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Manabe H, Manabe T, Honda Y, Kawade Y, Kambayashi D, Manabe Y, Kudo K. Simple mathematical model for predicting COVID-19 outbreaks in Japan based on epidemic waves with a cyclical trend. BMC Infect Dis 2024; 24:465. [PMID: 38724890 PMCID: PMC11080248 DOI: 10.1186/s12879-024-09354-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/30/2023] [Accepted: 04/26/2024] [Indexed: 05/13/2024] Open
Abstract
BACKGROUND Several models have been used to predict outbreaks during the COVID-19 pandemic, with limited success. We developed a simple mathematical model to accurately predict future epidemic waves. METHODS We used data from the Ministry of Health, Labour and Welfare of Japan for newly confirmed COVID-19 cases. COVID-19 case data were summarized as weekly data, and epidemic waves were visualized and identified. The periodicity of COVID-19 in each prefecture of Japan was confirmed using time-series analysis and the autocorrelation coefficient, which was used to investigate the longer-term pattern of COVID-19 cases. Outcomes using the autocorrelation coefficient were visualized via a correlogram to capture the periodicity of the data. An algorithm for a simple prediction model of the seventh COVID-19 wave in Japan comprised three steps. Step 1: machine learning techniques were used to depict the regression lines for each epidemic wave, denoting the "rising trend line"; Step 2: an exponential function with good fit was identified from data of rising straight lines up to the sixth wave, and the timing of the rise of the seventh wave and speed of its spread were calculated; Step 3: a logistic function was created using the values calculated in Step 2 as coefficients to predict the seventh wave. The accuracy of the model in predicting the seventh wave was confirmed using data up to the sixth wave. RESULTS Up to March 31, 2023, the correlation coefficient value was approximately 0.5, indicating significant periodicity. The spread of COVID-19 in Japan was repeated in a cycle of approximately 140 days. Although there was a slight lag in the starting and peak times in our predicted seventh wave compared with the actual epidemic, our developed prediction model had a fairly high degree of accuracy. CONCLUSION Our newly developed prediction model based on the rising trend line could predict COVID-19 outbreaks up to a few months in advance with high accuracy. The findings of the present study warrant further investigation regarding application to emerging infectious diseases other than COVID-19 in which the epidemic wave has high periodicity.
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Affiliation(s)
- Hiroki Manabe
- Shitennoji University, 3-2-1 Gakuenmae, Habikino City, 583-8501, Osaka, Japan.
| | - Toshie Manabe
- Nagoya City University School of Data Science, Nagoya City, Aichi, Japan
- Nagoya City University Graduate School of Medicine, Nagoya City, Aichi, Japan
| | - Yuki Honda
- Shitennoji University, 3-2-1 Gakuenmae, Habikino City, 583-8501, Osaka, Japan
| | - Yoshihiro Kawade
- Nagoya City University Graduate School of Medicine, Nagoya City, Aichi, Japan
| | - Dan Kambayashi
- Nagoya City University Graduate School of Medicine, Nagoya City, Aichi, Japan
- Showa Pharmaceutical University, Machida, Tokyo, Japan
| | - Yoshiki Manabe
- Tokyo University Graduate School of Engineering, Tokyo, Japan
| | - Koichiro Kudo
- Waseda University Organization Regional and inter-regional Studies, Tokyo, Japan
- Kawakita General Hospital, Tokyo, Japan
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Rodríguez-Fernández R, Fernández-Gómez Á, Mejuto JC, Astray G. Modelling Polyphenol Extraction through Ultrasound-Assisted Extraction by Machine Learning in Olea europaea Leaves. Foods 2023; 12:4483. [PMID: 38137287 PMCID: PMC10742609 DOI: 10.3390/foods12244483] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/07/2023] [Revised: 12/05/2023] [Accepted: 12/11/2023] [Indexed: 12/24/2023] Open
Abstract
The study of the phenolic compounds present in olive leaves (Olea europaea) is of great interest due to their health benefits. In this research, different machine learning algorithms such as RF, SVM, and ANN, with temperature, time, and volume as input variables, were developed to model the extract yield and the total phenolic content (TPC) from experimental data reported in the literature. In terms of extract yield, the neural network-based ANNZ-L model presents the lowest root mean square error (RMSE) value in the validation phase (9.44 mg/g DL), which corresponds with a mean absolute percentage error (MAPE) of 3.7%. On the other hand, the best model to determine the TPC value was the neural network-based model ANNR, with an RMSE of 0.89 mg GAE/g DL in the validation phase (MAPE of 2.9%). Both models obtain, for the test phase, MAPE values of 4.9 and 3.5%, respectively. This affirms that ANN models would be good modelling tools to determine the extract yield and TPC value of the ultrasound-assisted extraction (UAE) process of olive leaves under different temperatures, times, and solvents.
