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Shyamala A, Murugeswari S, Mahendran G, Jothi Chitra R. Hybrid grey assisted whale optimization based machine learning for the COVID-19 prediction. Comput Methods Biomech Biomed Engin 2023:1-10. [PMID: 38112339 DOI: 10.1080/10255842.2023.2292008] [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: 04/18/2023] [Accepted: 12/03/2023] [Indexed: 12/21/2023]
Abstract
Recently, COVID-19 (coronavirus) has been a huge influence on the socio and economic field. COVID-19 cases are seriously increasing day-day and also don't identified proper vaccine for COVID-19. Hence, COVID-19 is fast spreading virus and it causes more deaths. In order to address this, the work has proposed a machine learning (ML) scheme for the prediction of COVID-19 positive, negative, and deceased instances. Initially, the data is pre-processed by eliminating redundant and missing values. Then, the features are selected using hybrid grey assisted whale optimization algorithm (H-GAWOA). Finally, the classifier ANFIS (adaptive network-based fuzzy inference systems) is used for investigating the confirmed, survival and death rate of COVID-19. The performance is analysed on John Hopkins University dataset and the performances like MSE, RMSE, MAPE, and R2 are measured. In all the comparisons, the MSE value is very less for the proposed model. Particularly, in the deceased cases prediction, the MSE value is 0.00 for the proposed H-GAWOA-ANFIS. Finally, it is proved that the suggested model is able to generate the better results when contrast to the other approaches.
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Affiliation(s)
- A Shyamala
- Department of Electronics and Communication Engineering, Mohamed Sathak Engineering College, Kilakarai, Ramanathapuram, Chennai, Tamil Nadu, India
| | - S Murugeswari
- Department of Electronics and Communication Engineering, Syed Ammal Engineering College, Ramanathapuram, Tamil Nadu, India
| | - G Mahendran
- Department of Electronics and Communication Engineering, Syed Ammal Engineering College, Ramanathapuram, Tamil Nadu, India
| | - R Jothi Chitra
- Department of Electronics and Communication Engineering, Velammal Institute of Technology, Thiruvallur, Chennai, Tamil Nadu, India
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2
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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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3
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Alkhammash EH, Assiri SA, Nemenqani DM, Althaqafi RMM, Hadjouni M, Saeed F, Elshewey AM. Application of Machine Learning to Predict COVID-19 Spread via an Optimized BPSO Model. Biomimetics (Basel) 2023; 8:457. [PMID: 37887588 PMCID: PMC10604133 DOI: 10.3390/biomimetics8060457] [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: 08/26/2023] [Revised: 09/21/2023] [Accepted: 09/21/2023] [Indexed: 10/28/2023] Open
Abstract
During the pandemic of the coronavirus disease (COVID-19), statistics showed that the number of affected cases differed from one country to another and also from one city to another. Therefore, in this paper, we provide an enhanced model for predicting COVID-19 samples in different regions of Saudi Arabia (high-altitude and sea-level areas). The model is developed using several stages and was successfully trained and tested using two datasets that were collected from Taif city (high-altitude area) and Jeddah city (sea-level area) in Saudi Arabia. Binary particle swarm optimization (BPSO) is used in this study for making feature selections using three different machine learning models, i.e., the random forest model, gradient boosting model, and naive Bayes model. A number of predicting evaluation metrics including accuracy, training score, testing score, F-measure, recall, precision, and receiver operating characteristic (ROC) curve were calculated to verify the performance of the three machine learning models on these datasets. The experimental results demonstrated that the gradient boosting model gives better results than the random forest and naive Bayes models with an accuracy of 94.6% using the Taif city dataset. For the dataset of Jeddah city, the results demonstrated that the random forest model outperforms the gradient boosting and naive Bayes models with an accuracy of 95.5%. The dataset of Jeddah city achieved better results than the dataset of Taif city in Saudi Arabia using the enhanced model for the term of accuracy.
