Background:The short term forecasts regarding different parameters of the COVID-19 are very important to make informed decisions.However,majority of the earlier contributions have used classical time series models,suc...Background:The short term forecasts regarding different parameters of the COVID-19 are very important to make informed decisions.However,majority of the earlier contributions have used classical time series models,such as auto regressive integrated moving average(ARIMA)models,to obtain the said forecasts for Iran and its neighbors.In addition,the impacts of lifting the lockdowns in the said countries have not been studied.The aim of this paper is to propose more flexible Bayesian structural time series(BSTS)models for forecasting the future trends of the COVID-19 in Iran and its neighbors,and to compare the predictive power of the BSTS models with frequently used ARIMA models.The paper also aims to investigate the casual impacts of lifting the lockdown in the targeted countries using proposed models.Methods:We have proposed BSTS models to forecast the patterns of this pandemic in Iran and its neighbors.The predictive power of the proposed models has been compared with ARIMA models using different forecast accuracy criteria.We have also studied the causal impacts of resuming commercial/social activities in these countries using intervention analysis under BSTS models.The forecasts for next thirty days were obtained by using the data from March 16 to July 22,2020.These data have been obtained from Our World in Data and Humanitarian Data Exchange(HDX).All the numerical results have been obtained using R software.Results:Different measures of forecast accuracy advocated that forecasts under BSTS models were better than those under ARIMA models.Our forecasts suggested that the active numbers of cases are expected to decrease in Iran and its neighbors,except Afghanistan.However,the death toll is expected to increase at more pace in majority of these countries.The resuming of commercial/social activities in these countries has accelerated the surges in number of positive cases.Conclusions:The serious efforts would be needed to make sure that these expected figures regarding active number of cases come true.Iran and its neighbors need to improve their extensive healthcare infrastructure to cut down the higher expected death toll.Finally,these countries should develop and implement the strict SOPs for the commercial activities in order to prevent the expected second wave of the pandemic.展开更多
文摘Background:The short term forecasts regarding different parameters of the COVID-19 are very important to make informed decisions.However,majority of the earlier contributions have used classical time series models,such as auto regressive integrated moving average(ARIMA)models,to obtain the said forecasts for Iran and its neighbors.In addition,the impacts of lifting the lockdowns in the said countries have not been studied.The aim of this paper is to propose more flexible Bayesian structural time series(BSTS)models for forecasting the future trends of the COVID-19 in Iran and its neighbors,and to compare the predictive power of the BSTS models with frequently used ARIMA models.The paper also aims to investigate the casual impacts of lifting the lockdown in the targeted countries using proposed models.Methods:We have proposed BSTS models to forecast the patterns of this pandemic in Iran and its neighbors.The predictive power of the proposed models has been compared with ARIMA models using different forecast accuracy criteria.We have also studied the causal impacts of resuming commercial/social activities in these countries using intervention analysis under BSTS models.The forecasts for next thirty days were obtained by using the data from March 16 to July 22,2020.These data have been obtained from Our World in Data and Humanitarian Data Exchange(HDX).All the numerical results have been obtained using R software.Results:Different measures of forecast accuracy advocated that forecasts under BSTS models were better than those under ARIMA models.Our forecasts suggested that the active numbers of cases are expected to decrease in Iran and its neighbors,except Afghanistan.However,the death toll is expected to increase at more pace in majority of these countries.The resuming of commercial/social activities in these countries has accelerated the surges in number of positive cases.Conclusions:The serious efforts would be needed to make sure that these expected figures regarding active number of cases come true.Iran and its neighbors need to improve their extensive healthcare infrastructure to cut down the higher expected death toll.Finally,these countries should develop and implement the strict SOPs for the commercial activities in order to prevent the expected second wave of the pandemic.