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Affiliation(s)
| | | | | | - Gonzalo Astray
- Universidade de Vigo, Departamento de Química Física, Facultade de Ciencias, 32004 Ourense, Spain; (R.R.-F.); (Á.F.-G.); (J.C.M.)
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Nordin NI, Mustafa WA, Lola MS, Madi EN, Kamil AA, Nasution MD, K. Abdul Hamid AA, Zainuddin NH, Aruchunan E, Abdullah MT. Enhancing COVID-19 Classification Accuracy with a Hybrid SVM-LR Model. Bioengineering (Basel) 2023; 10:1318. [PMID: 38002441 PMCID: PMC10669812 DOI: 10.3390/bioengineering10111318] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/25/2023] [Revised: 10/03/2023] [Accepted: 10/09/2023] [Indexed: 11/26/2023] Open
Abstract
Support ector achine (SVM) is a newer machine learning algorithm for classification, while logistic regression (LR) is an older statistical classification method. Despite the numerous studies contrasting SVM and LR, new improvements such as bagging and ensemble have been applied to them since these comparisons were made. This study proposes a new hybrid model based on SVM and LR for predicting small events per variable (EPV). The performance of the hybrid, SVM, and LR models with different EPV values was evaluated using COVID-19 data from December 2019 to May 2020 provided by the WHO. The study found that the hybrid model had better classification performance than SVM and LR in terms of accuracy, mean squared error (MSE), and root mean squared error (RMSE) for different EPV values. This hybrid model is particularly important for medical authorities and practitioners working in the face of future pandemics.
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Affiliation(s)
- Noor Ilanie Nordin
- Faculty of Ocean Engineering Technology and Informatics, Universiti Malaysia Terengganu, Kuala Nerus 21030, Terengganu, Malaysia or (N.I.N.); (A.A.K.A.H.)
- Faculty of Computer and Mathematical Sciences, Universiti Teknologi MARA Kelantan, Bukit Ilmu, Machang 18500, Kelantan, Malaysia
| | - Wan Azani Mustafa
- Faculty of Electrical Engineering & Technology, Pauh Putra Campus, Universiti Malaysia Perlis (UniMAP), Arau 02600, Perlis, Malaysia
- Centre of Excellence for Advanced Computing, Pauh Putra Campus, Universiti Malaysia Perlis (UniMAP), Arau 02600, Perlis, Malaysia
| | - Muhamad Safiih Lola
- Faculty of Ocean Engineering Technology and Informatics, Universiti Malaysia Terengganu, Kuala Nerus 21030, Terengganu, Malaysia or (N.I.N.); (A.A.K.A.H.)
- Special Interest Group on Modeling and Data Analytics (SIGMDA), Universiti Malaysia Terengganu, Kuala Nerus 21030, Terengganu, Malaysia
| | - Elissa Nadia Madi
- Faculty of Informatics and Computing, Universiti Sultan Zainal Abidin (UniSZA), Besut Campus, Besut 22200, Terengganu, Malaysia;
| | - Anton Abdulbasah Kamil
- Faculty of Economics, Administrative and Social Sciences, Istanbul Gelisim University, Cihangir Mah. Şehit Jandarma Komando Er Hakan Öner Sk. No:1 Avcılar, İstanbul 34310, Turkey;
| | - Marah Doly Nasution
- Faculty of Teacher and Education, University Muhammadiyah Sumatera Utara, Jl. Kapten Muchtar Basri No.3, Glugur Darat II, Kec. Medan Tim., Kota Medan 20238, Sumatera Utara, Indonesia;
| | - Abdul Aziz K. Abdul Hamid
- Faculty of Ocean Engineering Technology and Informatics, Universiti Malaysia Terengganu, Kuala Nerus 21030, Terengganu, Malaysia or (N.I.N.); (A.A.K.A.H.)
