Estimating the Epidemic Size of Superspreading Coronavirus Outbreaks in Real Time: Quantitative Study.
JMIR Public Health Surveill
; 10: e46687, 2024 Feb 12.
Article
in En
| MEDLINE
| ID: mdl-38345850
ABSTRACT
BACKGROUND:
Novel coronaviruses have emerged and caused major epidemics and pandemics in the past 2 decades, including SARS-CoV-1, MERS-CoV, and SARS-CoV-2, which led to the current COVID-19 pandemic. These coronaviruses are marked by their potential to produce disproportionally large transmission clusters from superspreading events (SSEs). As prompt action is crucial to contain and mitigate SSEs, real-time epidemic size estimation could characterize the transmission heterogeneity and inform timely implementation of control measures.OBJECTIVE:
This study aimed to estimate the epidemic size of SSEs to inform effective surveillance and rapid mitigation responses.METHODS:
We developed a statistical framework based on back-calculation to estimate the epidemic size of ongoing coronavirus SSEs. We first validated the framework in simulated scenarios with the epidemiological characteristics of SARS, MERS, and COVID-19 SSEs. As case studies, we retrospectively applied the framework to the Amoy Gardens SARS outbreak in Hong Kong in 2003, a series of nosocomial MERS outbreaks in South Korea in 2015, and 2 COVID-19 outbreaks originating from restaurants in Hong Kong in 2020.RESULTS:
The accuracy and precision of the estimation of epidemic size of SSEs improved with longer observation time; larger SSE size; and more accurate prior information about the epidemiological characteristics, such as the distribution of the incubation period and the distribution of the onset-to-confirmation delay. By retrospectively applying the framework, we found that the 95% credible interval of the estimates contained the true epidemic size after 37% of cases were reported in the Amoy Garden SARS SSE in Hong Kong, 41% to 62% of cases were observed in the 3 nosocomial MERS SSEs in South Korea, and 76% to 86% of cases were confirmed in the 2 COVID-19 SSEs in Hong Kong.CONCLUSIONS:
Our framework can be readily integrated into coronavirus surveillance systems to enhance situation awareness of ongoing SSEs.Key words
Full text:
1
Collection:
01-internacional
Database:
MEDLINE
Main subject:
Cross Infection
/
COVID-19
Type of study:
Observational_studies
/
Risk_factors_studies
Limits:
Humans
Language:
En
Journal:
JMIR Public Health Surveill
Year:
2024
Document type:
Article
Country of publication:
Canada