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Multi-objective evolutionary polynomial regression-based prediction of energy consumption probing

Bonakdari H, Ebtehaj I, Akhbari A

Water Sci Technol, 2017 Jun;75(12):2791-2799.
PMID: 28659519 DOI: 10.2166/wst.2017.158

Electrocoagulation (EC) is employed to investigate the energy consumption (EnC) of synthetic wastewater. In order to find the best process conditions, the influence of various parameters including initial pH, initial dye concentration, applied voltage, initial electrolyte concentration, and treatment time are investigated in this study. EnC is considered the main criterion of process evaluation in investigating the effect of the independent variables on the EC process and determining the optimum condition. Evolutionary polynomial regression is combined with a multi-objective genetic algorithm (EPR-MOGA) to present a new, simple and accurate equation for estimating EnC to overcome existing method weaknesses. To survey the influence of the effective variables, six different input combinations are considered. According to the results, EPR-MOGA Model 1 is the most accurate compared to other models, as it has the lowest error indices in predicting EnC (MARE = 0.35, RMSE = 2.33, SI = 0.23 and R2 = 0.98). A comparison of EPR-MOGA with reduced quadratic multiple regression methods in terms of feasibility confirms that EPR-MOGA is an effective alternative method. Moreover, the partial derivative sensitivity analysis method is employed to analyze the EnC variation trend according to input variables.

Matched MeSH terms: Models, Statistical*
A comparison of bivariate, multivariate random-effects, and Poisson correlated gamma-frailty models to meta-analyze individual patient data of ordinal scale diagnostic tests

Simoneau G, Levis B, Cuijpers P, Ioannidis JPA, Patten SB, Shrier I, et al.

Biom J, 2017 Nov;59(6):1317-1338.
PMID: 28692782 DOI: 10.1002/bimj.201600184

Individual patient data (IPD) meta-analyses are increasingly common in the literature. In the context of estimating the diagnostic accuracy of ordinal or semi-continuous scale tests, sensitivity and specificity are often reported for a given threshold or a small set of thresholds, and a meta-analysis is conducted via a bivariate approach to account for their correlation. When IPD are available, sensitivity and specificity can be pooled for every possible threshold. Our objective was to compare the bivariate approach, which can be applied separately at every threshold, to two multivariate methods: the ordinal multivariate random-effects model and the Poisson correlated gamma-frailty model. Our comparison was empirical, using IPD from 13 studies that evaluated the diagnostic accuracy of the 9-item Patient Health Questionnaire depression screening tool, and included simulations. The empirical comparison showed that the implementation of the two multivariate methods is more laborious in terms of computational time and sensitivity to user-supplied values compared to the bivariate approach. Simulations showed that ignoring the within-study correlation of sensitivity and specificity across thresholds did not worsen inferences with the bivariate approach compared to the Poisson model. The ordinal approach was not suitable for simulations because the model was highly sensitive to user-supplied starting values. We tentatively recommend the bivariate approach rather than more complex multivariate methods for IPD diagnostic accuracy meta-analyses of ordinal scale tests, although the limited type of diagnostic data considered in the simulation study restricts the generalization of our findings.

Matched MeSH terms: Models, Statistical*
A hybrid data envelopment analysis and game theory model for performance measurement in healthcare

Zare H, Tavana M, Mardani A, Masoudian S, Kamali Saraji M

Health Care Manag Sci, 2019 Sep;22(3):475-488.
PMID: 30225622 DOI: 10.1007/s10729-018-9456-4

Performance measurement plays an important role in the successful design and reform of regional healthcare management systems. In this study, we propose a hybrid data envelopment analysis (DEA) and game theory model for measuring the performance and productivity in the healthcare centers. The input and output variables associated with the efficiency of the healthcare centers are identified by reviewing the relevant literature, and then used in conjunction with the internal organizational data. The selected indicators and collected data are then weighted and prioritized with the help of experts in the field. A case study is presented to demonstrate the applicability and efficacy of the proposed model. The results reveal useful information and insights on the efficiency levels of the regional healthcare centers in the case study.