- Special Interest Group on Applied Informatics and Intelligent Applications (AINIA), Universiti Malaysia Terengganu, Kuala Nerus 21030, Terengganu, Malaysia
| | - Nurul Hila Zainuddin
- Mathematics Department, Faculty of Science and Mathematics, Universiti Pendidikan Sultan Idris, Tanjong Malim 53900, Perak Darul Ridzuan, Malaysia;
| | - Elayaraja Aruchunan
- Department of Decision Science, Faculty of Business and Economics, University Malaya, Kuala Lumpur 50603, Malaysia;
| | - Mohd Tajuddin Abdullah
- Fellow Academy of Sciences Malaysia, Level 20, West Wing Tingkat 20, Menara MATRADE, Jalan Sultan Haji Ahmad Shah, Kuala Lumpur 50480, Malaysia;
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Narasimhan G, Victor A. Analysis of computational intelligence approaches for predicting disease severity in humans: Challenges and research guidelines. JOURNAL OF EDUCATION AND HEALTH PROMOTION 2023; 12:334. [PMID: 38023081 PMCID: PMC10671019 DOI: 10.4103/jehp.jehp_298_23] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/02/2023] [Accepted: 04/12/2023] [Indexed: 12/01/2023]
Abstract
The word disease is a common word and there are many diseases like heart disease, diabetes, breast cancer, COVID-19, and kidney disease that threaten humans. Data-mining methods are proving to be increasingly beneficial in the present day, especially in the field of medical applications; through the use of machine-learning methods, that are used to extract valuable information from healthcare data, which can then be used to predict and treat diseases early, reducing the risk of human life. Machine-learning techniques are useful especially in the field of health care in extracting information from healthcare data. These data are very much helpful in predicting the disease early and treating the patients to reduce the risk of human life. For classification and decision-making, data mining is very much suitable. In this paper, a comprehensive study on several diseases and diverse machine-learning approaches that are functional to predict those diseases and also the different datasets used in prediction and making decisions are discussed in detail. The drawbacks of the models from various research papers have been observed and reveal countless computational intelligence approaches. Naïve Bayes, logistic regression (LR), SVM, and random forest are able to produce the best accuracy. With further optimization algorithms like genetic algorithm, particle swarm optimization, and ant colony optimization combined with machine learning, better performance can be achieved in terms of accuracy, specificity, precision, recall, and specificity.
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Affiliation(s)
- Geetha Narasimhan
- School of Computer Science and Engineering, Vellore Institute of Technology, Vellore, Tamil Nadu, India
| | - Akila Victor
- School of Computer Science and Engineering, Vellore Institute of Technology, Vellore, Tamil Nadu, India
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Feng Y, Park J. Using machine learning-based binary classifiers for predicting organizational members' user satisfaction with collaboration software. PeerJ Comput Sci 2023; 9:e1481. [PMID: 37547399 PMCID: PMC10403168 DOI: 10.7717/peerj-cs.1481] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/13/2022] [Accepted: 06/14/2023] [Indexed: 08/08/2023]
Abstract
Background In today's digital economy, enterprises are adopting collaboration software to facilitate digital transformation. However, if employees are not satisfied with the collaboration software, it can hinder enterprises from achieving the expected benefits. Although existing literature has contributed to user satisfaction after the introduction of collaboration software, there are gaps in predicting user satisfaction before its implementation. To address this gap, this study offers a machine learning-based forecasting method. Methods We utilized national public data provided by the national information society agency of South Korea. To enable the data to be used in a machine learning-based binary classifier, we discretized the predictor variable. We then validated the effectiveness of our prediction model by calculating feature importance scores and prediction accuracy. Results We identified 10 key factors that can predict user satisfaction. Furthermore, our analysis indicated that the naive Bayes (NB) classifier achieved the highest prediction accuracy rate of 0.780, followed by logistic regression (LR) at 0.767, extreme gradient boosting (XGBoost) at 0.744, support vector machine (SVM) at 0.744, K-nearest neighbor (KNN) at 0.707, and decision tree (DT) at 0.637. Conclusions This research identifies essential indicators that can predict user satisfaction with collaboration software across four levels: institutional guidance, information and communication technology (ICT) environment, company culture, and demographics. Enterprises can use this information to evaluate their current collaboration status and develop strategies for introducing collaboration software. Furthermore, this study presents a novel approach to predicting user satisfaction and confirm the effectiveness of the machine learning-based prediction method proposed in this study, adding to the existing knowledge on the subject.
