanics perform poorly in the presence of variable patient effort. Typical modelling approaches either attempt to mitigate the effect of the patient effort on the airway pressure waveforms, or attempt to capture the size and shape of the patient effort. This work analyses a range of methods to identify respiratory mechanics in volume controlled ventilation modes when there is patient effort. The models are compared using 4 Datasets, each with a sample of 30 breaths before, and 2-3 minutes after sedation has been administered. The sedation will reduce patient efforts, but the underlying pulmonary mechanical properties are unlikely to change during this short time. Model identified parameters from breathing cycles with patient effort are compared to breathing cycles that do not have patient effort. All models have advantages and disadvantages, so model selection may be specific to the respiratory mechanics application. However, in general, the combined method of iterative interpolative pressure reconstruction, and stacking multiple consecutive breaths together has the best performance over the Dataset. The variability of identified elastance when there is patient effort is the lowest with this method, and there is little systematic offset in identified mechanics when sedation is administered.
    Matched MeSH terms: Models, Statistical*
  13. An effective extracellular protein secretion by an ABC transporter system in Escherichia coli: statistical modeling and optimization of cyclodextrin glucanotransferase secretory production
    Low KO, Mahadi NM, Rahim RA, Rabu A, Abu Bakar FD, Murad AM, et al.
    J Ind Microbiol Biotechnol, 2011 Sep;38(9):1587-97.
    PMID: 21336875 DOI: 10.1007/s10295-011-0949-0
    Direct transport of recombinant protein from cytosol to extracellular medium offers great advantages, such as high specific activity and a simple purification step. This work presents an investigation on the potential of an ABC (ATP-binding cassette) transporter system, the hemolysin transport system, for efficient protein secretion in Escherichia coli (E. coli). A higher secretory production of recombinant cyclodextrin glucanotransferase (CGTase) was achieved by a new plasmid design and subsequently by optimization of culture conditions via central composite design. An improvement of at least fourfold extracellular recombinant CGTase was obtained using the new plasmid design. The optimization process consisted of 20 experiments involving six star points and six replicates at the central point. The predicted optimum culture conditions for maximum recombinant CGTase secretion were found to be 25.76 μM IPTG, 1.0% (w/v) arabinose and 34.7°C post-induction temperature, with a predicted extracellular CGTase activity of 68.76 U/ml. Validation of the model gave an extracellular CGTase activity of 69.15 ± 0.71 U/ml, resulting in a 3.45-fold increase compared to the initial conditions. This corresponded to an extracellular CGTase yield of about 0.58 mg/l. We showed that a synergistic balance of transported protein and secretory pathway is important for efficient protein transport. In addition, we also demonstrated the first successful removal of the C-terminal secretion signal from the transported fusion protein by thrombin proteolytic cleavage.
    Matched MeSH terms: Models, Statistical*
  14. The use of response surface methodology for enhanced production of a thermostable bacterial lipase in a novel yeast system
    Abu ML, Mohammad R, Oslan SN, Salleh AB
    Prep Biochem Biotechnol, 2021;51(4):350-360.
    PMID: 32940138 DOI: 10.1080/10826068.2020.1818256
    A thermostable bacterial lipase from Geobacillus zalihae was expressed in a novel yeast Pichia sp. strain SO. The preliminary expression was too low and discourages industrial production. This study sought to investigate the optimum conditions for T1 lipase production in Pichia sp. strain SO. Seven medium conditions were investigated and optimized using Response Surface Methodology (RSM). Five responding conditions namely; temperature, inoculum size, incubation time, culture volume and agitation speed observed through Plackett-Burman Design (PBD) method had a significant effect on T1 lipase production. The medium conditions were optimized using Box-Behnken Design (BBD). Investigations reveal that the optimum conditions for T1 lipase production and Biomass concentration (OD600) were; Temperature 31.76 °C, incubation time 39.33 h, culture volume 132.19 mL, inoculum size 3.64%, and agitation speed of 288.2 rpm with a 95% PI low as; 12.41 U/mL and 95% PI high of 13.65 U/mL with an OD600 of; 95% PI low as; 19.62 and 95% PI high as; 22.62 as generated by the software was also validated. These predicted parameters were investigated experimentally and the experimental result for lipase activity observed was 13.72 U/mL with an OD600 of 24.5. At these optimum conditions, there was a 3-fold increase on T1 lipase activity. This study is the first to develop a statistical model for T1 lipase production and biomass concentration in Pichia sp. Strain SO. The optimized production of T1 lipase presents a choice for its industrial application.