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Affiliation(s)
- Eman H. Alkhammash
- Department of Computer Science, College of Computers and Information Technology, Taif University, P.O. Box 11099, Taif 21944, Saudi Arabia;
| | - Sara Ahmad Assiri
- Otolaryngology-Head and Neck Surgert Department, King Faisal Hospital, P.O. Box 11099, Taif 21944, Saudi Arabia;
| | - Dalal M. Nemenqani
- College of Medicine, Taif University, P.O. Box 11099, Taif 21944, Saudi Arabia; (D.M.N.); (R.M.M.A.)
| | - Raad M. M. Althaqafi
- College of Medicine, Taif University, P.O. Box 11099, Taif 21944, Saudi Arabia; (D.M.N.); (R.M.M.A.)
| | - Myriam Hadjouni
- Department of Computer Sciences, College of Computer and Information Science, Princess Nourah bint Abdulrahman University, P.O. Box 84428, Riyadh 11671, Saudi Arabia
| | - Faisal Saeed
- DAAI Research Group, Department of Computing and Data Science, School of Computing and Digital Technology, Birmingham City University, Birmingham B4 7XG, UK;
| | - Ahmed M. Elshewey
- Faculty of Computers and Information, Computer Science Department, Suez University, Suez 43533, Egypt;
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Yang J, Xu X, Ma X, Wang Z, You Q, Shan W, Yang Y, Bo X, Yin C. Application of machine learning to predict hospital visits for respiratory diseases using meteorological and air pollution factors in Linyi, China. ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH INTERNATIONAL 2023; 30:88431-88443. [PMID: 37438508 DOI: 10.1007/s11356-023-28682-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/10/2023] [Accepted: 07/04/2023] [Indexed: 07/14/2023]
Abstract
Urbanization and industrial development have resulted in increased air pollution, which is concerning for public health. This study evaluates the effect of meteorological factors and air pollution on hospital visits for respiratory diseases (pneumonia, acute upper respiratory infections, and chronic lower respiratory diseases). The test dataset comprises meteorological parameters, air pollutant concentrations, and outpatient hospital visits for respiratory diseases in Linyi, China, from January 1, 2016 to August 20, 2022. We use support vector regression (SVR) to build models that enable analysis of the effect of meteorological factors and air pollutants on the number of outpatient visits for respiratory diseases. Spearman correlation analysis and SVR model results indicate that NO2, PM2.5, and PM10 are correlated with the occurrence of respiratory diseases, with the strongest correlation relating to pneumonia. An increase in the daily average temperature and daily relative humidity decreases the number of patients with pneumonia and chronic lower respiratory diseases but increases the number of patients with acute upper respiratory infections. The SVR modeling has the potential to predict the number of respiratory-related hospital visits. This work demonstrates that machine learning can be combined with meteorological and air pollution data for disease prediction, providing a useful tool whereby policymakers can take preventive measures.
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Affiliation(s)
- Jing Yang
- Intersection of Wohushan Road and Wuhan Road in Beicheng New Area, Linyi People's Hospital, Linyi, 276000, Shandong, People's Republic of China
| | - Xin Xu
- Department of Environmental Science and Engineering, Beijing University of Chemical Technology, Beijing, 100029, People's Republic of China
| | - Xiaotian Ma
- School of Information and Control Engineering, Jilin Institute of Chemical Technology, Jilin City, 132022, People's Republic of China
| | - Zhaotong Wang
- Department of Environmental Science and Engineering, Beijing University of Chemical Technology, Beijing, 100029, People's Republic of China
| | - Qian You
- School of Management and Engineering, Capital University of Economics and Business, Beijing, 100070, People's Republic of China
| | - Wanyue Shan
- Department of Environmental Science and Engineering, Beijing University of Chemical Technology, Beijing, 100029, People's Republic of China
| | - Ying Yang
- Department of Environmental Science and Engineering, Beijing University of Chemical Technology, Beijing, 100029, People's Republic of China
| | - Xin Bo
- Department of Environmental Science and Engineering, Beijing University of Chemical Technology, Beijing, 100029, People's Republic of China
- BUCT Institute for Carbon-Neutrality of Chinese Industries, Beijing, 100029, People's Republic of China
| | - Chuansheng Yin
- Intersection of Wohushan Road and Wuhan Road in Beicheng New Area, Linyi People's Hospital, Linyi, 276000, Shandong, People's Republic of China.
