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Real-Time Estimation of the Risk of Death from Novel Coronavirus (COVID-19) Infection: Inference Using Exported Cases

The exported cases of 2019 novel coronavirus (COVID-19) infection that were confirmed outside China provide an opportunity to estimate the cumulative incidence and confirmed case fatality risk (cCFR) in mainland China. Knowledge of the cCFR is critical to characterize the severity and understand the...

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Autores principales: Jung, Sung-mok, Akhmetzhanov, Andrei R., Hayashi, Katsuma, Linton, Natalie M., Yang, Yichi, Yuan, Baoyin, Kobayashi, Tetsuro, Kinoshita, Ryo, Nishiura, Hiroshi
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7074479/
https://www.ncbi.nlm.nih.gov/pubmed/32075152
http://dx.doi.org/10.3390/jcm9020523
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author Jung, Sung-mok
Akhmetzhanov, Andrei R.
Hayashi, Katsuma
Linton, Natalie M.
Yang, Yichi
Yuan, Baoyin
Kobayashi, Tetsuro
Kinoshita, Ryo
Nishiura, Hiroshi
author_facet Jung, Sung-mok
Akhmetzhanov, Andrei R.
Hayashi, Katsuma
Linton, Natalie M.
Yang, Yichi
Yuan, Baoyin
Kobayashi, Tetsuro
Kinoshita, Ryo
Nishiura, Hiroshi
author_sort Jung, Sung-mok
collection PubMed
description The exported cases of 2019 novel coronavirus (COVID-19) infection that were confirmed outside China provide an opportunity to estimate the cumulative incidence and confirmed case fatality risk (cCFR) in mainland China. Knowledge of the cCFR is critical to characterize the severity and understand the pandemic potential of COVID-19 in the early stage of the epidemic. Using the exponential growth rate of the incidence, the present study statistically estimated the cCFR and the basic reproduction number—the average number of secondary cases generated by a single primary case in a naïve population. We modeled epidemic growth either from a single index case with illness onset on 8 December 2019 (Scenario 1), or using the growth rate fitted along with the other parameters (Scenario 2) based on data from 20 exported cases reported by 24 January 2020. The cumulative incidence in China by 24 January was estimated at 6924 cases (95% confidence interval [CI]: 4885, 9211) and 19,289 cases (95% CI: 10,901, 30,158), respectively. The latest estimated values of the cCFR were 5.3% (95% CI: 3.5%, 7.5%) for Scenario 1 and 8.4% (95% CI: 5.3%, 12.3%) for Scenario 2. The basic reproduction number was estimated to be 2.1 (95% CI: 2.0, 2.2) and 3.2 (95% CI: 2.7, 3.7) for Scenarios 1 and 2, respectively. Based on these results, we argued that the current COVID-19 epidemic has a substantial potential for causing a pandemic. The proposed approach provides insights in early risk assessment using publicly available data.
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spelling pubmed-70744792020-03-20 Real-Time Estimation of the Risk of Death from Novel Coronavirus (COVID-19) Infection: Inference Using Exported Cases Jung, Sung-mok Akhmetzhanov, Andrei R. Hayashi, Katsuma Linton, Natalie M. Yang, Yichi Yuan, Baoyin Kobayashi, Tetsuro Kinoshita, Ryo Nishiura, Hiroshi J Clin Med Article The exported cases of 2019 novel coronavirus (COVID-19) infection that were confirmed outside China provide an opportunity to estimate the cumulative incidence and confirmed case fatality risk (cCFR) in mainland China. Knowledge of the cCFR is critical to characterize the severity and understand the pandemic potential of COVID-19 in the early stage of the epidemic. Using the exponential growth rate of the incidence, the present study statistically estimated the cCFR and the basic reproduction number—the average number of secondary cases generated by a single primary case in a naïve population. We modeled epidemic growth either from a single index case with illness onset on 8 December 2019 (Scenario 1), or using the growth rate fitted along with the other parameters (Scenario 2) based on data from 20 exported cases reported by 24 January 2020. The cumulative incidence in China by 24 January was estimated at 6924 cases (95% confidence interval [CI]: 4885, 9211) and 19,289 cases (95% CI: 10,901, 30,158), respectively. The latest estimated values of the cCFR were 5.3% (95% CI: 3.5%, 7.5%) for Scenario 1 and 8.4% (95% CI: 5.3%, 12.3%) for Scenario 2. The basic reproduction number was estimated to be 2.1 (95% CI: 2.0, 2.2) and 3.2 (95% CI: 2.7, 3.7) for Scenarios 1 and 2, respectively. Based on these results, we argued that the current COVID-19 epidemic has a substantial potential for causing a pandemic. The proposed approach provides insights in early risk assessment using publicly available data. MDPI 2020-02-14 /pmc/articles/PMC7074479/ /pubmed/32075152 http://dx.doi.org/10.3390/jcm9020523 Text en © 2020 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Jung, Sung-mok
Akhmetzhanov, Andrei R.
Hayashi, Katsuma
Linton, Natalie M.
Yang, Yichi
Yuan, Baoyin
Kobayashi, Tetsuro
Kinoshita, Ryo
Nishiura, Hiroshi
Real-Time Estimation of the Risk of Death from Novel Coronavirus (COVID-19) Infection: Inference Using Exported Cases
title Real-Time Estimation of the Risk of Death from Novel Coronavirus (COVID-19) Infection: Inference Using Exported Cases
title_full Real-Time Estimation of the Risk of Death from Novel Coronavirus (COVID-19) Infection: Inference Using Exported Cases
title_fullStr Real-Time Estimation of the Risk of Death from Novel Coronavirus (COVID-19) Infection: Inference Using Exported Cases
title_full_unstemmed Real-Time Estimation of the Risk of Death from Novel Coronavirus (COVID-19) Infection: Inference Using Exported Cases
title_short Real-Time Estimation of the Risk of Death from Novel Coronavirus (COVID-19) Infection: Inference Using Exported Cases
title_sort real-time estimation of the risk of death from novel coronavirus (covid-19) infection: inference using exported cases
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7074479/
https://www.ncbi.nlm.nih.gov/pubmed/32075152
http://dx.doi.org/10.3390/jcm9020523
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