    Matched MeSH terms: Models, Statistical*
  15. Rasch analysis of the Malaysian Secondary School Student Leadership Inventory (M3SLI)
    Ling MT
    J Appl Meas, 2016;17(3):365-392.
    PMID: 28027058
    The importance of instilling leadership skills in students has always been a main subject of discussion in Malaysia. Malaysian Secondary School Students Leadership Inventory (M3SLI) is an instrument which has been piloted tested in year 2013. The main purpose of this study is to examine and optimize the functioning of the rating scale categories in M3SLI by investigating the rating scale category counts, average and expected rating scale category measures, and steps calibrations. In detail, the study was aimed to (1) identify whether the five-point rating scale was functioning as intended and (2) review the effect of a rating scale category revision on the psychometric characteristics of M3SLI. The study was carried out on students aged between 13 to 18 years (2183 students) by stratified random sampling in 26 public schools in Sabah, Malaysia, with the results analysed using Winsteps. This study found that the rating scale of Personality and Values constructs needed to be modified while the scale for Leadership Skills was maintained. For future studies, other aspects of psychometric properties like differential item functioning (DIF) based on demographic variables such as gender, school locations and forms should be researched on prior to the use of the instrument.
    Matched MeSH terms: Models, Statistical*
  16. Factor structure of parent and teacher ratings of the ODD symptoms for Malaysian primary school children
    Gomez R
    Asian J Psychiatr, 2017 Feb;25:22-26.
    PMID: 28262156 DOI: 10.1016/j.ajp.2016.10.013
    This present study used confirmatory factor analysis (CFA) to examine the applicability of one-, two- three- and second order Oppositional Defiant Disorder (ODD) factor models, proposed in previous studies, in a group of Malaysian primary school children. These models were primarily based on parent reports. In the current study, parent and teacher ratings of the ODD symptoms were obtained for 934 children. For both groups of respondents, the findings showing some support for all models examined, with most support for a second order model with Burke et al. (2010) three factors (oppositional, antagonistic, and negative affect) as the primary factors. The diagnostic implications of the findings are discussed.
    Matched MeSH terms: Models, Statistical*
  17. Fulltext Similarity measure and domain adaptation in multiple mixture model clustering: An application to image processing
    Leong SH, Ong SH
    PLoS One, 2017;12(7):e0180307.
    PMID: 28686634 DOI: 10.1371/journal.pone.0180307
    This paper considers three crucial issues in processing scaled down image, the representation of partial image, similarity measure and domain adaptation. Two Gaussian mixture model based algorithms are proposed to effectively preserve image details and avoids image degradation. Multiple partial images are clustered separately through Gaussian mixture model clustering with a scan and select procedure to enhance the inclusion of small image details. The local image features, represented by maximum likelihood estimates of the mixture components, are classified by using the modified Bayes factor (MBF) as a similarity measure. The detection of novel local features from MBF will suggest domain adaptation, which is changing the number of components of the Gaussian mixture model. The performance of the proposed algorithms are evaluated with simulated data and real images and it is shown to perform much better than existing Gaussian mixture model based algorithms in reproducing images with higher structural similarity index.
    Matched MeSH terms: Models, Statistical*
  18. Fulltext Global mortality variations in patients with heart failure: results from the International Congestive Heart Failure (INTER-CHF) prospective cohort study
    Dokainish H, Teo K, Zhu J, Roy A, AlHabib KF, ElSayed A, et al.
    Lancet Glob Health, 2017 07;5(7):e665-e672.
    PMID: 28476564 DOI: 10.1016/S2214-109X(17)30196-1
    BACKGROUND: Most data on mortality and prognostic factors in patients with heart failure come from North America and Europe, with little information from other regions. Here, in the International Congestive Heart Failure (INTER-CHF) study, we aimed to measure mortality at 1 year in patients with heart failure in Africa, China, India, the Middle East, southeast Asia and South America; we also explored demographic, clinical, and socioeconomic variables associated with mortality.

