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Accounting for the Potential of Overdispersion in Estimation of the Time-varying Reproduction Number.
- Source :
- Epidemiology; Mar2023, Vol. 34 Issue 2, p201-205, 5p
- Publication Year :
- 2023
-
Abstract
- Background: The time-varying reproduction number, R <subscript> t </subscript>, is commonly used to monitor the transmissibility of an infectious disease during an epidemic, but standard methods for estimating R <subscript> t </subscript> seldom account for the impact of overdispersion on transmission. Methods: We developed a negative binomial framework to estimate R <subscript> t </subscript> and a time-varying dispersion parameter (k <subscript> t </subscript>). We applied the framework to COVID-19 incidence data in Hong Kong in 2020 and 2021. We conducted a simulation study to compare the performance of our model with the conventional Poisson-based approach. Results: Our framework estimated an R <subscript> t </subscript> peaking around 4 (95% credible interval = 3.13, 4.30), similar to that from the Poisson approach but with a better model fit. Our approach further estimated k <subscript> t </subscript> <0.5 at the start of both waves, indicating appreciable heterogeneity in transmission. We also found that k <subscript> t </subscript> decreased sharply to around 0.4 when a large cluster of infections occurred. Conclusions: Our proposed approach can contribute to the estimation of R <subscript> t </subscript> and monitoring of the time-varying dispersion parameters to quantify the role of superspreading. [ABSTRACT FROM AUTHOR]
Details
- Language :
- English
- ISSN :
- 10443983
- Volume :
- 34
- Issue :
- 2
- Database :
- Supplemental Index
- Journal :
- Epidemiology
- Publication Type :
- Academic Journal
- Accession number :
- 161621704
- Full Text :
- https://doi.org/10.1097/EDE.0000000000001563