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Namasudra S, Dhamodharavadhani S, Rathipriya R. Nonlinear Neural Network Based Forecasting Model for Predicting COVID-19 Cases. Neural Process Lett 2023; 55:171-191. [PMID: 33821142 PMCID: PMC8012519 DOI: 10.1007/s11063-021-10495-w] [Citation(s) in RCA: 28] [Impact Index Per Article: 28.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 03/12/2021] [Indexed: 02/07/2023]
Abstract
The recent COVID-19 outbreak has severely affected people around the world. There is a need of an efficient decision making tool to improve awareness about the spread of COVID-19 infections among the common public. An accurate and reliable neural network based tool for predicting confirmed, recovered and death cases of COVID-19 can be very helpful to the health consultants for taking appropriate actions to control the outbreak. This paper proposes a novel Nonlinear Autoregressive (NAR) Neural Network Time Series (NAR-NNTS) model for forecasting COVID-19 cases. This NAR-NNTS model is trained with Scaled Conjugate Gradient (SCG), Levenberg Marquardt (LM) and Bayesian Regularization (BR) training algorithms. The performance of the proposed model has been compared by using Root Mean Square Error (RMSE), Mean Square Error (MSE) and correlation co-efficient i.e. R-value. The results show that NAR-NNTS model trained with LM training algorithm performs better than other models for COVID-19 epidemiological data prediction.
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Affiliation(s)
- Suyel Namasudra
- Department of Computer Science and Engineering, National Institute of Technology Patna, Bihar, India
| | | | - R Rathipriya
- Department of Computer Science, Periyar University, Salem, India
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Kaliappan J, Srinivasan K, Mian Qaisar S, Sundararajan K, Chang CY, C S. Performance Evaluation of Regression Models for the Prediction of the COVID-19 Reproduction Rate. Front Public Health 2021; 9:729795. [PMID: 34595149 PMCID: PMC8476853 DOI: 10.3389/fpubh.2021.729795] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/23/2021] [Accepted: 08/16/2021] [Indexed: 01/28/2023] Open
Abstract
This paper aims to evaluate the performance of multiple non-linear regression techniques, such as support-vector regression (SVR), k-nearest neighbor (KNN), Random Forest Regressor, Gradient Boosting, and XGBOOST for COVID-19 reproduction rate prediction and to study the impact of feature selection algorithms and hyperparameter tuning on prediction. Sixteen features (for example, Total_cases_per_million and Total_deaths_per_million) related to significant factors, such as testing, death, positivity rate, active cases, stringency index, and population density are considered for the COVID-19 reproduction rate prediction. These 16 features are ranked using Random Forest, Gradient Boosting, and XGBOOST feature selection algorithms. Seven features are selected from the 16 features according to the ranks assigned by most of the above mentioned feature-selection algorithms. Predictions by historical statistical models are based solely on the predicted feature and the assumption that future instances resemble past occurrences. However, techniques, such as Random Forest, XGBOOST, Gradient Boosting, KNN, and SVR considered the influence of other significant features for predicting the result. The performance of reproduction rate prediction is measured by mean absolute error (MAE), mean squared error (MSE), root mean squared error (RMSE), R-Squared, relative absolute error (RAE), and root relative squared error (RRSE) metrics. The performances of algorithms with and without feature selection are similar, but a remarkable difference is seen with hyperparameter tuning. The results suggest that the reproduction rate is highly dependent on many features, and the prediction should not be based solely upon past values. In the case without hyperparameter tuning, the minimum value of RAE is 0.117315935 with feature selection and 0.0968989 without feature selection, respectively. The KNN attains a low MAE value of 0.0008 and performs well without feature selection and with hyperparameter tuning. The results show that predictions performed using all features and hyperparameter tuning is more accurate than predictions performed using selected features.
