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Predictive Models for Cumulative Confirmed COVID-19 Cases by Day in Southeast Asia

Yupaporn Areepong1, Rapin Sunthornwat2,*

1 Department of Applied Statistics, King Mongkut’s University of Technology North Bangkok, Bangkok, 10800, Thailand
2 Industrial Technology Program, Pathumwan Institute of Technology, Bangkok, 10330, Thailand

* Corresponding Author: Rapin Sunthornwat. Email: email

(This article belongs to this Special Issue: Computer Modelling of Transmission, Spread, Control and Diagnosis of COVID-19)

Computer Modeling in Engineering & Sciences 2020, 125(3), 927-942.


Coronavirus disease 2019 outbreak has spread as a pandemic since the end of year 2019. This situation has been causing a lot of problems of human beings such as economic problems, health problems. The forecasting of the number of infectious people is required by the authorities of all countries including Southeast Asian countries to make a decision and control the outbreak. This research is to investigate the suitable forecasting model for the number of infectious people in Southeast Asian countries. A comparison of forecasting models between logistic growth curve which is symmetric and Gompertz growth curve which is asymmetric based on the maximum of Coefficent of Determination and the minimum of Root Mean Squared Percentage Error is also proposed. The estimation of parameters of the forecasting models is evaluated by the least square method. In addition, spreading of the outbreak is estimated by the derivative of the number of cumulative cases. The findings show that Gompertz growth curve is a suitable forecasting model for Indonesia, Philippines, and Malaysia and logistic growth curve suits the other countries in South Asia.


Cite This Article

Areepong, Y., Sunthornwat, R. (2020). Predictive Models for Cumulative Confirmed COVID-19 Cases by Day in Southeast Asia. CMES-Computer Modeling in Engineering & Sciences, 125(3), 927–942.


cc This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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