Matched MeSH terms: Models, Statistical*
Fulltext Utilization of a Mixture Cure Rate Model based on the Generalized Modified Weibull Distribution for the Analysis of Leukemia Patients

Omer ME, Abu Bakar M, Adam M, Mustafa M

Asian Pac J Cancer Prev, 2021 Apr 01;22(4):1045-1053.
PMID: 33906295 DOI: 10.31557/APJCP.2021.22.4.1045

OBJECTIVE: Cure rate models are survival models, commonly applied to model survival data with a cured fraction. In the existence of a cure rate, if the distribution of survival times for susceptible patients is specified, researchers usually prefer cure models to parametric models. Different distributions can be assumed for the survival times, for instance, generalized modified Weibull (GMW), exponentiated Weibull (EW), and log-beta Weibull. The purpose of this study is to select the best distribution for uncured patients' survival times by comparing the mixture cure models based on the GMW distribution and its particular cases.
MATERIALS AND METHODS: A data set of 91 patients with high-risk acute lymphoblastic leukemia (ALL) followed for five years from 1982 to 1987 was chosen for fitting the mixture cure model. We used the maximum likelihood estimation technique via R software 3.6.2 to obtain the estimates for parameters of the proposed model in the existence of cure rate, censored data, and covariates. For the best model choice, the Akaike information criterion (AIC) was implemented.
RESULTS: After comparing different parametric models fitted to the data, including or excluding cure fraction, without covariates, the smallest AIC values were obtained by the EW and the GMW distributions, (953.31/969.35) and (955.84/975.99), respectively. Besides, assuming a mixture cure model based on GMW with covariates, an estimated ratio between cure fractions for allogeneic and autologous bone marrow transplant groups (and its 95% confidence intervals) were 1.42972 (95% CI: 1.18614 - 1.72955).
CONCLUSION: The results of this study reveal that the EW and the GMW distributions are the best choices for the survival times of Leukemia patients.
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Matched MeSH terms: Models, Statistical*
Reliability of reasons for early termination of breastfeeding: application of a bivariate probability model with sample selection to data from surveys in Malaysia in 1976-77 and 1988-89

Kuate-Defo B, DaVanzo J

Popul Stud (Camb), 2006 Mar;60(1):83-98.
PMID: 16464777 DOI: 10.1080/00324720500462223

Although extensively collected, data on people's reasons for their behaviour provided retrospectively have been met with some scepticism on the grounds that they may be subject to biases and errors that jeopardize their usefulness. This study investigates, for a sample of 1,327 births, the reliability with which women in Peninsular Malaysia recalled, at intervals 12 years apart, reasons for not initiating or for stopping breastfeeding less than 3 months after a birth. Overall, we find low to moderate reliability of recall. Levels of reliability are relatively high for some reasons (the child died and no or insufficient milk) but low for some others (child ill, breastfeeding inconvenient). Results from selection models show that reliability does not vary with the length of time since the child's birth but is inversely related to socio-economic status (proxied by education and employment). Social status, social norms, and health-related factors appear to be significant influences on women's consistency of reporting.

Matched MeSH terms: Models, Statistical*
Fulltext An online time-to-event dashboard comparing the effective control of COVID-19 among continents using the inflection point on an ogive curve: Observational study

Lee KW, Chien TW, Yeh YT, Chou W, Wang HY

Medicine (Baltimore), 2021 Mar 12;100(10):e24749.
PMID: 33725830 DOI: 10.1097/MD.0000000000024749

BACKGROUND: During the COVID-19 pandemic, one of the frequently asked questions is which countries (or continents) are severely hit. Aside from using the number of confirmed cases and the fatality to measure the impact caused by COVID-19, few adopted the inflection point (IP) to represent the control capability of COVID-19. How to determine the IP days related to the capability is still unclear. This study aims to (i) build a predictive model based on item response theory (IRT) to determine the IP for countries, and (ii) compare which countries (or continents) are hit most.
METHODS: We downloaded COVID-19 outbreak data of the number of confirmed cases in all countries as of October 19, 2020. The IRT-based predictive model was built to determine the pandemic IP for each country. A model building scheme was demonstrated to fit the number of cumulative infected cases. Model parameters were estimated using the Solver add-in tool in Microsoft Excel. The absolute advantage coefficient (AAC) was computed to track the IP at the minimum of incremental points on a given ogive curve. The time-to-event analysis (a.k.a. survival analysis) was performed to compare the difference in IPs among continents using the area under the curve (AUC) and the respective 95% confidence intervals (CIs). An online comparative dashboard was created on Google Maps to present the epidemic prediction for each country.
RESULTS: The top 3 countries that were hit severely by COVID-19 were France, Malaysia, and Nepal, with IP days at 263, 262, and 262, respectively. The top 3 continents that were hit most based on IP days were Europe, South America, and North America, with their AUCs and 95% CIs at 0.73 (0.61-0.86), 0.58 (0.31-0.84), and 0.54 (0.44-0.64), respectively. An online time-event result was demonstrated and shown on Google Maps, comparing the IP probabilities across continents.
CONCLUSION: An IRT modeling scheme fitting the epidemic data was used to predict the length of IP days. Europe, particularly France, was hit seriously by COVID-19 based on the IP days. The IRT model incorporated with AAC is recommended to determine the pandemic IP.