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Affiliation(s)
- Jayakumar Kaliappan
- School of Computer Science and Engineering, Vellore Institute of Technology, Vellore, India
| | - Kathiravan Srinivasan
- School of Computer Science and Engineering, Vellore Institute of Technology, Vellore, India
| | - Saeed Mian Qaisar
- Electrical and Computer Engineering Department, Effat University, Jeddah, Saudi Arabia
| | - Karpagam Sundararajan
- School of Information Technology and Engineering, Vellore Institute of Technology, Vellore, India
| | - Chuan-Yu Chang
- Department of Computer Science and Information Engineering, National Yunlin University of Science and Technology, Douliu, Taiwan
| | - Suganthan C
- School of Social Sciences and Languages, Vellore Institute of Technology, Vellore, India
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Torres–Signes A, Frías MP, Ruiz-Medina MD. COVID-19 mortality analysis from soft-data multivariate curve regression and machine learning. STOCHASTIC ENVIRONMENTAL RESEARCH AND RISK ASSESSMENT : RESEARCH JOURNAL 2021; 35:2659-2678. [PMID: 33897300 PMCID: PMC8053745 DOI: 10.1007/s00477-021-02021-0] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Accepted: 04/03/2021] [Indexed: 05/25/2023]
Abstract
UNLABELLED A multiple objective space-time forecasting approach is presented involving cyclical curve log-regression, and multivariate time series spatial residual correlation analysis. Specifically, the mean quadratic loss function is minimized in the framework of trigonometric regression. While, in our subsequent spatial residual correlation analysis, maximization of the likelihood allows us to compute the posterior mode in a Bayesian multivariate time series soft-data framework. The presented approach is applied to the analysis of COVID-19 mortality in the first wave affecting the Spanish Communities, since March 8, 2020 until May 13, 2020. An empirical comparative study with Machine Learning (ML) regression, based on random k-fold cross-validation, and bootstrapping confidence interval and probability density estimation, is carried out. This empirical analysis also investigates the performance of ML regression models in a hard- and soft-data frameworks. The results could be extrapolated to other counts, countries, and posterior COVID-19 waves. SUPPLEMENTARY INFORMATION The online version contains supplementary material available at 10.1007/s00477-021-02021-0.
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Affiliation(s)
- Antoni Torres–Signes
- Department of Statistics and Operation Research, Faculty of Sciences, University of Málaga, Málaga, Spain
| | - María P. Frías
- Department of Statistics and Operation Research, Faculty of Sciences, University of Jaén, Jaén, Spain
| | - María D. Ruiz-Medina
- Department of Statistics and Operation Research, Faculty of Sciences, University of Granada, Granada, Spain
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Braga MDB, Fernandes RDS, de Souza GN, da Rocha JEC, Dolácio CJF, Tavares IDS, Pinheiro RR, Noronha FN, Rodrigues LLS, Ramos RTJ, Carneiro AR, de Brito SR, Diniz HAC, Botelho MDN, Vallinoto ACR. Artificial neural networks for short-term forecasting of cases, deaths, and hospital beds occupancy in the COVID-19 pandemic at the Brazilian Amazon. PLoS One 2021; 16:e0248161. [PMID: 33705453 PMCID: PMC7951831 DOI: 10.1371/journal.pone.0248161] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/10/2020] [Accepted: 02/20/2021] [Indexed: 12/24/2022] Open
Abstract
The first case of the novel coronavirus in Brazil was notified on February 26, 2020. After 21 days, the first case was reported in the second largest State of the Brazilian Amazon. The State of Pará presented difficulties in combating the pandemic, ranging from underreporting and a low number of tests to a large territorial distance between cities with installed hospital capacity. Due to these factors, mathematical data-driven short-term forecasting models can be a promising initiative to assist government officials in more agile and reliable actions. This study presents an approach based on artificial neural networks for the daily and cumulative forecasts of cases and deaths caused by COVID-19, and the forecast of demand for hospital beds. Six scenarios with different periods were used to identify the quality of the generated forecasting and the period in which they start to deteriorate. Results indicated that the computational model adapted capably to the training period and was able to make consistent short-term forecasts, especially for the cumulative variables and for demand hospital beds.
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Affiliation(s)
| | | | | | | | | | - Ivaldo da Silva Tavares
- Forestry Engineering Department, Universidade Federal de Viçosa, Viçosa, Minas Gerais, Brazil
| | | | | | - Luana Lorena Silva Rodrigues
- Postgraduate Program in Health Sciences, Institute of Collective Health, Universidade Federal do Oeste do Pará, Santarém, Pará, Brazil
| | | | | | | | - Hugo Alex Carneiro Diniz
- Institute of Educational Sciences, Universidade Federal do Oeste do Pará, Santarém, Pará, Brazil
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