Accounting for the Potential of Overdispersion in Estimation of the Time-varying Reproduction Number.

Autor:	Ho F; From the WHO Collaborating Centre for Infectious Disease Epidemiology and Control, School of Public Health, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong Special Administrative Region, China., Parag KV; MRC Centre for Global Infectious Disease Analysis, Imperial College London, London, United Kingdom.; NIHR Health Protection Research Unit in Behavioural Science and Evaluation, University of Bristol, Bristol, United Kingdom., Adam DC; From the WHO Collaborating Centre for Infectious Disease Epidemiology and Control, School of Public Health, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong Special Administrative Region, China., Lau EHY; From the WHO Collaborating Centre for Infectious Disease Epidemiology and Control, School of Public Health, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong Special Administrative Region, China.; Laboratory of Data Discovery for Health Limited, Hong Kong Science Park, New Territories, Hong Kong., Cowling BJ; From the WHO Collaborating Centre for Infectious Disease Epidemiology and Control, School of Public Health, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong Special Administrative Region, China.; Laboratory of Data Discovery for Health Limited, Hong Kong Science Park, New Territories, Hong Kong., Tsang TK; From the WHO Collaborating Centre for Infectious Disease Epidemiology and Control, School of Public Health, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong Special Administrative Region, China.; Laboratory of Data Discovery for Health Limited, Hong Kong Science Park, New Territories, Hong Kong.
Jazyk:	angličtina
Zdroj:	Epidemiology (Cambridge, Mass.) [Epidemiology] 2023 Mar 01; Vol. 34 (2), pp. 201-205. Date of Electronic Publication: 2022 Dec 13.
DOI:	10.1097/EDE.0000000000001563
Abstrakt:	Background: The time-varying reproduction number, Rt, is commonly used to monitor the transmissibility of an infectious disease during an epidemic, but standard methods for estimating Rt seldom account for the impact of overdispersion on transmission. Methods: We developed a negative binomial framework to estimate Rt and a time-varying dispersion parameter (kt). 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 Rt 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 kt <0.5 at the start of both waves, indicating appreciable heterogeneity in transmission. We also found that kt decreased sharply to around 0.4 when a large cluster of infections occurred. Conclusions: Our proposed approach can contribute to the estimation of Rt and monitoring of the time-varying dispersion parameters to quantify the role of superspreading. Competing Interests: Disclosure: B.J.C. reports honoraria from AstraZeneca, GSK, Moderna, Roche and Sanofi Pasteur. The other authors report no conflicts of interest. (Copyright © 2022 Wolters Kluwer Health, Inc. All rights reserved.)
Databáze:	MEDLINE
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