Matched MeSH terms: Models, Statistical*
Fulltext The adoption of cryptocurrency as a disruptive force: Deep learning-based dual stage structural equation modelling and artificial neural network analysis

Abbasi GA, Tiew LY, Tang J, Goh YN, Thurasamy R

PLoS One, 2021;16(3):e0247582.
PMID: 33684120 DOI: 10.1371/journal.pone.0247582

In recent years, the growth of cryptocurrency has undergone an enormous increase in cryptocurrency markets all around the world. Sadly, only insignificant heed has been paid to the unveiling of determinants of cryptocurrency adoption globally, particularly in emerging markets like Malaysia. The purpose of the study is to examine whether the application of deep learning-based dual-stage Partial Least Square-Structural Equation Modelling (PLS-SEM) & Artificial Neural Network (ANN) analysis enable better in-depth research results as compared to single-step PLS-SEM approach and to excavate factors which can predict behavioural intention to adopt cryptocurrency. The Unified Theory of Acceptance and Use of Technology 2 (UTAUT2) model were extended with the inclusion of trust and personnel innovativeness. The model was further validated by introducing a new path model compared to the original UTAUT2 model and the moderating role of personal innovativeness between performance expectancy and price value, with a sample of 314 respondents. Contrary to previous technology adoption studies that used PLS-SEM & ANN as single-stage analysis, this study further enhanced the analysis by applying a deep learning-based dual-stage PLS-SEM and ANN method. The application of deep learning-based dual-stage PLS-SEM & ANN analysis is a novel methodological approach, detecting both linear and non-linear associations among constructs. At the same time, it is regarded as a superior statistical approach as compared to traditional hybrid shallow SEM & ANN single-stage analysis. Also, sensitivity analysis provides normalised importance using multi-layer perceptron with the feed-forward-back-propagation algorithm. Furthermore, the deep learning-based dual-stage PLS-SEM & ANN revealed that trust proved to be the strongest predictor in driving user intention. The introduction of this new methodology and the theoretical contribution opens the vistas of the extant body of knowledge in technology-adoption related literature. This study also provides theoretical, practical and methodological contributions.

Matched MeSH terms: Models, Statistical*
Fulltext A preliminary investigation of user perception and behavioral intention for different review types: customers and designers perspective

Qazi A, Raj RG, Tahir M, Waheed M, Waheed M, Khan SU, et al.

ScientificWorldJournal, 2014;2014:872929.
PMID: 24711739 DOI: 10.1155/2014/872929

Existing opinion mining studies have focused on and explored only two types of reviews, that is, regular and comparative. There is a visible gap in determining the useful review types from customers and designers perspective. Based on Technology Acceptance Model (TAM) and statistical measures we examine users' perception about different review types and its effects in terms of behavioral intention towards using online review system. By using sample of users (N = 400) and designers (N = 106), current research work studies three review types, A (regular), B (comparative), and C (suggestive), which are related to perceived usefulness, perceived ease of use, and behavioral intention. The study reveals that positive perception of the use of suggestive reviews improves users' decision making in business intelligence. The results also depict that type C (suggestive reviews) could be considered a new useful review type in addition to other types, A and B.