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5
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De Ruvo S, Pio G, Vessio G, Volpe V. Forecasting and what-if analysis of new positive COVID-19 cases during the first three waves in Italy. Med Biol Eng Comput 2023:10.1007/s11517-023-02831-0. [PMID: 37316767 DOI: 10.1007/s11517-023-02831-0] [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: 05/10/2022] [Accepted: 03/29/2023] [Indexed: 06/16/2023]
Abstract
The joint exploitation of data related to epidemiological, mobility, and restriction aspects of COVID-19 with machine learning algorithms can support the development of predictive models that can be used to forecast new positive cases and study the impact of more or less severe restrictions. In this work, we integrate heterogeneous data from several sources and solve a multivariate time series forecasting task, specifically targeting the Italian case at both national and regional levels, during the first three waves of the pandemic. The goal is to build a robust predictive model to predict the number of new cases over a given time horizon so that any restrictive actions can be better planned. In addition, we perform a what-if analysis based on the best-identified predictive models to evaluate the impact of specific restrictions on the trend of positive cases. Our focus on the first three waves is motivated by the fact that it represents a typical emergency scenario (when no stable cure or vaccine is available) that may occur when a new pandemic spreads. Our experimental results prove that exploiting the considered heterogeneous data leads to accurate predictive models, reaching a WAPE of 5.75% at the national level. Furthermore, in the subsequent what-if analysis, we observed that strong all-in-one initiatives, such as total lockdowns, may not be adequate, while more specific and targeted solutions should be adopted. The developed models can help policy and decision-makers better plan intervention strategies and retrospectively analyze the effects of the decisions made at different scales. Joint exploitation of data on epidemiological, mobility, and restriction aspects of COVID-19 with machine learning algorithms to learn predictive models to forecast new positive cases.
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Affiliation(s)
- Serena De Ruvo
- Dept. of Computer Science, University of Bari Aldo Moro, Bari, Italy
| | - Gianvito Pio
- Dept. of Computer Science, University of Bari Aldo Moro, Bari, Italy.
- Big Data Lab, National Interuniversity Consortium for Informatics (CINI), Rome, Italy.
| | - Gennaro Vessio
- Dept. of Computer Science, University of Bari Aldo Moro, Bari, Italy
| | - Vincenzo Volpe
- Dept. of Computer Science, University of Bari Aldo Moro, Bari, Italy
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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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7
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Zheng L, Chen Y, Jiang S, Song J, Zheng J. Predicting the distribution of COVID-19 through CGAN-Taking Macau as an example. Front Big Data 2023; 6:1008292. [PMID: 36760879 PMCID: PMC9907848 DOI: 10.3389/fdata.2023.1008292] [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: 08/01/2022] [Accepted: 01/06/2023] [Indexed: 01/26/2023] Open
Abstract
Machine learning (ML) is an innovative method that is widely used in data prediction. Predicting the COVID-19 distribution using ML is essential for urban security risk assessment and governance. This study uses conditional generative adversarial network (CGAN) to construct a method to predict the COVID-19 hotspot distribution through urban texture and business formats and establishes a relationship between urban elements and COVID-19 so that machines can automatically predict the epidemic hotspots in cities. Taking Macau as an example, this method is used to determine the correlation between the urban texture and business hotspots of Macau and the new epidemic hotspot clusters. Different types of samples afforded different epidemic prediction accuracies. The results show the following: (1) CGAN can accurately predict the distribution area of COVID-19, and the accuracy can exceed 70%. (2) The results of predicting the COVID-19 distribution through urban texture and POI data of hospitals and stations are the best, with an accuracy of more than 60% in experiments in different regions of Macau. (3) The proposed method can also predict other areas in the city that may be at risk of COVID-19 and help urban epidemic prevention and control.
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Li D, Ren X, Su Y. Predicting COVID-19 using lioness optimization algorithm and graph convolution network. Soft comput 2023; 27:5437-5501. [PMID: 36686544 PMCID: PMC9838306 DOI: 10.1007/s00500-022-07778-2] [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] [Accepted: 12/21/2022] [Indexed: 01/11/2023]
Abstract
In this paper, a graph convolution network prediction model based on the lioness optimization algorithm (LsOA-GCN) is proposed to predict the cumulative number of confirmed COVID-19 cases in 17 regions of Hubei Province from March 23 to March 29, 2020, according to the transmission characteristics of COVID-19. On the one hand, Spearman correlation analysis with delay days and LsOA are used to capture the dynamic changes of feature information to obtain the temporal features. On the other hand, the graph convolutional network is used to capture the topological structure of the city network, so as to obtain spatial information and finally realize the prediction task. Then, we evaluate this model through performance evaluation indicators and statistical test methods and compare the results of LsOA-GCN with 10 representative prediction methods in the current epidemic prediction study. The experimental results show that the LsOA-GCN prediction model is significantly better than other prediction methods in all indicators and can successfully capture spatio-temporal information from feature data, thereby achieving accurate prediction of epidemic trends in different regions of Hubei Province.
