Estimating the Epidemic Size of Superspreading Coronavirus Outbreaks in Real Time : Quantitative Study
©Kitty Y Lau, Jian Kang, Minah Park, Gabriel Leung, Joseph T Wu, Kathy Leung. Originally published in JMIR Public Health and Surveillance (https://publichealth.jmir.org), 12.02.2024..
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.
| Medienart: |
E-Artikel |
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| Erscheinungsjahr: |
2024 |
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| Erschienen: |
2024 |
| Enthalten in: |
Zur Gesamtaufnahme - volume:10 |
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| Enthalten in: |
JMIR public health and surveillance - 10(2024) vom: 12. Feb., Seite e46687 |
| Sprache: |
Englisch |
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| Beteiligte Personen: |
Lau, Kitty Y [VerfasserIn] |
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| Links: |
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| Themen: |
COVID-19 |
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| Anmerkungen: |
Date Completed 14.02.2024 Date Revised 15.02.2024 published: Electronic Citation Status MEDLINE |
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| doi: |
10.2196/46687 |
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| funding: |
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| Förderinstitution / Projekttitel: |
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NLM368359905 |
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| 520 | |a ©Kitty Y Lau, Jian Kang, Minah Park, Gabriel Leung, Joseph T Wu, Kathy Leung. Originally published in JMIR Public Health and Surveillance (https://publichealth.jmir.org), 12.02.2024. | ||
| 520 | |a 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 | ||
| 520 | |a OBJECTIVE: This study aimed to estimate the epidemic size of SSEs to inform effective surveillance and rapid mitigation responses | ||
| 520 | |a 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 | ||
| 520 | |a 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 | ||
| 520 | |a CONCLUSIONS: Our framework can be readily integrated into coronavirus surveillance systems to enhance situation awareness of ongoing SSEs | ||
| 650 | 4 | |a Journal Article | |
| 650 | 4 | |a COVID-19 | |
| 650 | 4 | |a MERS | |
| 650 | 4 | |a Middle East respiratory syndrome | |
| 650 | 4 | |a SARS | |
| 650 | 4 | |a SSE | |
| 650 | 4 | |a coronavirus | |
| 650 | 4 | |a coronavirus disease 2019 | |
| 650 | 4 | |a epidemic size | |
| 650 | 4 | |a severe acute respiratory syndrome | |
| 650 | 4 | |a superspreading event | |
| 700 | 1 | |a Kang, Jian |e verfasserin |4 aut | |
| 700 | 1 | |a Park, Minah |e verfasserin |4 aut | |
| 700 | 1 | |a Leung, Gabriel |e verfasserin |4 aut | |
| 700 | 1 | |a Wu, Joseph T |e verfasserin |4 aut | |
| 700 | 1 | |a Leung, Kathy |e verfasserin |4 aut | |
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