Matched MeSH terms: Models, Statistical
Hybrid Harmony Search-Artificial Intelligence Models in Credit Scoring

Goh RY, Lee LS, Seow HV, Gopal K

Entropy (Basel), 2020 Sep 04;22(9).
PMID: 33286758 DOI: 10.3390/e22090989

Credit scoring is an important tool used by financial institutions to correctly identify defaulters and non-defaulters. Support Vector Machines (SVM) and Random Forest (RF) are the Artificial Intelligence techniques that have been attracting interest due to their flexibility to account for various data patterns. Both are black-box models which are sensitive to hyperparameter settings. Feature selection can be performed on SVM to enable explanation with the reduced features, whereas feature importance computed by RF can be used for model explanation. The benefits of accuracy and interpretation allow for significant improvement in the area of credit risk and credit scoring. This paper proposes the use of Harmony Search (HS), to form a hybrid HS-SVM to perform feature selection and hyperparameter tuning simultaneously, and a hybrid HS-RF to tune the hyperparameters. A Modified HS (MHS) is also proposed with the main objective to achieve comparable results as the standard HS with a shorter computational time. MHS consists of four main modifications in the standard HS: (i) Elitism selection during memory consideration instead of random selection, (ii) dynamic exploration and exploitation operators in place of the original static operators, (iii) a self-adjusted bandwidth operator, and (iv) inclusion of additional termination criteria to reach faster convergence. Along with parallel computing, MHS effectively reduces the computational time of the proposed hybrid models. The proposed hybrid models are compared with standard statistical models across three different datasets commonly used in credit scoring studies. The computational results show that MHS-RF is most robust in terms of model performance, model explainability and computational time.

Matched MeSH terms: Models, Statistical
Fulltext Handling overdispersion in mortality data in time-series epidemiologic reserch using SAS software

Wan Rozita, W.M., Rasimah, A., Mazrura, S., Lim, K.H., Thana, S.

Malaysian Journal of Public Health Medicine, 2010;10(2):6-15.
MyJurnal

Analysis of count event data such as mortality cases, were often modelled using Poisson regression model. Maximum likelihood procedures were used by using SAS software to estimate the model parameters of a Poisson regression model. However, the Negative Binomial distribution has been widely suggested as the alternative to the Poisson when there is proof of overdispersion phenomenon. We modelled the mortality cases as the dependent variable using Poisson and Negative Binomial regression and compare both of the models. The procedures were done in SAS by using the function PROC GENMOD. The results showed that the mortality data in Poisson regression exhibit large ratio values between deviance to degree of freedom which indicate model misspecification or overdispersion. This large ratio was found to be reduced in Negative Binomial regression. The Normal probability plot of Pearson residual confirmed that the Negative Binomial regression is a better model than Poisson regression in modelling the mortality data. The objective of this study is to compare the goodness of fit of Poisson regression model and Negative Binomial regression model in the application of air pollution epidemiologic time series study by using SAS software.

Matched MeSH terms: Models, Statistical
Fulltext Development of an edible coating based on chitosan-glycerol to delay ‘Berangan’ banana (Musa sapientum cv. Berangan) ripening process

Jafarizadeh Malmiri, H., Osman, A., Tan, C.P., Abdul Rahman, R.

International Food Research Journal, 2011;18(3):-.
MyJurnal

Response surface methodology (RSM) was used to optimize the concentrations of chitosan and glycerol for coating Berangan banana (Musa sapientum cv. Berangan). The effects of main edible coating components, chitosan (0.5-2.5%, w/w) and glycerol (0-2%, w/w) on weight loss, firmness, total colour difference, total soluble solids content (TSS) and titratable acidity (TA) of coated banana were studied during 10 days of storage at 26±2°C and 40-50% relative humidity. Results showed that the experimental data could be adequately fitted into a second-order polynomial model with coefficient of determination (R 2 ) ranging from 0.745 to 0.930 for all the variables studied. In general, the chitosan concentration appeared to be the most significant (P< 0.1) factor influencing all variables except for TSS. The optimum concentration of chitosan and glycerol were predicted to be 2.02% and 0.18%, respectively. Statistical assessment showed insignificant difference between experimental and predicted values.

Matched MeSH terms: Models, Statistical
Fulltext Application of statistical experimental designs for optimization of medium composition in biodegradation of procion red MX-8B

Khadijah, O., Lee, K.K., Abdullah, M.F.F.

ASM Science Journal, 2010;4(2):103-112.
MyJurnal

Two sequential statistical experimental designs were used to screen and investigate the dependence of the amount of biodegradation of Procion Red MX-8B (PR-MX8B) on the fermentation variables. Fourteen factors were screened using the Plackett-Burman design. Among these factors, the most significant variables which included yeast extract, corn steep solids and starch influencing PR-MX8B decolourisation were statistically elucidated for optimization. The optimum concentrations of 5.00 g/l yeast extract, 2.99 g/l starch and 1.89 g/l corn steep solids were predicted by applying the Box-Behnken design to the second order polynomial model fitted to the results obtained. The best predicted optimal conditions verified experimentally yielded 72.11% while the predicted value from the polynomial model was 79.17%. The experimental values were in good agreement with the predicted values with a 90.81% degree of accuracy.

