COVID-19 due to infections with the 2019 novel coronavirus(SARS-CoV-2)was first reported in Wuhan,Hubei Province,China in December 2019.Up to 2 March 2020,it has already spread to more than 55 countries,infected more ...COVID-19 due to infections with the 2019 novel coronavirus(SARS-CoV-2)was first reported in Wuhan,Hubei Province,China in December 2019.Up to 2 March 2020,it has already spread to more than 55 countries,infected more than 85000 individuals,and yielded to about 3000 death cases[1].Due to the vigorous outbreak of COVID-19,it has become a global concern.Further management and controlling of this worldwide threat inevitably relies on the preparedness and precise risk assessment of the future,particularly in countries that new cases have been observed.In this regard,estimation of new probable confirmed and death cases in the near future is crucial to health care systems of each country.As a result,some researches have been conducted to address this new concern[2-4].展开更多
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 f...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.展开更多
文摘COVID-19 due to infections with the 2019 novel coronavirus(SARS-CoV-2)was first reported in Wuhan,Hubei Province,China in December 2019.Up to 2 March 2020,it has already spread to more than 55 countries,infected more than 85000 individuals,and yielded to about 3000 death cases[1].Due to the vigorous outbreak of COVID-19,it has become a global concern.Further management and controlling of this worldwide threat inevitably relies on the preparedness and precise risk assessment of the future,particularly in countries that new cases have been observed.In this regard,estimation of new probable confirmed and death cases in the near future is crucial to health care systems of each country.As a result,some researches have been conducted to address this new concern[2-4].
文摘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.