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Affiliation(s)
- Yituo Feng
- Management Information System, Chungbuk National University, Cheongju, South Korea
| | - Jungryeol Park
- Technology Policy Research Division, Electronics and Telecommunications Research Institute, Daejeon, South Korea
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Aamir M, Khan N, Naeem M, Bilal M, Khan F, Abdullah S. Implications of the COVID-19 pandemic on the shanghai, New York, and Pakistan stock exchanges. Heliyon 2023; 9:e17525. [PMID: 37456005 PMCID: PMC10292918 DOI: 10.1016/j.heliyon.2023.e17525] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/22/2022] [Revised: 06/19/2023] [Accepted: 06/20/2023] [Indexed: 07/18/2023] Open
Abstract
This research aims to determine the impact of COVID-19 on the stock markets of Pakistan (Islamabad), China (Shanghai), and the United States of America (New York). These three stock markets were chosen to demonstrate the variation in the degree of influence based on varied times in which the respective nations were impacted by COVID-19. COVID-19, a pandemic virus, was still present in China in December 2020. The one-year timeline helps us understand the pattern of the effect on different stock markets that show onward to guide us to indicate that in this situation, the lack of economic movement (due to the lockdown) had a more negative effect on stock prices than the increase in the number of new confirmed cases of the COVID-19 virus. This study was carried out to assess the influence of COVID-19 on the financial sectors, including the stock market. The effects were assessed by employing the Autoregressive Distributed Lag Model (ARDL) to demonstrate correlations between three stock markets (Pakistan, Shanghai, and New York) and COVID-19 instances. The study's major goal is to demonstrate the differences in the three countries' levels of influence. We got empirical results and discovered that the confirmed cases had a detrimental influence on three stock exchanges. However, all three countries saw an increase in the number of recovery cases. The number of deaths was minor for Pakistan and China but had a detrimental impact on the New York Stock Exchange.
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Affiliation(s)
- Muhammad Aamir
- Department of Statistics, Abdul Wali Khan University Mardan, Pakistan
| | - Nazeem Khan
- Department of Statistics, Abdul Wali Khan University Mardan, Pakistan
| | - Muhammad Naeem
- Department of Statistics, Abdul Wali Khan University Mardan, Pakistan
| | - Muhammad Bilal
- Department of Statistics, Abdul Wali Khan University Mardan, Pakistan
- Department of Mathematical Sciences, Balochistan University of Information Technology, Engineering and Management Sciences (BUITEMS), Quetta, Pakistan
| | - Faisal Khan
- Department of Electrical and Electronic Engineering, College of Science and Engineering, National University of Ireland Galway, Ireland
| | - Saleem Abdullah
- Department of Mathematics, Abdul Wali Khan University Mardan, Pakistan
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K Abdul Hamid AA, Wan Mohamad Nawi WIA, Lola MS, Mustafa WA, Abdul Malik SM, Zakaria S, Aruchunan E, Zainuddin NH, Gobithaasan R, Abdullah MT. Improvement of Time Forecasting Models Using Machine Learning for Future Pandemic Applications Based on COVID-19 Data 2020–2022. Diagnostics (Basel) 2023; 13:diagnostics13061121. [PMID: 36980429 PMCID: PMC10047172 DOI: 10.3390/diagnostics13061121] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/22/2023] [Revised: 02/17/2023] [Accepted: 02/20/2023] [Indexed: 03/18/2023] Open
Abstract
Improving forecasts, particularly the accuracy, efficiency, and precision of time-series forecasts, is becoming critical for authorities to predict, monitor, and prevent the spread of the Coronavirus disease. However, the results obtained from the predictive models are imprecise and inefficient because the dataset contains linear and non-linear patterns, respectively. Linear models such as autoregressive integrated moving average cannot be used effectively to predict complex time series, so nonlinear approaches are better suited for such a purpose. Therefore, to achieve a more accurate and efficient predictive value of COVID-19 that is closer to the true value of COVID-19, a hybrid approach was implemented. Therefore, the objectives of this study are twofold. The first objective is to propose intelligence-based prediction methods to achieve better prediction results called autoregressive integrated moving average–least-squares support vector machine. The second objective is to investigate the performance of these proposed models by comparing them with the autoregressive integrated moving average, support vector machine, least-squares support vector machine, and autoregressive integrated moving average–support vector machine. Our investigation is based on three COVID-19 real datasets, i.e., daily new cases data, daily new death cases data, and daily new recovered cases data. Then, statistical measures such as mean square error, root mean square error, mean absolute error, and mean absolute percentage error were performed to verify that the proposed models are better than the autoregressive integrated moving average, support vector machine model, least-squares support vector machine, and autoregressive integrated moving average–support vector machine. Empirical results using three recent datasets of known the Coronavirus Disease-19 cases in Malaysia show that the proposed model generates the smallest mean square error, root mean square error, mean absolute error, and mean absolute percentage error values for training and testing datasets compared to the autoregressive integrated moving average, support vector machine, least-squares support vector machine, and autoregressive integrated moving average–support vector machine models. This means that the predicted value of the proposed model is closer to the true value. These results demonstrate that the proposed model can generate estimates more accurately and efficiently. Compared to the autoregressive integrated moving average, support vector machine, least-squares support vector machine, and autoregressive integrated moving average–support vector machine models, our proposed models perform much better in terms of percent error reduction for both training and testing all datasets. Therefore, the proposed model is possibly the most efficient and effective way to improve prediction for future pandemic performance with a higher level of accuracy and efficiency.
