Forecasting Brazilian and American COVID-19 cases based on artificial intelligence coupled with climatic exogenous variables

TL;DR

This study evaluates AI-based models, coupled with climatic variables and variational mode decomposition, to forecast COVID-19 cases in Brazil and the US, demonstrating improved accuracy for short-term predictions.

Contribution

It introduces a hybrid forecasting approach combining AI models with VMD and climatic data, enhancing short-term COVID-19 case prediction accuracy.

Findings

VMD hybrid models outperform single models in accuracy.

Six-day-ahead forecasts achieve 70% better accuracy.

Past cases, temperature, and precipitation are key predictors.

Abstract

The novel coronavirus disease (COVID-19) is a public health problem once according to the World Health Organization up to June 10th, 2020, more than 7.1 million people were infected, and more than 400 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. 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. 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 Artificial Intelligence 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 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 hybridization of VMD outperformed single forecasting models regarding the accuracy, specifically when the horizon is six-days-ahead, achieving better accuracy in 70% of the cases. Regarding the exogenous variables, the importance ranking as predictor variables is past cases, temperature, and precipitation. 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.