    METHODS: We enrolled consecutive patients with heart failure (3695 [66%] clinic outpatients, 2105 [34%] hospital in patients) from 108 centres in six geographical regions. We recorded baseline demographic and clinical characteristics and followed up patients at 6 months and 1 year from enrolment to record symptoms, medications, and outcomes. Time to death was studied with Cox proportional hazards models adjusted for demographic and clinical variables, medications, socioeconomic variables, and region. We used the explained risk statistic to calculate the relative contribution of each level of adjustment to the risk of death.

    FINDINGS: We enrolled 5823 patients within 1 year (with 98% follow-up). Overall mortality was 16·5%: highest in Africa (34%) and India (23%), intermediate in southeast Asia (15%), and lowest in China (7%), South America (9%), and the Middle East (9%). Regional differences persisted after multivariable adjustment. Independent predictors of mortality included cardiac variables (New York Heart Association Functional Class III or IV, previous admission for heart failure, and valve disease) and non-cardiac variables (body-mass index, chronic kidney disease, and chronic obstructive pulmonary disease). 46% of mortality risk was explained by multivariable modelling with these variables; however, the remainder was unexplained.

    INTERPRETATION: Marked regional differences in mortality in patients with heart failure persisted after multivariable adjustment for cardiac and non-cardiac factors. Therefore, variations in mortality between regions could be the result of health-care infrastructure, quality and access, or environmental and genetic factors. Further studies in large, global cohorts are needed.

    FUNDING: The study was supported by Novartis.

    Study site: Multination
    Matched MeSH terms: Models, Statistical*
  19. Fulltext Acoustic emission corrosion feature extraction and severity prediction using hybrid wavelet packet transform and linear support vector classifier
    May Z, Alam MK, Nayan NA, Rahman NAA, Mahmud MS
    PLoS One, 2021;16(12):e0261040.
    PMID: 34914761 DOI: 10.1371/journal.pone.0261040
    Corrosion in carbon-steel pipelines leads to failure, which is a major cause of breakdown maintenance in the oil and gas industries. The acoustic emission (AE) signal is a reliable method for corrosion detection and classification in the modern Structural Health Monitoring (SHM) system. The efficiency of this system in detection and classification mainly depends on the suitable AE features. Therefore, many feature extraction and classification methods have been developed for corrosion detection and severity assessment. However, the extraction of appropriate AE features and classification of various levels of corrosion utilizing these extracted features are still challenging issues. To overcome these issues, this article proposes a hybrid machine learning approach that combines Wavelet Packet Transform (WPT) integrated with Fast Fourier Transform (FFT) for multiresolution feature extraction and Linear Support Vector Classifier (L-SVC) for predicting corrosion severity levels. A Laboratory-based Linear Polarization Resistance (LPR) test was performed on carbon-steel samples for AE data acquisition over a different time span. AE signals were collected at a high sampling rate with a sound well AE sensor using AEWin software. Simulation results show a linear relationship between the proposed approach-based extracted AE features and the corrosion process. For multi-class problems, three corrosion severity stages have been made based on the corrosion rate over time and AE activity. The ANOVA test results indicate the significance within and between the feature-groups where F-values (F-value>1) rejects the null hypothesis and P-values (P-value<0.05) are less than the significance level. The utilized L-SVC classifier achieves higher prediction accuracy of 99.0% than the accuracy of other benchmarked classifiers. Findings of our proposed machine learning approach confirm that it can be effectively utilized for corrosion detection and severity assessment in SHM applications.
    Matched MeSH terms: Models, Statistical*
  20. The present and future disease burden of hepatitis C virus infections with today's treatment paradigm: Volume 4
    Chan HLY, Chen CJ, Omede O, Al Qamish J, Al Naamani K, Bane A, et al.
    J Viral Hepat, 2017 10;24 Suppl 2:25-43.
    PMID: 29105283 DOI: 10.1111/jvh.12760
    Factors influencing the morbidity and mortality associated with viremic hepatitis C virus (HCV) infection change over time and place, making it difficult to compare reported estimates. Models were developed for 17 countries (Bahrain, Bulgaria, Cameroon, Colombia, Croatia, Dominican Republic, Ethiopia, Ghana, Hong Kong, Jordan, Kazakhstan, Malaysia, Morocco, Nigeria, Qatar and Taiwan) to quantify and characterize the viremic population as well as forecast the changes in the infected population and the corresponding disease burden from 2015 to 2030. Model inputs were agreed upon through expert consensus, and a standardized methodology was followed to allow for comparison across countries. The viremic prevalence is expected to remain constant or decline in all but four countries (Ethiopia, Ghana, Jordan and Oman); however, HCV-related morbidity and mortality will increase in all countries except Qatar and Taiwan. In Qatar, the high-treatment rate will contribute to a reduction in total cases and HCV-related morbidity by 2030. In the remaining countries, however, the current treatment paradigm will be insufficient to achieve large reductions in HCV-related morbidity and mortality.
    Matched MeSH terms: Models, Statistical*
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