Matched MeSH terms: Models, Statistical
Fulltext Sorption of so2 and no by modified palm shell activated carbon: breakthrough curve model

S. Bhatia, K. T. Lee, A. R. Mohamed, Sumathi, S.

Pertanika Journal of Science & Technology, 2014;22(1):307-314.
MyJurnal

Simultaneous removal of SO2 and NO from simulated flue gas by cerium oxide supported over palm shell activated carbon (Ce/PSAC) was studied in a fixed bed adsorber. In this study, the adsorption breakthrough of SO2 and NO on Ce/PSAC at different reaction temperatures was manipulated to test their applicability to a model developed by Yoon and Nelson (1984) for breakthrough curves. Yoon and Nelson (1984) developed a relatively simple model addressing the adsorption and breakthrough of adsorbate vapour with respect to activated charcoal. This model was based on the assumption that the rate of decrease in the probability of adsorption for each adsorbate molecule is proportional to the probability of adsorbate adsorption and the probability of adsorbate breakthrough on the adsorbent. A regression analysis (least square method) has been used to give the model parameters of k and t1/2. The results showed that the agreement between the model and the experimental results is satisfactory. From the observation, it is concluded that the simple two-parameter model of Yoon and Nelson’s model can be applied for modelling the breakthrough curves of SO2 and NO gas adsorption over Ce/PSAC.

Matched MeSH terms: Models, Statistical
Fulltext Removal of shells in palm kernel cake via static cling and electrostatic separation

Norhani Abdullah, Muhamad Akhmal Hakim Roslan, Shuhaimi Mustafa

Journal of Biochemistry, Microbiology and Biotechnology, 2015;3(1):1-6.
MyJurnal

Palm kernel cake (PKC), a by-product of the palm oil industry is limited in its use as a feed ingredient for poultry due to its high fibre and lignin content. The presence of these antinutritive components is the result of shells contaminating the by-product. The nutritive value of PKC has to be improved in order to increase its inclusion rate in poultry diet. In this study, PKC was subjected to a separation method using static cling and electrostatic separation to removethe shells present in PKC. Response surface methodology based on Box-Behnken design was used to optimize the separation method with moisture content (8 to 18%), particle size distribution (0.5 to 2.5 mm) and feed rate (20 to 200 g/min) as the independent variables evaluated. According to the regression coefficients and significance of the quadratic polynomial model, the optimum separation parameters were as follows: 13% PKC moisture content;

Matched MeSH terms: Models, Statistical
Fulltext Land-use change alters the mechanisms assembling rainforest mammal communities in Borneo

Wearn OR, Carbone C, Rowcliffe JM, Pfeifer M, Bernard H, Ewers RM

J Anim Ecol, 2019 01;88(1):125-137.
PMID: 30178485 DOI: 10.1111/1365-2656.12903

The assembly of species communities at local scales is thought to be driven by environmental filtering, species interactions and spatial processes such as dispersal limitation. Little is known about how the relative balance of these drivers of community assembly changes along environmental gradients, especially man-made environmental gradients associated with land-use change. Using concurrent camera- and live-trapping, we investigated the local-scale assembly of mammal communities along a gradient of land-use intensity (old-growth forest, logged forest and oil palm plantations) in Borneo. We hypothesised that increasing land-use intensity would lead to an increasing dominance of environmental control over spatial processes in community assembly. Additionally, we hypothesised that competitive interactions among species might reduce in concert with declines in α-diversity (previously documented) along the land-use gradient. To test our first hypothesis, we partitioned community variance into the fractions explained by environmental and spatial variables. To test our second hypothesis, we used probabilistic models of expected species co-occurrence patterns, in particular focussing on the prevalence of spatial avoidance between species. Spatial avoidance might indicate competition, but might also be due to divergent habitat preferences. We found patterns that are consistent with a shift in the fundamental mechanics governing local community assembly. In support of our first hypothesis, the importance of spatial processes (dispersal limitation and fine-scale patterns of home-ranging) appeared to decrease from low to high intensity land-uses, whilst environmental control increased in importance (in particular due to fine-scale habitat structure). Support for our second hypothesis was weak: whilst we found that the prevalence of spatial avoidance decreased along the land-use gradient, in particular between congeneric species pairs most likely to be in competition, few instances of spatial avoidance were detected in any land-use, and most were likely due to divergent habitat preferences. The widespread changes in land-use occurring in the tropics might be altering not just the biodiversity found in landscapes, but also the fundamental mechanics governing the local assembly of communities. A better understanding of these mechanics, for a range of taxa, could underpin more effective conservation and management of threatened tropical landscapes.