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Affiliation(s)
- Dong Li
- College of Economics and Management, Xi’an University of Posts and Telecommunications, Xi’an, 710061 Shaanxi People’s Republic of China
| | - Xiaofei Ren
- College of Economics and Management, Xi’an University of Posts and Telecommunications, Xi’an, 710061 Shaanxi People’s Republic of China
| | - Yunze Su
- College of Economics and Management, Xi’an University of Posts and Telecommunications, Xi’an, 710061 Shaanxi People’s Republic of China
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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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Chakraborty A, Das D, Mitra S, De D, Pal AJ. Forecasting adversities of COVID-19 waves in India using intelligent computing. INNOVATIONS IN SYSTEMS AND SOFTWARE ENGINEERING 2022:1-17. [PMID: 36186271 PMCID: PMC9512957 DOI: 10.1007/s11334-022-00486-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 05/16/2021] [Accepted: 09/14/2022] [Indexed: 06/16/2023]
Abstract
The second wave of the COVID-19 pandemic outburst triggered enormously all over India. This ill-fated and fatal brawl affected millions of Indian citizens, with many active and infected Indians struggling to recover from this deadly disease to date, leading to a grief situation. The present situation warrants developing a robust and sound forecasting model to evaluate the adversities of the epidemic with reasonable accuracy to assist officials in curbing this hazard. Consequently, we employed Auto-ARIMA, Auto-ETS, Auto-MLP, Auto-ELM, AM, MLP and proposed ELM methods for assessing accumulative infected COVID-19 individuals by the end of July 2021. We made 90 days of advanced forecasting, i.e., up to 24 July 2021, for the number of cumulative infected COVID-19 cases of India using all seven methods in 15 days' intervals. We fine-tuned the hyper-parameters to enhance the prediction performance of these models and observed that the proposed ELM model offers satisfactory accuracy with MAPE of 5.01, and it rendered better accuracy than the other six models. To comprehend the dataset's nature, five features are extracted. The resulting feature values encouraged further investigation of the models for an updated dataset, where the proposed model provides encouraging results.
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Affiliation(s)
- Arijit Chakraborty
- Bachelor of Computer Application Department, The Heritage Academy, Kolkata, India
| | - Dipankar Das
- Bachelor of Computer Application Department, The Heritage Academy, Kolkata, India
| | - Sajal Mitra
- Department of Computer Science and Engineering, Heritage Institute of Technology, Kolkata, India
| | - Debashis De
- Department of Computer Science and Engineering, Maulana Abul Kalam Azad University of Technology, Kolkata, India
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Daniyal M, Tawiah K, Muhammadullah S, Opoku-Ameyaw K. Comparison of Conventional Modeling Techniques with the Neural Network Autoregressive Model (NNAR): Application to COVID-19 Data. JOURNAL OF HEALTHCARE ENGINEERING 2022; 2022:4802743. [PMID: 35747601 PMCID: PMC9213132 DOI: 10.1155/2022/4802743] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/15/2022] [Revised: 05/12/2022] [Accepted: 05/19/2022] [Indexed: 11/17/2022]
Abstract
The coronavirus disease 2019 (COVID-19) pandemic continues to destroy human life around the world. Almost every country throughout the globe suffered from this pandemic, forcing various governments to apply different restrictions to reduce its impact. In this study, we compare different time-series models with the neural network autoregressive model (NNAR). The study used COVID-19 data in Pakistan from February 26, 2020, to February 18, 2022, as a training and testing data set for modeling. Different models were applied and estimated on the training data set, and these models were assessed on the testing data set. Based on the mean absolute scaled error (MAE) and root mean square error (RMSE) for the training and testing data sets, the NNAR model outperformed the autoregressive integrated moving average (ARIMA) model and other competing models indicating that the NNAR model is the most appropriate for forecasting. Forecasts from the NNAR model showed that the cumulative confirmed COVID-19 cases will be 1,597,180 and cumulative confirmed COVID-19 deaths will be 32,628 on April 18, 2022. We encourage the Pakistan Government to boost its immunization policy.
