Analysis and Prediction of COVID-19 Data in Taiwan

15 Pages Posted: 15 Jun 2020

See all articles by Li-Pang Chen

Li-Pang Chen

University of Western Ontario

Date Written: May 27, 2020

Abstract

Started in 2019, every country has been affected by COVID-19 pandemic, and the number of infected cases and deaths is still increasing. When other countries have a larger number of infected cases and steeper increasing trends, Taiwan has less number of infected cases and the trend of infected cases is quite stable because of early preparation. To better understand the growth of the number of infected cases in Taiwan, we aim to construct predictive models to analyze the historical data and also predict the future number of infected cases. To build up predictive models and study predictions, we examine some methods in time series analysis. Through comprehensive analysis, we find that the predicted number of infected cases is still stable and a strictly increasing trend seems not to exist.

Note: Funding: No funding to support this research.

Declaration of Interest: No conflict of interests.

Keywords: COVID-19; infection; modeling; neural network; prediction; time-dependent; time series analysis

Suggested Citation

Chen, Li-Pang, Analysis and Prediction of COVID-19 Data in Taiwan (May 27, 2020). Available at SSRN: https://ssrn.com/abstract=3611761 or http://dx.doi.org/10.2139/ssrn.3611761

Li-Pang Chen (Contact Author)

University of Western Ontario ( email )

1151 Richmond Street
Suite 2
London, Ontario N6A 5B8
Canada

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