Reproduction number, discrete forecasting model, and chaos analytics for Coronavirus Disease 2019 outbreak in India, Bangladesh, and Myanmar

Abstract

Emerging Infectious Disease of Coronavirus 2019 is a catastrophe of human beings. Controlling, monitoring, and forecasting the COVID-19 outbreak is very important for the authorities to make a decision on launching the policy for suppressing it. Reproduction numbers of epidemiology models and forecasting models have been developed to control and monitor the COVID-19 outbreak via policies of authorities, such as social distancing and wearing mask. Thus, the purposes of this research are to estimate the reproduction number of susceptible infectious recovered models and forecast the number of total COVID-19 cases every day based on discrete logistic models. Moreover, chaos analysis and spreading power of the number of COVID-19 cases by day are investigated with regard to COVID-19 situation in India, Bangladesh, and Myanmar. The results showed that its spread was highest in India and the transmission potential of COVID-19 in Myanmar is higher compared to India and Bangladesh.

KEYWORDS:

• Coronavirus disease 2019
• parameter estimation
• biostatistics
• chaos analysis

Disclosure statement

No potential conflict of interest was reported by the author(s).

Additional information

Notes on contributors

Rapin Sunthornwat

Rapin Sunthornwat is currently a lecturer at industrial technology program in Pathumwan Institute of Technology, Thailand. He is an expert in biostatistics, mathematical biology, statistical process control, and forecasting modelling. Nowadays, her field of interest is application growth curve and stochastic model to predict COVID-19 outbreak.

Yupaporn Areepong

Yupaporn Areepong is currently a Professor at department of applied statistics in King Mongkut’s University of Technology North Bangkok, Thailand. She is an expert in statistical process control, sequential change-point analysis, and time series analysis. Nowadays, her field of interest is application statistical process control for monitoring and controlling COVID-19 outbreak.