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Affiliation(s)
- Muhammad Daniyal
- Department of Statistics, Islamia University of Bahawalpur, Bahawalpur, Pakistan
| | - Kassim Tawiah
- Department of Mathematics and Statistics, University of Energy and Natural Resources, Sunyani, Ghana
- Department of Statistics and Actuarial Science, Kwame Nkrumah University of Science and Technology, Kumasi, Ghana
| | - Sara Muhammadullah
- Pakistan Institute of Development Economics, Islamabad, Pakistan
- National Institute of Health, Islamabad, Pakistan
| | - Kwaku Opoku-Ameyaw
- Department of Mathematics and Statistics, University of Energy and Natural Resources, Sunyani, Ghana
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Shah H, Shah S, Tanwar S, Gupta R, Kumar N. Fusion of AI techniques to tackle COVID-19 pandemic: models, incidence rates, and future trends. MULTIMEDIA SYSTEMS 2022; 28:1189-1222. [PMID: 34276140 PMCID: PMC8275905 DOI: 10.1007/s00530-021-00818-1] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/05/2020] [Accepted: 05/29/2021] [Indexed: 05/05/2023]
Abstract
The COVID-19 pandemic is rapidly spreading across the globe and infected millions of people that take hundreds of thousands of lives. Over the years, the role of Artificial intelligence (AI) has been on the rise as its algorithms are getting more and more accurate and it is thought that its role in strengthening the existing healthcare system will be the most profound. Moreover, the pandemic brought an opportunity to showcase AI and healthcare integration potentials as the current infrastructure worldwide is overwhelmed and crumbling. Due to AI's flexibility and adaptability, it can be used as a tool to tackle COVID-19. Motivated by these facts, in this paper, we surveyed how the AI techniques can handle the COVID-19 pandemic situation and present the merits and demerits of these techniques. This paper presents a comprehensive end-to-end review of all the AI-techniques that can be used to tackle all areas of the pandemic. Further, we systematically discuss the issues of the COVID-19, and based on the literature review, we suggest their potential countermeasures using AI techniques. In the end, we analyze various open research issues and challenges associated with integrating the AI techniques in the COVID-19.
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Affiliation(s)
- Het Shah
- Department of Computer Science and Engineering, Institute of Technology, Nirma University, Ahmedabad, India
| | - Saiyam Shah
- Department of Computer Science and Engineering, Institute of Technology, Nirma University, Ahmedabad, India
| | - Sudeep Tanwar
- Department of Computer Science and Engineering, Institute of Technology, Nirma University, Ahmedabad, India
| | - Rajesh Gupta
- Department of Computer Science and Engineering, Institute of Technology, Nirma University, Ahmedabad, India
| | - Neeraj Kumar
- Department of Computer Science Engineering, Thapar Institute of Engineering and Technology, Deemed to be University, Patiala, India
- School of Computer Science, University of Petroleum and Energy Studies, Dehradun, Uttarakhand India
- King Abdul Aziz University, Jeddah, Saudi Arabia
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Deep-Data-Driven Neural Networks for COVID-19 Vaccine Efficacy. EPIDEMIOLGIA (BASEL, SWITZERLAND) 2021; 2:564-586. [PMID: 36417217 PMCID: PMC9620931 DOI: 10.3390/epidemiologia2040039] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 09/26/2021] [Revised: 11/09/2021] [Accepted: 11/24/2021] [Indexed: 12/14/2022]
Abstract
Vaccination strategies to lessen the impact of the spread of a disease are fundamental to public health authorities and policy makers. The socio-economic benefit of full return to normalcy is the core of such strategies. In this paper, a COVID-19 vaccination model with efficacy rate is developed and analyzed. The epidemiological parameters of the model are learned via a feed-forward neural network. A hybrid approach that combines residual neural network with variants of recurrent neural network is implemented and analyzed for reliable and accurate prediction of daily cases. The error metrics and a k-fold cross validation with random splitting reveal that a particular type of hybrid approach called residual neural network with gated recurrent unit is the best hybrid neural network architecture. The data-driven simulations confirm the fact that the vaccination rate with higher efficacy lowers the infectiousness and basic reproduction number. As a study case, COVID-19 data for the state of Tennessee in USA is used.
