Estimating Effective Reproductive Number of COVID-19 in Shiraz, Iran, from April to October in 2021
Archives of Advances in Biosciences,
Vol. 14 No. 1 (2023),
19 February 2023
,
Page 1-5
https://doi.org/10.22037/aab.v14i1.39403
Abstract
Introduction: COVID-19 is an infectious disease that was first reported in Wuhan, China, on 31st of December, 2019. After about three years, it is still one of the main health problems all over the world. Interventions to control COVID-19 should be based on epidemiological parameters which describe the dynamics of the disease. The goal of this study is to estimate the reproductive number parameter to understand the speed and dynamics of COVID-19 in Shiraz, Fars province of Iran.
Materials and Methods: 479 cases of COVID-19 were sampled in Shiraz, Iran. Case-pairs of infector-infectees were obtained by brief phone interviews with the patients. Considering time between symptom onsets of the infectors and their infectees as serial interval, best possible distribution was fitted to the serial interval data. To estimate reproductive number, it is assumed that reproductive number is linked to daily incidence and serial interval distribution.
Results: Gamma distribution with mean of 4.610 and standard deviation of 5.746 was fitted on serial interval data. Using this distribution and daily incidence, reproductive number was estimated. The reproductive number values ranged from 0.730 (95% CI: 0.713, 0.747) to 2.181 (95% CI: 2.183, 2.224). These values indicated that there were two peaks in April and May; following the interventions after those peaks, reproductive number values reduced to below 1. Hence, the interventions were effective and successfully managed the outbreak in both waves.
Conclusion: low reproductive number values in some periods of time indicates that preventive measures were effective in Shiraz, Fars province of Iran. In order to control the disease, reproductive number should decrease to below 1 which is happening at the end of the study.
- COVID 19
- Infectious disease
- Iran
- Machine learning
- Reproductive number
- Serial interval
How to Cite
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