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Affiliation(s)
- Abdul Aziz K Abdul Hamid
- Faculty of Ocean Engineering Technology and Informatics, Universiti Malaysia Terengganu, Kuala Nerus 21030, Terengganu, Malaysia
- Special Interest Group on Applied Informatics and Intelligent Applications (AINIA), Universiti Malaysia Terengganu, Kuala Nerus 21030, Terengganu, Malaysia
| | | | - Muhamad Safiih Lola
- Faculty of Ocean Engineering Technology and Informatics, Universiti Malaysia Terengganu, Kuala Nerus 21030, Terengganu, Malaysia
- Special Interest Group on Modeling and Data Analytics (SIGMDA), Universiti Malaysia Terengganu, Kuala Nerus 21030, Terengganu, Malaysia
- Correspondence: (M.S.L.); (W.A.M.)
| | - Wan Azani Mustafa
- Faculty of Electronic Engineering & Technology, Pauh Putra Campus, Universiti Malaysia Perlis (UniMAP), Arau 02600, Perlis, Malaysia
- Centre of Excellence for Advanced Computing, Pauh Putra Campus, Universiti Malaysia Perlis (UniMAP), Arau 02600, Perlis, Malaysia
- Correspondence: (M.S.L.); (W.A.M.)
| | - Siti Madhihah Abdul Malik
- Faculty of Ocean Engineering Technology and Informatics, Universiti Malaysia Terengganu, Kuala Nerus 21030, Terengganu, Malaysia
| | - Syerrina Zakaria
- Faculty of Ocean Engineering Technology and Informatics, Universiti Malaysia Terengganu, Kuala Nerus 21030, Terengganu, Malaysia
| | - Elayaraja Aruchunan
- Faculty of Science, Institute of Mathematical Sciences, Universiti Malaya, Kuala Lumpur 50603, Malaysia
| | - Nurul Hila Zainuddin
- Mathematics Department, Faculty of Science and Mathematics, Universiti Pendidikan Sultan Idris, Tanjong Malim 53900, Perak Darul Ridzuan, Malaysia
| | - R.U. Gobithaasan
- Faculty of Ocean Engineering Technology and Informatics, Universiti Malaysia Terengganu, Kuala Nerus 21030, Terengganu, Malaysia
- Special Interest Group on Modeling and Data Analytics (SIGMDA), Universiti Malaysia Terengganu, Kuala Nerus 21030, Terengganu, Malaysia
| | - Mohd Tajuddin Abdullah
- Faculty of Fisheries and Food Science, Universiti Malaysia Terengganu, Kuala Nerus 21030, Terengganu, Malaysia
- Fellow Academy of Sciences Malaysia, Level 20, West Wing Tingkat 20, Menara MATRADE, Jalan Sultan Haji Ahmad Shah, Kuala Lumpur 50480, Malaysia
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McClymont H, Si X, Hu W. Using weather factors and google data to predict COVID-19 transmission in Melbourne, Australia: A time-series predictive model. Heliyon 2023; 9:e13782. [PMID: 36845036 PMCID: PMC9941072 DOI: 10.1016/j.heliyon.2023.e13782] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/05/2022] [Revised: 02/09/2023] [Accepted: 02/10/2023] [Indexed: 02/23/2023] Open
Abstract
Background Forecast models have been essential in understanding COVID-19 transmission and guiding public health responses throughout the pandemic. This study aims to assess the effect of weather variability and Google data on COVID-19 transmission and develop multivariable time series AutoRegressive Integrated Moving Average (ARIMA) models for improving traditional predictive modelling for informing public health policy. Methods COVID-19 case notifications, meteorological factors and Google data were collected over the B.1.617.2 (Delta) outbreak in Melbourne, Australia from August to November 2021. Timeseries cross-correlation (TSCC) was used to evaluate the temporal correlation between weather factors, Google search trends, Google Mobility data and COVID-19 transmission. Multivariable time series ARIMA models were fitted to forecast COVID-19 incidence and Effective Reproductive Number (R eff ) in the Greater Melbourne region. Five models were fitted to compare and validate predictive models using moving three-day ahead forecasts to test the predictive accuracy for both COVID-19 incidence and R eff over the Melbourne Delta outbreak. Results Case-only ARIMA model resulted in an R squared (R2) value of 0.942, Root Mean Square Error (RMSE) of 141.59, and Mean Absolute Percentage Error (MAPE) of 23.19. The model including transit station mobility (TSM) and maximum temperature (Tmax) had greater predictive accuracy with R2 0.948, RMSE 137.57, and MAPE 21.26. Conclusion Multivariable ARIMA modelling for COVID-19 cases and R eff was useful for predicting epidemic growth, with higher predictive accuracy for models including TSM and Tmax. These results suggest that TSM and Tmax would be useful for further exploration for developing weather-informed early warning models for future COVID-19 outbreaks with potential application for the inclusion of weather and Google data with disease surveillance in developing effective early warning systems for informing public health policy and epidemic response.