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Kumar N, Susan S. Particle swarm optimization of partitions and fuzzy order for fuzzy time series forecasting of COVID-19. Appl Soft Comput 2021; 110:107611. [PMID: 34518764 PMCID: PMC8425580 DOI: 10.1016/j.asoc.2021.107611] [Citation(s) in RCA: 17] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/04/2021] [Revised: 06/03/2021] [Accepted: 06/07/2021] [Indexed: 12/16/2022]
Abstract
Major hyperparameters which affect fuzzy time series (FTS) forecasting are the number of partitions, length of partition intervals in the universe of discourse, and the fuzzy order. There are very few studies which have considered an integrated solution to optimize all the hyperparameters. In this paper, we strive to achieve optimum values of all three hyperparameters for fuzzy time series forecasting of the COVID-19 pandemic using the Particle Swarm Optimization (PSO) algorithm. We specifically propose two techniques, namely nested FTS-PSO and exhaustive search FTS-PSO for determining the optimal interval length, as an augmentation to the FTS-PSO model that optimizes the interval length and the fuzzy order. Nested PSO has two PSO loops: (i) the inner PSO optimizes the combination of fuzzy order and boundaries of intervals for a given number of partitions defined by the outer loop, and the resultant cost is fed back to the outer PSO; (ii) the outer PSO optimizes the number of partitions to reduce the cost while meeting the defined constraint. Exhaustive search FTS-PSO also has two loops where the inner loop is similar to nested FTS-PSO while the outer loop iterates over a pre-defined search space of number of partitions. We analyze the effectiveness of the two approaches by comparing with ARIMA, FbProphet, and the state-of-the-art FTS and FTS-PSO models. We adopt COVID-19 highly affected 10 countries worldwide to perform forecasting of coronavirus confirmed cases. We consider two phases of COVID-19 spread, one from the year 2020 and another from 2021. Our study provides an analytical aspect of the COVID-19 pandemic, and aims to achieve optimal number and length of intervals along with fuzzy order for FTS forecasting of COVID-19. The results prove that the exhaustive search FTS-PSO outperformed all the methods whereas nested FTS-PSO performed moderately well.
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Affiliation(s)
- Naresh Kumar
- Department of Information Technology, Delhi Technological University, Delhi, India
| | - Seba Susan
- Department of Information Technology, Delhi Technological University, Delhi, India
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Mangla S, Pathak AK, Arshad M, Haque U. Short-term forecasting of the COVID-19 outbreak in India. Int Health 2021; 13:410-420. [PMID: 34091670 PMCID: PMC8194983 DOI: 10.1093/inthealth/ihab031] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/10/2020] [Revised: 11/02/2020] [Accepted: 05/27/2021] [Indexed: 01/28/2023] Open
Abstract
As the outbreak of coronavirus disease 2019 (COVID-19) is rapidly spreading in different parts of India, a reliable forecast for the cumulative confirmed cases and the number of deaths can be helpful for policymakers in making the decisions for utilizing available resources in the country. Recently, various mathematical models have been used to predict the outbreak of COVID-19 worldwide and also in India. In this article we use exponential, logistic, Gompertz growth and autoregressive integrated moving average (ARIMA) models to predict the spread of COVID-19 in India after the announcement of various unlock phases. The mean absolute percentage error and root mean square error comparative measures were used to check the goodness-of-fit of the growth models and Akaike information criterion for ARIMA model selection. Using COVID-19 pandemic data up to 20 December 2020 from India and its five most affected states (Maharashtra, Karnataka, Andhra Pradesh, Tamil Nadu and Kerala), we report 15-days-ahead forecasts for cumulative confirmed cases and the number of deaths. Based on available data, we found that the ARIMA model is the best-fitting model for COVID-19 cases in India and its most affected states.
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Affiliation(s)
- Sherry Mangla
- Department of Mathematics and Statistics, Central University of Punjab, Bathinda, Punjab, India 151401
| | - Ashok Kumar Pathak
- Department of Mathematics and Statistics, Central University of Punjab, Bathinda, Punjab, India 151401
| | - Mohd Arshad
- Department of Mathematics, Indian Institute of Technology Indore, Simrol, Indore, India 453552
- Department of Statistics and Operations Research, Aligarh Muslim University, Aligarh, India 202002
| | - Ubydul Haque
- Department of Biostatistics and Epidemiology, University of North Texas Health Science Center, Fort Worth, TX, USA
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Selvakumar K, Lokesh S. The prediction of the lifetime of the new coronavirus in the USA using mathematical models. Soft comput 2021; 25:10575-10594. [PMID: 33716562 PMCID: PMC7943712 DOI: 10.1007/s00500-021-05643-2] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/08/2023]
Abstract
The World Health Organization (WHO) on December 31, 2019, was informed of several cases of respiratory diseases of unknown origin in the city of Wuhan in the Chinese Province of Hubei, the clinical manifestations of which were similar to those of viral pneumonia and manifested as fever, cough, and shortness of breath. And, the disease caused by the virus is named the new coronavirus disease 2019 and it will be abbreviated as 2019-nCoV and COVID-19. As of January 30, 2020, the WHO classified this epidemic as a global health emergency (Chung et al. in Radiology 295(1):202-207, 2020). It is an international real-life problem. Due to deaths, globally everyone is under fear. Now, it is the responsibility of researchers to give hope to the people. In this article, we aim to better protect people and general pandemic preparedness by predicting the lifetime of the disease-causing virus using three mathematical models. This article deals with a complex real-life problem people face all over the world, an international real-life problem. The main focus is on the USA due to large infection and death due to coronavirus and thereby the life of every individual is uncertain. The death counts of the USA from February 29 to April 22, 2020, are used in this article as a data set. The death counts of the USA are fitted by the solutions of three mathematical models and a solution to an international problem is achieved. Based on the death rate, the lifetime of the coronavirus COVID-19 is predicted as 1464.76 days from February 29, 2020. That is, after March 2024 there will be no death in the USA due to COVID-19 if everyone follows the guidelines of WHO and the advice of healthcare workers. People and government can get prepared for this situation and many lives can be saved. It is the contribution of soft computing. Finally, this article suggests several steps to control the spread and severity of the disease. The research work, the lifetime prediction presented in this article is entirely new and differs from all other articles in the literature.
