ArticleEpidemics2023
A statistical framework for tracking the time-varying superspreading potential of COVID-19 epidemic.
Article in Epidemics, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers, 1 of them a synthesis that pooled it.
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Who cites it
8 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Host factors associated with respiratory particle emission and virus presence within respiratory particles: a systematic review.Frontiers in microbiology · 2025Pooled it
- Tracking Dynamics of Superspreading Through Contacts, Exposures, and Transmissions in Edge-Based Network Epidemics.Bulletin of mathematical biology · 2026Article
- Time-series modeling of epidemics in complex populations: Detecting changes in incidence volatility over time.PLoS computational biology · 2025Article
- Improved estimation of the effective reproduction number with heterogeneous transmission rates and reporting delays.Scientific reports · 2024Article
- Consistent Healthcare Safety Recommendation System for Preventing Contagious Disease Infections in Human Crowds.Sensors (Basel, Switzerland) · 2023Article
- Time-varying overdispersion of SARS-CoV-2 transmission during the periods when different variants of concern were circulating in Japan.Scientific reports · 2023Article
- Dining-Out Behavior as a Proxy for the Superspreading Potential of SARS-CoV-2 Infections: Modeling Analysis.JMIR public health and surveillance · 2023Article
- Are the epidemic prevention facilities effective? How cities should choose epidemic prevention facilities: Taking Wuhan as an example.Frontiers in public health · 2023Article
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Authors and funding
11 authors.
Funding
No grant is acknowledged in the PubMed record.
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.
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