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Soft computing techniques for forecasting of COVID-19 in Pakistan. ALEXANDRIA ENGINEERING JOURNAL 2023; 63:45-56. [PMCID: PMC9357447 DOI: 10.1016/j.aej.2022.07.029] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/19/2022] [Revised: 07/16/2022] [Accepted: 07/18/2022] [Indexed: 12/01/2023]
Abstract
Novel Pandemic COVID-19 led globally to severe health barriers and financial issues in different parts of the world. The forecast on COVID-19 infections is significant. Demeanor vital data will help in executing policies to reduce the number of cases efficiently. Filtering techniques are appropriate for dynamic model structures as it provide reasonable estimates over the recursive Bayesian updates. Kalman Filters, used for controlling epidemics, are valuable in knowing contagious infections. Artificial Neural Networks (ANN) have generally been used for classification and forecasting problems. ANN models show an essential role in several successful applications of neural networks and are commonly used in economic and business studies. Long short-term memory (LSTM) model is one of the most popular technique used in time series analysis. This paper aims to forecast COVID-19 on the basis of ANN, KF, LSTM and SVM methods. We applied ANN, KF, LSTM and SVM for the COVID-19 data in Pakistan to find the number of deaths, confirm cases, and cases of recovery. The three methods were used for prediction, and the results showed the performance of LSTM to be better than that of ANN and KF method. ANN, KF, LSTM and SVM endorsed the COVID-19 data in closely all three scenarios. LSTM, ANN and KF followed the fluctuations of the original data and made close COVID-19 predictions. The results of the three methods helped significantly in the decision-making direction for short term strategies and in the control of the COVID-19 outbreak.
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Wan Mohamad Nawi WIA, K Abdul Hamid AA, Lola MS, Zakaria S, Aruchunan E, Gobithaasan RU, Zainuddin NH, Mustafa WA, Abdullah ML, Mokhtar NA, Abdullah MT. Developing forecasting model for future pandemic applications based on COVID-19 data 2020-2022. PLoS One 2023; 18:e0285407. [PMID: 37172040 PMCID: PMC10180663 DOI: 10.1371/journal.pone.0285407] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/28/2022] [Accepted: 04/13/2023] [Indexed: 05/14/2023] Open
Abstract
Improving forecasting particularly time series forecasting accuracy, efficiency and precisely become crucial for the authorities to forecast, monitor, and prevent the COVID-19 cases so that its spread can be controlled more effectively. However, the results obtained from prediction models are inaccurate, imprecise as well as inefficient due to linear and non-linear patterns exist in the data set, respectively. Therefore, to produce more accurate and efficient COVID-19 prediction value that is closer to the true COVID-19 value, a hybrid approach has been implemented. Thus, aims of this study is (1) to propose a hybrid ARIMA-SVM model to produce better forecasting results. (2) to investigate in terms of the performance of the proposed models and percentage improvement against ARIMA and SVM models. statistical measurements such as MSE, RMSE, MAE, and MAPE then conducted to verify that the proposed models are better than ARIMA and SVM models. Empirical results with three real datasets of well-known cases of COVID-19 in Malaysia show that, compared to the ARIMA and SVM models, the proposed model generates the smallest MSE, RMSE, MAE and MAPE values for the training and testing datasets, means that the predicted value from the proposed model is closer to the actual value. These results prove that the proposed model can generate estimated values more accurately and efficiently. As compared to ARIMA and SVM, our proposed models perform much better in terms of error reduction percentages for all datasets. This is demonstrated by the maximum scores of 73.12%, 74.6%, 90.38%, and 68.99% in the MAE, MAPE, MSE, and RMSE, respectively. Therefore, the proposed model can be the best and effective way to improve prediction performance with a higher level of accuracy and efficiency in predicting cases of COVID-19.