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Affiliation(s)
- K. Selvakumar
- Department of Science and Humanities, Anna University, Chennai, India
- University College of Engineering, Nagercoil, Tamil Nadu 629004 India
| | - S. Lokesh
- Department of Computer Science and Engineering, Hindustan Institute of Technology, Othakalmandapam, Coimbatore, Tamil Nadu 641032 India
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Nutritional knowledge, attitude and behaviours regarding Coronavirus Disease 2019 among residents of Gonabad, Iran. Public Health Nutr 2021; 24:1088-1094. [PMID: 33427193 PMCID: PMC7870909 DOI: 10.1017/s1368980021000033] [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] [Indexed: 11/07/2022]
Abstract
OBJECTIVE Coronavirus Disease 2019 (COVID-19) is a respiratory disease and a major global health-related issue. Knowledge, attitude and behaviours associated with this disease are being developed. Infected animals and consumption of contaminated foods are probably the main sources of this viral infection. Adhering to a healthy diet is effective in preventing patient affliction and recovery. Thus, the present research aims to determine the nutritional knowledge, attitude and behaviours associated with COVID-19 among Residents of Gonabad, Iran. DESIGN The present online cross-sectional survey was conducted in March 2020 on 389 people selected through convenient sampling method. The data collection instrument was a questionnaire developed by the present researchers comprising four sections: demographic information, knowledge, attitude and nutritional behaviour. The collected data were analysed statistically in SPSS. SETTING Gonabad city in Khorasan Razavi in the Northeast of Iran. PARTICIPANTS All people at or above 18 years of age. RESULTS The present results revealed that the mean age of the participants was 37·3 ± 11·3 years. The mean scores for knowledge, attitude and nutritional behaviour were, respectively, 9·7 ± 1·4, 14·3 ± 3·5 and 29·6 ± 4·2. The mean scores for nutritional knowledge and attitude showed no statistically significant correlation with age, education, occupation, marital status and economic status. However, the mean nutritional behaviour score was significantly higher among women than men (P = 0·004). CONCLUSIONS Participants' nutritional attitude and behaviour regarding COVID-19, at its early stage in Iran, were average and above average. The public education provided with this regard can have affected this result.
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Tat Dat T, Frédéric P, Hang NTT, Jules M, Duc Thang N, Piffault C, Willy R, Susely F, Lê HV, Tuschmann W, Tien Zung N. Epidemic Dynamics via Wavelet Theory and Machine Learning with Applications to Covid-19. BIOLOGY 2020; 9:E477. [PMID: 33353045 PMCID: PMC7767158 DOI: 10.3390/biology9120477] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/19/2020] [Revised: 12/13/2020] [Accepted: 12/15/2020] [Indexed: 01/27/2023]
Abstract
We introduce the concept of epidemic-fitted wavelets which comprise, in particular, as special cases the number I(t) of infectious individuals at time t in classical SIR models and their derivatives. We present a novel method for modelling epidemic dynamics by a model selection method using wavelet theory and, for its applications, machine learning-based curve fitting techniques. Our universal models are functions that are finite linear combinations of epidemic-fitted wavelets. We apply our method by modelling and forecasting, based on the Johns Hopkins University dataset, the spread of the current Covid-19 (SARS-CoV-2) epidemic in France, Germany, Italy and the Czech Republic, as well as in the US federal states New York and Florida.
