Estimating the Epidemic Size of Superspreading Coronavirus Outbreaks in Real Time: Quantitative Study.

Publication date: Feb 12, 2024

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. This study aimed to estimate the epidemic size of SSEs to inform effective surveillance and rapid mitigation responses. 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. 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. Our framework can be readily integrated into coronavirus surveillance systems to enhance situation awareness of ongoing SSEs.

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Concepts Keywords
Accurate coronavirus
Coronaviruses coronavirus disease 2019
Decades COVID-19
Pandemic epidemic size
Restaurants MERS
SARS
SSE
superspreading event

Semantics

Type Source Name
disease VO time
disease MESH COVID-19 pandemic
disease VO effective
disease MESH severe acute respiratory syndrome
disease MESH Middle East respiratory syndrome
disease IDO host
disease IDO symptom
disease IDO contact tracing
disease VO population
disease MESH infection
disease MESH asymptomatic infections
drug DRUGBANK Spinosad
drug DRUGBANK L-Tyrosine
drug DRUGBANK Troleandomycin
disease MESH uncertainty
disease MESH secondary infection
drug DRUGBANK Medical air
drug DRUGBANK Etoperidone
disease VO report
disease VO organization
drug DRUGBANK Guanosine
disease MESH viral shedding
disease IDO infectivity
disease IDO susceptibility
disease MESH infectious diseases
disease MESH legionellosis
pathway KEGG Legionellosis
disease MESH Viral diseases
disease IDO intervention
drug DRUGBANK Coenzyme M

Original Article

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