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Forecasting Brazilian and American COVID-19 cases based on artificial intelligence coupled with climatic exogenous variables
- Source :
- Chaos, Solitons & Fractals, Chaos, Solitons, and Fractals
- Publication Year :
- 2020
- Publisher :
- Elsevier BV, 2020.
-
Abstract
- Highlights • Hybrid and single models are employed to forecast COVID-19 cases in the Brazilian and USA context. • Models for multi-step-ahead forecasting coupled with climatic variables are evaluated. • Out-of-sample forecasting errors lower than 3.08% are achieved by best models. • Temperature and Precipitation variables play a key role in the forecasting model. • VMD-based models are the most suitable tools to forecast COVID-19 cases six-days-ahead.<br />Graphical abstract<br />The novel coronavirus disease (COVID-19) is a public health problem once according to the World Health Organization up to June 24th, 2020, more than 9.1 million people were infected, and more than 470 thousand have died worldwide. In the current scenario, the Brazil and the United States of America present a high daily incidence of new cases and deaths. Therefore, it is important to forecast the number of new cases in a time window of one week, once this can help the public health system developing strategic planning to deals with the COVID-19. The application of the forecasting artificial intelligence (AI) models has the potential of deal with dynamical behavior of time-series like of COVID-19. In this paper, Bayesian regression neural network, cubist regression, k-nearest neighbors, quantile random forest, and support vector regression, are used stand-alone, and coupled with the recent pre-processing variational mode decomposition (VMD) employed to decompose the time series into several intrinsic mode functions. All AI techniques are evaluated in the task of time-series forecasting with one, three, and six-days-ahead the cumulative COVID-19 cases in five Brazilian and American states, with a high number of cases up to April 28th, 2020. Previous cumulative COVID-19 cases and exogenous variables as daily temperature and precipitation were employed as inputs for all forecasting models. The models’ effectiveness are evaluated based on the performance criteria. In general, the hybridization of VMD outperformed single forecasting models regarding the accuracy, specifically when the horizon is six-days-ahead, the hybrid VMD–single models achieved better accuracy in 70% of the cases. Regarding the exogenous variables, the importance ranking as predictor variables is, from the upper to the lower, past cases, temperature, and precipitation. Therefore, due to the efficiency of evaluated models to forecasting cumulative COVID-19 cases up to six-days-ahead, the adopted models can be recommended as a promising models for forecasting and be used to assist in the development of public policies to mitigate the effects of COVID-19 outbreak.
- Subjects :
- FOS: Computer and information sciences
Artificial intelligence
Computer Science - Machine Learning
Exogenous variables
Computer science
General Mathematics
General Physics and Astronomy
01 natural sciences
Article
Machine Learning (cs.LG)
010305 fluids & plasmas
Machine learning
0103 physical sciences
Quantitative Biology - Populations and Evolution
010301 acoustics
Artificial neural network
business.industry
Applied Mathematics
Populations and Evolution (q-bio.PE)
Mode (statistics)
COVID-19
Statistical and Nonlinear Physics
Regression
Random forest
Support vector machine
Ranking
FOS: Biological sciences
business
Bayesian linear regression
Variational mode decomposition
Forecasting
Quantile
Subjects
Details
- Language :
- English
- ISSN :
- 09600779
- Database :
- OpenAIRE
- Journal :
- Chaos, Solitons & Fractals
- Accession number :
- edsair.doi.dedup.....cb8d531a730b9d77d1e602a1fbb412f1
- Full Text :
- https://doi.org/10.1016/j.chaos.2020.110027