|
|
 |
| |
|
|
|
Application of Markov-Switching-Dynamic-Regression Model for COVID-19 Reproduction Rate: A Case study of Zimbabwe |
|
|
|
PP: 705-721 |
|
|
|
doi:10.18576/jsap/140503
|
|
|
|
Author(s) |
|
|
|
C. Shoko,
K. Makatjane,
G. Madisa-Maseko,
|
|
|
|
Abstract |
|
|
| In this study, we develop a Markov Switching Dynamic Regression (MS(k)-DR(p)) model for the regime switches
based on the reproduction rate in real-time. The effects of the stringency index, daily COVID-19 deaths and total vaccinated individuals on the regimes are also analysed. Before the fitting of the MS(k)-DR(p) model, this study analysed the cointegration of the response variable and the explanatory variable using the Johansen procedure. Results from analysis show that the peaks and reproduction rate can best be described by shifts between two regimes, MS(2)-DR(1). The first regime is defined by high reproduction number and the second regime is identified by a low reproduction rate. The MS(2)-DR(1) model for the reproduction rate could only be explained by the stringency index and the daily COVID-19 deaths. An increase in stringency index results in a decrease in the reproduction rate for regime 2 and the opposite is true for regime 1. The developed model closely tracks regime changes caused by changes in the stringency index that include lock-down, mandatory mask- wearing, social distancing, etc. Thus, the MS-DR model is a useful policy tool for monitoring interventions by the public
health sector in controlling epidemics that have the same behaviour as COVID-19 |
|
|
|
|
|
|
|
