Probability Distributions for Modeling of COVID-19 cases and deaths in Thailand

ABSTRACT

The COVID-19 is a pandemic and continues to mutate and spread within Thailand and throughout the world. Recently, Omicron is a new COVID-19 variant of concern because it has several mutations that may have an impact on how it behaves. It is therefore important to understand COVID-19 dynamics in order to prevent or control infections appropriately. In this study, we analyzed a model of the daily number of COVID-19 cases and deaths in Thailand using five different probability distributions. Maximum likelihood estimation (MLE) is applied to estimate parameters of the five distributions. The results indicate that the Weibull distribution and the log-normal distribution are the most suitable distributions that fit the data on daily confirmed cases and on daily confirmed deaths, respectively, by using the Akaike information criterion (AIC) and the Bayes information criterion (BIC). © 2022. International Journal of Mathematics and Computer Science. All Rights Reserved.