文章基本信息

标题：Data-driven analysis and prediction of COVID-19 infection in Southeast Asia by using a phenomenological model
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作者：Faihatuz Zuhairoh ; Dedi Rosadi
期刊名称：Pakistan Journal of Statistics and Operation Research
印刷版ISSN：2220-5810
出版年度：2022
卷号：18
期号：1
页码：59-69
DOI：10.18187/pjsor.v18i1.3714
语种：English
出版社：College of Statistical and Actuarial Sciences
摘要：COVID-19 has spread throughout the world, including in Southeast Asia. Many studies have made predictions using various models. However, very few are data-driven based. Meanwhile for the COVID-19 case, which is still ongoing, it is very suitable to use data-driven approach with phenomenological models. This paper aimed to obtain effective forecasting models and then predict when COVID-19 in Southeast Asia will peak and end using daily cumulative case data. The research applied the Richards curve and the logistic growth model, combining the two models to make prediction of the COVID-19 cases in Southeast Asia, both the countries with one pandemic wave or those with more than one pandemic wave. The best prediction results were obtained using the Richards curve with the logistic growth model parameters used as the initial values. In the best scenario, the Southeast Asia region is expected to be free from the COVID-19 pandemic at the end of 2021. These modeling results are expected to provide information about the provision of health facilities and how to handle infectious disease outbreaks in the future.
关键词：COVID-19; data-driven; prediction; Richards curve; logistic growth model; Southeast Asia