Hedi Hedi, Anie Lusiani, Anny Suryani, Agus Binarto
{"title":"Forecasting COVID-19 cases for Top-3 countries of Southeast Asian Nation","authors":"Hedi Hedi, Anie Lusiani, Anny Suryani, Agus Binarto","doi":"10.33122/ijtmer.v5i2.138","DOIUrl":null,"url":null,"abstract":"Several countries continue controlling the spread of the corona virus to decrease the number of new COVID-19 cases. Currently some Southeast Asian countries require an estimate of the num-ber of daily new COVID-19 cases of in the future in order to reopen or consider lifting strict pre-vention policies. This study applies ARIMA and SARIMA forecasting models to predict the de-cline in the number of new cases in three Southeast Asian countries. The first modelling is carried out using the ARIMA model with optimized model parameters based on the Akaike Information Criterion (AIC) and Bayesian Information Criterion (BIC) analysis. Then, the Mean Absolute Error (MAE), Root Mean Square Error (RMSE) and Mean Absolute Percent Error (MAPE) are evaluated is applied a criterion to select the best model. The best ARIMA and SARIMA models are selected manually and they are used to predict the number of new cases in three Southeast Asian coun-tries. It is expected that the number of new cases in these countries will experience a significant decline in the next month from September 2021. The prediction of SARIMA model indicates a better result than the ARIMA model which confirms the existence of a season in COVID-19 data.","PeriodicalId":280543,"journal":{"name":"International Journal of Trends in Mathematics Education Research","volume":"2 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2022-06-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"International Journal of Trends in Mathematics Education Research","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.33122/ijtmer.v5i2.138","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
Abstract
Several countries continue controlling the spread of the corona virus to decrease the number of new COVID-19 cases. Currently some Southeast Asian countries require an estimate of the num-ber of daily new COVID-19 cases of in the future in order to reopen or consider lifting strict pre-vention policies. This study applies ARIMA and SARIMA forecasting models to predict the de-cline in the number of new cases in three Southeast Asian countries. The first modelling is carried out using the ARIMA model with optimized model parameters based on the Akaike Information Criterion (AIC) and Bayesian Information Criterion (BIC) analysis. Then, the Mean Absolute Error (MAE), Root Mean Square Error (RMSE) and Mean Absolute Percent Error (MAPE) are evaluated is applied a criterion to select the best model. The best ARIMA and SARIMA models are selected manually and they are used to predict the number of new cases in three Southeast Asian coun-tries. It is expected that the number of new cases in these countries will experience a significant decline in the next month from September 2021. The prediction of SARIMA model indicates a better result than the ARIMA model which confirms the existence of a season in COVID-19 data.
一些国家继续控制冠状病毒的传播,以减少新发COVID-19病例数。目前,一些东南亚国家需要估计未来每天新增的COVID-19病例数,以便重新开放或考虑取消严格的预防政策。本研究运用ARIMA和SARIMA预测模型,预测东南亚三国新发病例数的下降趋势。采用基于赤池信息准则(Akaike Information Criterion, AIC)和贝叶斯信息准则(Bayesian Information Criterion, BIC)分析的ARIMA模型,优化模型参数,进行了第一次建模。然后,对平均绝对误差(MAE)、均方根误差(RMSE)和平均绝对百分比误差(MAPE)进行评估,作为选择最佳模型的标准。最好的ARIMA和SARIMA模型是人工选择的,它们被用来预测三个东南亚国家的新病例数。预计从2021年9月起,下个月这些国家的新病例数将大幅下降。SARIMA模型的预测结果优于ARIMA模型,证实了COVID-19数据存在季节。