Delay Time Parameter and Its Confidence Interval of Predictive Time Series of COVID-19 Outbreak Between the First and the Second Wave

Abstract

The outbreak of coronavirus disease 2019 (COVID-19) has become a major problem facing humans all around the world. For governments, in order to deal with the outbreak and protect the population, it is important to predict the number of infectious cases in the future to monitor the COVID-19 situation. This research aimed to compare the effectiveness of the logistic and the delay logistic time series in predicting the total number of infectious cases by using actual data from four countries, i.e. Thailand, South Korea, Egypt, and Nigeria. The total number of COVID-19 cases was collected during the first and the second wave of the COVID-19 outbreak. The validation and accuracy of the predictive growth curve time series were determined based on statistical values, i.e. the coefficient of determination and the root mean squared percentage error. It was found that the logistic time series was more appropriate for predicting the first wave in the four countries. For the second wave, the delay logistic time series was preferable. Moreover, the confidence interval based on Chebyshev’s inequality of delay time between the first and the second wave of the COVID-19 outbreak is also proposed.

Keywords

• confidence interval
• Coronavirus disease 2019
• delay logistic time series
• logistic time series