An assessment of transmission dynamics via time-varying reproduction number of the second wave of the COVID-19 epidemic in Fiji

Abstract

This study involves the estimation of a key epidemiological parameter for evaluating and monitoring the transmissibility of a disease. The time-varying reproduction number is the index for quantifying the transmissibility of infectious diseases. Accurate and timely estimation of the time-varying reproduction number is essential for optimizing non-pharmacological interventions and movement control orders during epidemics. The time-varying reproduction number for the second wave of the pandemic in Fiji is estimated using the popular EpiEstim R package and the publicly available COVID-19 data from 19 April 2021 to 1 December 2021. Our findings show that the non-pharmacological interventions and movement control orders introduced and enforced by the Fijian Government had a significant impact in preventing the spread of COVID-19. Moreover, the results show that many restrictions were either relaxed or eased when the time-varying reproduction number was below the threshold value of 1. The results have provided some information on the second wave of the COVID-19 pandemic that could be used in the future as a guide for public health policymakers in Fiji. Estimation of time-varying reproduction numbers would be helpful for continuous monitoring of the effectiveness of the current public health policies that are being implemented in Fiji.

Advancements in Defining and Estimating the Reproduction Number in Infectious Disease Epidemiology

“… \begin{document}$ {R}_{t} $\end{document} estimates the spread of pathogens by monitoring and tracking data that evolves over time during the course of an outbreak ( 16 ). \begin{document}$ {R}_{t} $\end{document} is also an important parameter for describing the epidemiological characteristics of a disease and evaluating the effectiveness of PHSMs ( Figure 1 ) ( 17 - 18 ). The values of \begin{document}$ {R}_{t} $\end{document} vary due to factors such as changes in immunity and interventions across different populations, including interventions that impact personal contact networks ( 19 - 20 ).…”

“…is also an important parameter for describing the epidemiological characteristics of a disease and evaluating the effectiveness of PHSMs (Figure 1) (17)(18). The values of vary due to factors such as changes in immunity and interventions across different populations, including interventions that impact personal contact networks (19-20).…”