Application of artificial neural networks to predict the COVID-19 outbreak

Abstract

Background Millions of people have been infected worldwide in the COVID-19 pandemic. In this study, we aim to propose fourteen prediction models based on artificial neural networks (ANN) to predict the COVID-19 outbreak for policy makers. Methods The ANN-based models were utilized to estimate the confirmed cases of COVID-19 in China, Japan, Singapore, Iran, Italy, South Africa and United States of America. These models exploit historical records of confirmed cases, while their main difference is the number of days that they assume to have impact on the estimation process. The COVID-19 data were divided into a train part and a test part. The former was used to train the ANN models, while the latter was utilized to compare the purposes. The data analysis shows not only significant fluctuations in the daily confirmed cases but also different ranges of total confirmed cases observed in the time interval considered. Results Based on the obtained results, the ANN-based model that takes into account the previous 14 days outperforms the other ones. This comparison reveals the importance of considering the maximum incubation period in predicting the COVID-19 outbreak. Comparing the ranges of determination coefficients indicates that the estimated results for Italy are the best one. Moreover, the predicted results for Iran achieved the ranges of [0.09, 0.15] and [0.21, 0.36] for the mean absolute relative errors and normalized root mean square errors, respectively, which were the best ranges obtained for these criteria among different countries. Conclusion Based on the achieved results, the ANN-based model that takes into account the previous fourteen days for prediction is suggested to predict daily confirmed cases, particularly in countries that have experienced the first peak of the COVID-19 outbreak. This study has not only proved the applicability of ANN-based model for prediction of the COVID-19 outbreak, but also showed that considering incubation period of SARS-COV-2 in prediction models may generate more accurate estimations.

RETRACTED ARTICLE: The prediction of the lifetime of the new coronavirus in the USA using mathematical models

“…In Ghazaly et al (2020), to predict the outbreak COVID-19 use AI and DL with time series using nonlinear regressive network (NAR). Niazkar and Niazkar (2020) predicted the COVID-19 outbreak by prediction models based on ANN. In Tamang et al (2020), to predict and forecast the number of death due to COVID-19, ANN-based curve fitting is used.…”

“…( 2020 ), to predict the outbreak COVID-19 use AI and DL with time series using nonlinear regressive network (NAR). Niazkar and Niazkar ( 2020 ) predicted the COVID-19 outbreak by prediction models based on ANN. In Tamang et al.…”

COVID‐19 and hematology findings based on the current evidences: A puzzle with many missing pieces

“…In late 2019, a new coronavirus was isolated from pneumonia patients with unknown etiology for the first time in Wuhan, China 1,2 . According to the latest reported data, the emerging coronavirus has crossed international borders, infecting more than thirty‐seven million individuals, leading to more than million deaths 3 .…”

Assessment of Three Mathematical Prediction Models for Forecasting the COVID-19 Outbreak in Iran and Turkey

“…For this purpose, numerous prediction models have been proposed in the literature, which may be categorized into mathematical models [ 4 – 7 ] and soft computing approaches [ 3 , 8 , 9 ]. The former has many different types including simple explicit equations to a complicated system of equations with many parameters required to be calibrated.…”

“…The former has many different types including simple explicit equations to a complicated system of equations with many parameters required to be calibrated. The soft computing techniques use a part of data to capture the pandemic trend, which enables them to predict an approximation of future confirmed cases [ 9 ]. Both of these two types of prediction models require data either to calibrate parameters or to train intelligence-based models.…”