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Affiliation(s)
- Tô Tat Dat
- Centre de Mathématiques Laurent-Schwartz, École Polytechnique Cour Vaneau, 91120 Palaiseau, France
| | - Protin Frédéric
- Torus Actions SAS, 3 Avenue Didier Daurat, 31400 Toulouse, France; (P.F.); (N.T.T.H.); (M.J.); (N.D.T.); (C.P.); (F.S.)
| | - Nguyen T. T. Hang
- Torus Actions SAS, 3 Avenue Didier Daurat, 31400 Toulouse, France; (P.F.); (N.T.T.H.); (M.J.); (N.D.T.); (C.P.); (F.S.)
| | - Martel Jules
- Torus Actions SAS, 3 Avenue Didier Daurat, 31400 Toulouse, France; (P.F.); (N.T.T.H.); (M.J.); (N.D.T.); (C.P.); (F.S.)
| | - Nguyen Duc Thang
- Torus Actions SAS, 3 Avenue Didier Daurat, 31400 Toulouse, France; (P.F.); (N.T.T.H.); (M.J.); (N.D.T.); (C.P.); (F.S.)
| | - Charles Piffault
- Torus Actions SAS, 3 Avenue Didier Daurat, 31400 Toulouse, France; (P.F.); (N.T.T.H.); (M.J.); (N.D.T.); (C.P.); (F.S.)
| | - Rodríguez Willy
- Ecole Nationale de l’Aviation Civile, 7 Avenue Edouard Belin, 31400 Toulouse, France;
| | - Figueroa Susely
- Torus Actions SAS, 3 Avenue Didier Daurat, 31400 Toulouse, France; (P.F.); (N.T.T.H.); (M.J.); (N.D.T.); (C.P.); (F.S.)
| | - Hông Vân Lê
- Institute of Mathematics of the Czech Academy of Sciences, Zitna 25, 11567 Praha 1, Czech Republic;
| | - Wilderich Tuschmann
- Fakultät für Mathematik, Karlsruher Institut für Technologie (KIT), Englerstr. 2, D-76131 Karlsruhe, Germany;
| | - Nguyen Tien Zung
- Institut de Mathematiques de Toulouse, Université Toulouse 3, 18 Route de Narbonne, 31400 Toulouse, France;
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Daniyal M, Ogundokun RO, Abid K, Khan MD, Ogundokun OE. Predictive modeling of COVID-19 death cases in Pakistan. Infect Dis Model 2020; 5:897-904. [PMID: 33195884 PMCID: PMC7647892 DOI: 10.1016/j.idm.2020.10.011] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/21/2020] [Revised: 10/15/2020] [Accepted: 10/31/2020] [Indexed: 02/07/2023] Open
Abstract
BACKGROUND The world is presently facing the challenges posed by COVID-19 (2019-nCoV), especially in the public health sector, and these challenges are dangerous to both health and life. The disease results in an acute respiratory infection that may result in pain and death. In Pakistan, the disease curve shows a vertical trend by almost 256K established cases of the diseases and 6035 documented death cases till August 5, 2020. OBJECTIVE The primary purpose of this study is to provide the statistical model to predict the trend of COVID-19 death cases in Pakistan. The age and gender of COVID-19 victims were represented using a descriptive study. METHOD ology: Three regression models, which include Linear, logarithmic, and quadratic, were employed in this study for the modelling of COVID-19 death cases in Pakistan. These three models were compared based on R2, Adjusted R2, AIC, and BIC criterions. The data utilized for the modelling was obtained from the National Institute of Health of Pakistan from February 26, 2020 to August 5, 2020. CONCLUSION The finding deduced after the prediction modelling is that the rate of mortality would decrease by the end of October. The total number of deaths will reach its maximum point; then, it will gradually decrease. This indicates that the curve of total deaths will continue to be flat, i.e., it will shift to be constant, which is also the upper bound of the underlying function of absolute death.
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Affiliation(s)
- Muhammad Daniyal
- Department of Statistics, Islamia University of Bahawalpur, Pakistan
| | | | - Khadijah Abid
- Research Evaluation Unit, College of Physicians & Surgeons, Pakistan
| | | | - Opeyemi Eyitayo Ogundokun
- Directoriate Department, Audit Section, Agricultural and Rural Management Training Institute, Ilorin, Nigeria
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