Long-Term Time Prediction of Cumulative Number of Deaths in Brazil, China, Germany, Italy, Spain, the United States: an application to COVID-19 S-shaped models

Marcela Portela Santos De Figueiredo,André Luiz Pinto Dos Santos,Tiago Alessandro Espínola Ferreira,Maitê Priscila Lima Jota De Queiroz

doi:10.33448/rsd-v9i8.6565

Marcela Portela Santos De Figueiredo, André Luiz Pinto Dos Santos + Show 2 more

Open Access

https://doi.org/10.33448/rsd-v9i8.6565

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Abstract

This research aims to adjust the Gompertz and Bertalanffy nonlinear regression model for the accumulated deaths by COVID-19 in six countries Brazil, United States, Germany, Italy, China, and Spain. It employed three different performance measures in the training process, adjusted determination coefficient , Akaike Information Criterion (AIC), and Residual Mean Square (RMS). The Mean Absolute Percentage Error (MAPE) and the Relative Error (RE) criterion were used to select the best model in the test dataset. On the training dataset, the Bertalanffy model was the one that best described the growth of deaths for China, while the Gompertz model was the best for Brazil, Germany, Italy, Spain, and the United States. In contrast, the Bertalanffy model was the best for Spain in the test dataset, according to MAPE and RE. According to the Gompertz model, 214,100 CI (175,929;267,008) people will die in Brazil, that will reach a maximum of 1,577 with a prediction interval [1,367; 1,819] of daily new deaths at its disease peak. The nonlinear models studied described the number of deaths growth curve satisfactorily, providing parameters with practical interpretations. Evidence was found that Brazil may surpass the United States regarding the total number of deaths. Short and long-term time prediction, as well as the turning point of each country, are presented and compared to other predictive models of the literature.

Highlights

COVID-19 represents a considerable threat to health on a global scale. (Baumgartner et al, 2020)
This paper evaluates models forecasting in a multi-day-ahead forecasting strategy with 95% confidence interval and estimation of turning point of curve, whose accuracy of the models may assists theses countries in decision-making to implement social distance, quarantine or isolation strategies
It is possible to establish that the best model to be adjusted in the training data according to the criteria of Akaike Information Criterion (AIC), Residual Mean Squares (RMS), and : Gompertz for all countries, except for China, which had the Bertalanffy model more adequately adjusted to the data

Summary

Introduction

COVID-19 represents a considerable threat to health on a global scale. (Baumgartner et al, 2020). COVID-19 represents a considerable threat to health on a global scale. (Baumgartner et al, 2020). COVID-19 was first detected in China in late 2019 (Na Zhu et al, 2020), and has spread to Asia, Europe, North America, and, more recently, South America and Africa (Baumgartner et al, 2020). According to Rodriguez-Morales et al (2020), the first case of COVID-19 in Latin America occurred in Brazil. The country currently has the highest number of deaths in that region (Prado, 2020; European Center for Disease Prevention and Control, 2020a). On April 23rd, 20 European countries already showed evidence that they had passed the peak of transmission (European Center for Disease Prevention and Control, 2020b).

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