Matched MeSH terms: Models, Statistical
A unit root test based on the modified least squares estimator

Wararit Panichkitkosolkul

Sains Malaysiana, 2014;43:1623-1633.

A unit root test based on the modified least squares (MLS) estimator for first-order autoregressive process is proposed and compared with unit root tests based on the ordinary least squares (OLS), the weighted symmetric (WS) and the modified weighted symmetric (MWS) estimators. The percentiles of the null distributions of the unit root test are also reported. The empirical probabilities of type I error and powers of the unit root tests were estimated via Monte Carlo simulation. The simulation results showed that all unit root tests can control the probability of type I error for all situations. The empirical power of the test is higher than the other unit root tests, and Apart from that, the and tests also provide the highest empirical power. As an illustration, the monthly series of U.S. nominal interest rates on three-month treasury bills is analyzed.

Matched MeSH terms: Models, Statistical
Fulltext Economic-statistical design of synthetic np chart with estimated process parameter

Lee MH, Khoo MBC, Chew X, Then PHH

PLoS One, 2020;15(4):e0230994.
PMID: 32267874 DOI: 10.1371/journal.pone.0230994

The economic-statistical design of the synthetic np chart with estimated process parameter is presented in this study. The effect of process parameter estimation on the expected cost of the synthetic np chart is investigated with the imposed statistical constraints. The minimum number of preliminary subgroups is determined where an almost similar expected cost to the known process parameter case is desired for the given cost model parameters. However, the available number of preliminary subgroups in practice is usually limited, especially when the number of preliminary subgroups is large. Consequently, the optimal chart parameters of the synthetic np chart are computed by considering the practical number of preliminary subgroups in which the cost function is minimized. This leads to a lower expected cost compared to that of adopting the optimal chart parameter corresponding to the known process parameter case.

Matched MeSH terms: Models, Statistical
Modelling for determination of fe uptake by brassica chinensis jusl var. Parachinensi (bailey) tsen & lee (flowering white cabbage)

Ahmad Mahir R, Arfah A, Rozaimah Z, Siti Adyani S, Khairiah J, Ismail B

Sains Malaysiana, 2017;46:2305-2313.

The study was conducted to determine the best model suitable for the determination of ferrum uptake in Brassica chinensis (flowering white cabbage). A nonlinear regression model was selected to determine the amount of ferrum absorbed by each part of the Brassica chinensis plant namely the leaves, stems and roots. The Levenberg-Marquardt method was used to perform the nonlinear least square fit. This method employs information on the gradients and hence requires specification of the partial derivatives. A suitable model was obtained from the exponential regression model. The polynomial model was found to be appropriate for leaves, the mono-exponential model was suitable for stems and the simple exponential model for roots. The residual plots and the normal probability plots from each of the models indicated no substantial diagnostic problems, so it can be concluded that the polynomial and exponential regression models provide adequate fit to determine data on heavy metal uptake by the flowering white cabbage.

Matched MeSH terms: Models, Statistical
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.

Matched MeSH terms: Models, Statistical
Fulltext Combination of forecasts with an application to unemployment rate

Muniroh, M.F., Ismail, N., Lazim, M.A.

Pertanika Journal of Science & Technology, 2017;25(3):787-796.
MyJurnal

Combining forecast values based on simple univariate models may produce more favourable results than complex models. In this study, the results of combining the forecast values of Naïve model, Single Exponential Smoothing Model, The Autoregressive Moving Average (ARIMA) model, and Holt Method are shown to be superior to that of the Error Correction Model (ECM).Malaysia’s unemployment rates data are used in this study. The independent variable used in the ECM formulation is the industrial production index. Both data sets were collected for the months of January 2004 to December 2010. The selection criteria used to determine the best model, is the Mean Square Error (MSE), Root Mean Squared Error (RMSE) and Mean Absolute Percentage Error (MAPE). Initial findings showed that both time series data sets were not influenced by the seasonality effect.

Matched MeSH terms: Models, Statistical

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