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Affiliation(s)
| | - Abdul Aziz K Abdul Hamid
- Faculty of Ocean Engineering Technology and Informatics, Universiti Malaysia Terengganu, Kuala Nerus, Terengganu, Malaysia
- Special Interest Group on Applied Informatics and Intelligent Applications (AINIA) Universiti Malaysia Terengganu, Kuala Nerus, Terengganu, Malaysia
| | - Muhamad Safiih Lola
- Faculty of Ocean Engineering Technology and Informatics, Universiti Malaysia Terengganu, Kuala Nerus, Terengganu, Malaysia
- Special Interest Group on Modeling and Data Analytics (SIGMDA), Universiti Malaysia Terengganu, Kuala Nerus, Terengganu, Malaysia
| | - Syerrina Zakaria
- Faculty of Ocean Engineering Technology and Informatics, Universiti Malaysia Terengganu, Kuala Nerus, Terengganu, Malaysia
- Special Interest Group on Modeling and Data Analytics (SIGMDA), Universiti Malaysia Terengganu, Kuala Nerus, Terengganu, Malaysia
| | - Elayaraja Aruchunan
- Faculty of Science, Institute of Mathematical Sciences, Universiti Malaya, Kuala Lumpur, Kuala Lumpur Federal Territory, Malaysia
| | - R U Gobithaasan
- Faculty of Ocean Engineering Technology and Informatics, Universiti Malaysia Terengganu, Kuala Nerus, Terengganu, Malaysia
- Special Interest Group on Modeling and Data Analytics (SIGMDA), Universiti Malaysia Terengganu, Kuala Nerus, Terengganu, Malaysia
| | - Nurul Hila Zainuddin
- Mathematics Department, Faculty of Science and Mathematics, Universiti Pendidikan Sultan Idris, Tanjong Malim, Perak Darul Ridzuan, Malaysia
| | - Wan Azani Mustafa
- Faculty of Electrical Engineering & Technology, Universiti Malaysia Perlis, UniCITI Alam Campus, Sungai Chuchuh, Padang Besar, Perlis, Malaysia
- Advanced Computing (AdvCOMP), Centre of Excellence, Universiti Malaysia Perlis (UniMAP), Arau, Perlis, Malaysia
| | - Mohd Lazim Abdullah
- Faculty of Ocean Engineering Technology and Informatics, Universiti Malaysia Terengganu, Kuala Nerus, Terengganu, Malaysia
- Special Interest Group on Modeling and Data Analytics (SIGMDA), Universiti Malaysia Terengganu, Kuala Nerus, Terengganu, Malaysia
| | - Nor Aieni Mokhtar
- Institute of Oceanography and Environment, Universiti Malaysia Terengganu, Kuala Nerus, Terengganu, Malaysia
| | - Mohd Tajuddin Abdullah
- Faculty of Fisheries and Food Science, Universiti Malaysia Terengganu, Kuala Nerus, Terengganu, Malaysia
- Fellow Academy of Sciences Malaysia, Kuala Lumpur, Kuala Lumpur Federal Territory, Malaysia
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Lou HR, Wang X, Gao Y, Zeng Q. Comparison of ARIMA model, DNN model and LSTM model in predicting disease burden of occupational pneumoconiosis in Tianjin, China. BMC Public Health 2022; 22:2167. [PMID: 36434563 PMCID: PMC9694549 DOI: 10.1186/s12889-022-14642-3] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/02/2022] [Accepted: 11/16/2022] [Indexed: 11/27/2022] Open
Abstract
BACKGROUND This study aims to explore appropriate model for predicting the disease burden of pneumoconiosis in Tianjin by comparing the prediction effects of Autoregressive Integrated Moving Average (ARIMA) model, Deep Neural Networks (DNN) model and multivariate Long Short-Term Memory Neural Network (LSTM) models. METHODS Disability adjusted life year (DALY) was used to evaluate the disease burden of occupational pneumoconiosis. ARIMA model, DNN model and multivariate LSTM model were used to establish prediction model. Three performance evaluation metrics including Root Mean Squared Error (RMSE), Mean Absolute Error (MAE) and Mean Absolute Percentage Error (MAPE) were used to compare the prediction effects of the three models. RESULTS From 1990 to 2021, there were 10,694 cases of pneumoconiosis patients in Tianjin, resulting in a total of 112,725.52 person-years of DALY. During this period, the annual DALY showed a fluctuating trend, but it had a strong correlation with the number of pneumoconiosis patients, the average age of onset, the average age of receiving dust and the gross industrial product, and had a significant nonlinear relationship with them. The comparison of prediction results showed that the performance of multivariate LSTM model and DNN model is much better than that of traditional ARIMA model. Compared with the DNN model, the multivariate LSTM model performed better in the training set, showing lower RMES (42.30 vs. 380.96), MAE (29.53 vs. 231.20) and MAPE (1.63% vs. 2.93%), but performed less stable than the DNN on the test set, showing slightly higher RMSE (1309.14 vs. 656.44), MAE (886.98 vs. 594.47) and MAPE (36.86% vs. 22.43%). CONCLUSION The machine learning techniques of DNN and LSTM are an innovative method to accurately and efficiently predict the burden of pneumoconiosis with the simplest data. It has great application prospects in the monitoring and early warning system of occupational disease burden.
