tetano
Editor, Senior Moderator
JMIR Public Health Surveill
. 2024 Feb 12:10:e46687.
doi: 10.2196/46687. Estimating the Epidemic Size of Superspreading Coronavirus Outbreaks in Real Time: Quantitative Study
Kitty Y Lau[SUP] 1 2 [/SUP], Jian Kang[SUP] 2 [/SUP], Minah Park[SUP] 3 [/SUP], Gabriel Leung[SUP] 1 2 [/SUP], Joseph T Wu[SUP] 1 2 4 [/SUP], Kathy Leung[SUP] 1 2 4 [/SUP]
Affiliations
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.
Keywords: COVID-19; MERS; Middle East respiratory syndrome; SARS; SSE; coronavirus; coronavirus disease 2019; epidemic size; severe acute respiratory syndrome; superspreading event.
. 2024 Feb 12:10:e46687.
doi: 10.2196/46687. Estimating the Epidemic Size of Superspreading Coronavirus Outbreaks in Real Time: Quantitative Study
Kitty Y Lau[SUP] 1 2 [/SUP], Jian Kang[SUP] 2 [/SUP], Minah Park[SUP] 3 [/SUP], Gabriel Leung[SUP] 1 2 [/SUP], Joseph T Wu[SUP] 1 2 4 [/SUP], Kathy Leung[SUP] 1 2 4 [/SUP]
Affiliations
- PMID: 38345850
- DOI: 10.2196/46687
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.
Keywords: COVID-19; MERS; Middle East respiratory syndrome; SARS; SSE; coronavirus; coronavirus disease 2019; epidemic size; severe acute respiratory syndrome; superspreading event.