A statistical framework for tracking the time-varying superspreading potential of COVID-19 epidemic.
Epidemics
; 42: 100670, 2023 03.
Article
in English
| MEDLINE | ID: covidwho-2210265
ABSTRACT
Timely detection of an evolving event of an infectious disease with superspreading potential is imperative for territory-wide disease control as well as preventing future outbreaks. While the reproduction number (R) is a commonly-adopted metric for disease transmissibility, the transmission heterogeneity quantified by dispersion parameter k, a metric for superspreading potential is seldom tracked. In this study, we developed an estimation framework to track the time-varying risk of superspreading events (SSEs) and demonstrated the method using the three epidemic waves of COVID-19 in Hong Kong. Epidemiological contact tracing data of the confirmed COVID-19 cases from 23 January 2020 to 30 September 2021 were obtained. By applying branching process models, we jointly estimated the time-varying R and k. Individual-based outbreak simulations were conducted to compare the time-varying assessment of the superspreading potential with the typical non-time-varying estimate of k over a period of time. We found that the COVID-19 transmission in Hong Kong exhibited substantial superspreading during the initial phase of the epidemics, with only 1 % (95 % Credible interval [CrI] 0.6-2 %), 5 % (95 % CrI 3-7 %) and 10 % (95 % CrI 8-14 %) of the most infectious cases generated 80 % of all transmission for the first, second and third epidemic waves, respectively. After implementing local public health interventions, R estimates dropped gradually and k estimates increased thereby reducing the risk of SSEs to approaching zero. Outbreak simulations indicated that the non-time-varying estimate of k may overlook the possibility of large outbreaks. Hence, an estimation of the time-varying k as a compliment of R as a monitoring of both disease transmissibility and superspreading potential, particularly when public health interventions were relaxed is crucial for minimizing the risk of future outbreaks.
Keywords
Full text:
Available
Collection:
International databases
Database:
MEDLINE
Main subject:
Epidemics
/
COVID-19
Type of study:
Observational study
/
Prognostic study
Limits:
Humans
Country/Region as subject:
Asia
Language:
English
Journal:
Epidemics
Year:
2023
Document Type:
Article
Affiliation country:
J.epidem.2023.100670
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