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Affiliation(s)
- He-Ren Lou
- grid.464467.3Tianjin Center for Disease Control and Prevention, Tianjin, 300011 China ,grid.265021.20000 0000 9792 1228School of Public Health, Tianjin Medical University, Tianjin, 300070 China
| | - Xin Wang
- grid.464467.3Tianjin Center for Disease Control and Prevention, Tianjin, 300011 China
| | - Ya Gao
- grid.464467.3Tianjin Center for Disease Control and Prevention, Tianjin, 300011 China
| | - Qiang Zeng
- grid.464467.3Tianjin Center for Disease Control and Prevention, Tianjin, 300011 China
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12
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Deng B, Niu Y, Xu J, Rui J, Lin S, Zhao Z, Yu S, Guo Y, Luo L, Chen T, Li Q. Mathematical Models Supporting Control of COVID-19. China CDC Wkly 2022; 4:895-901. [PMID: 36285321 PMCID: PMC9579983 DOI: 10.46234/ccdcw2022.186] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/05/2022] [Accepted: 10/03/2022] [Indexed: 12/13/2022] Open
Abstract
Mathematical models have played an important role in the management of the coronavirus disease 2019 (COVID-19) pandemic. The aim of this review is to describe the use of COVID-19 mathematical models, their classification, and the advantages and disadvantages of different types of models. We conducted subject heading searches of PubMed and China National Knowledge Infrastructure with the terms "COVID-19," "Mathematical Statistical Model," "Model," "Modeling," "Agent-based Model," and "Ordinary Differential Equation Model" and classified and analyzed the scientific literature retrieved in the search. We categorized the models as data-driven or mechanism-driven. Data-driven models are mainly used for predicting epidemics, and have the advantage of rapid assessment of disease instances. However, their ability to determine transmission mechanisms is limited. Mechanism-driven models include ordinary differential equation (ODE) and agent-based models. ODE models are used to estimate transmissibility and evaluate impact of interventions. Although ODE models are good at determining pathogen transmission characteristics, they are less suitable for simulation of early epidemic stages and rely heavily on availability of first-hand field data. Agent-based models consider influences of individual differences, but they require large amounts of data and can take a long time to develop fully. Many COVID-19 mathematical modeling studies have been conducted, and these have been used for predicting trends, evaluating interventions, and calculating pathogen transmissibility. Successful infectious disease modeling requires comprehensive considerations of data, applications, and purposes.
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Affiliation(s)
- Bin Deng
- State Key Laboratory of Molecular Vaccinology and Molecular Diagnostics, School of Public Health, Xiamen University, Xiamen City, Fujian Province, China
| | - Yan Niu
- Chinese Center for Disease Control and Prevention, Beijing, China
| | - Jingwen Xu
- State Key Laboratory of Molecular Vaccinology and Molecular Diagnostics, School of Public Health, Xiamen University, Xiamen City, Fujian Province, China
| | - Jia Rui
- State Key Laboratory of Molecular Vaccinology and Molecular Diagnostics, School of Public Health, Xiamen University, Xiamen City, Fujian Province, China
| | - Shengnan Lin
- State Key Laboratory of Molecular Vaccinology and Molecular Diagnostics, School of Public Health, Xiamen University, Xiamen City, Fujian Province, China
| | - Zeyu Zhao
- State Key Laboratory of Molecular Vaccinology and Molecular Diagnostics, School of Public Health, Xiamen University, Xiamen City, Fujian Province, China
| | - Shanshan Yu
- State Key Laboratory of Molecular Vaccinology and Molecular Diagnostics, School of Public Health, Xiamen University, Xiamen City, Fujian Province, China
| | - Yichao Guo
- State Key Laboratory of Molecular Vaccinology and Molecular Diagnostics, School of Public Health, Xiamen University, Xiamen City, Fujian Province, China
| | - Li Luo
- State Key Laboratory of Molecular Vaccinology and Molecular Diagnostics, School of Public Health, Xiamen University, Xiamen City, Fujian Province, China
| | - Tianmu Chen
- State Key Laboratory of Molecular Vaccinology and Molecular Diagnostics, School of Public Health, Xiamen University, Xiamen City, Fujian Province, China,Tianmu Chen,
| | - Qun Li
- Chinese Center for Disease Control and Prevention, Beijing, China,Qun Li,
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