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Modeling the effect of age on quantiles of the incubation period distribution of COVID-19
BACKGROUND: The novel coronavirus SARS-CoV-2 (coronavirus disease 2019, COVID-19) has caused serious consequences on many aspects of social life throughout the world since the first case of pneumonia with unknown etiology was identified in Wuhan, Hubei province in China in December 2019. Note that t...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
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BioMed Central
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8474900/ https://www.ncbi.nlm.nih.gov/pubmed/34579681 http://dx.doi.org/10.1186/s12889-021-11761-1 |
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author | Liu, Xiaohui Wang, Lei Ma, Xiansi Wang, Jiewen Wu, Liwen |
author_facet | Liu, Xiaohui Wang, Lei Ma, Xiansi Wang, Jiewen Wu, Liwen |
author_sort | Liu, Xiaohui |
collection | PubMed |
description | BACKGROUND: The novel coronavirus SARS-CoV-2 (coronavirus disease 2019, COVID-19) has caused serious consequences on many aspects of social life throughout the world since the first case of pneumonia with unknown etiology was identified in Wuhan, Hubei province in China in December 2019. Note that the incubation period distribution is key to the prevention and control efforts of COVID-19. This study aimed to investigate the conditional distribution of the incubation period of COVID-19 given the age of infected cases and estimate its corresponding quantiles from the information of 2172 confirmed cases from 29 provinces outside Hubei in China. METHODS: We collected data on the infection dates, onset dates, and ages of the confirmed cases through February 16th, 2020. All the data were downloaded from the official websites of the health commission. As the epidemic was still ongoing at the time we collected data, the observations subject to biased sampling. To address this issue, we developed a new maximum likelihood method, which enables us to comprehensively study the effect of age on the incubation period. RESULTS: Based on the collected data, we found that the conditional quantiles of the incubation period distribution of COVID-19 vary by age. In detail, the high conditional quantiles of people in the middle age group are shorter than those of others while the low quantiles did not show the same differences. We estimated that the 0.95-th quantile related to people in the age group 23 ∼55 is less than 15 days. CONCLUSIONS: Observing that the conditional quantiles vary across age, we may take more precise measures for people of different ages. For example, we may consider carrying out an age-dependent quarantine duration in practice, rather than a uniform 14-days quarantine period. Remarkably, we may need to extend the current quarantine duration for people aged 0 ∼22 and over 55 because the related 0.95-th quantiles are much greater than 14 days. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at (10.1186/s12889-021-11761-1). |
format | Online Article Text |
id | pubmed-8474900 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-84749002021-09-28 Modeling the effect of age on quantiles of the incubation period distribution of COVID-19 Liu, Xiaohui Wang, Lei Ma, Xiansi Wang, Jiewen Wu, Liwen BMC Public Health Research Article BACKGROUND: The novel coronavirus SARS-CoV-2 (coronavirus disease 2019, COVID-19) has caused serious consequences on many aspects of social life throughout the world since the first case of pneumonia with unknown etiology was identified in Wuhan, Hubei province in China in December 2019. Note that the incubation period distribution is key to the prevention and control efforts of COVID-19. This study aimed to investigate the conditional distribution of the incubation period of COVID-19 given the age of infected cases and estimate its corresponding quantiles from the information of 2172 confirmed cases from 29 provinces outside Hubei in China. METHODS: We collected data on the infection dates, onset dates, and ages of the confirmed cases through February 16th, 2020. All the data were downloaded from the official websites of the health commission. As the epidemic was still ongoing at the time we collected data, the observations subject to biased sampling. To address this issue, we developed a new maximum likelihood method, which enables us to comprehensively study the effect of age on the incubation period. RESULTS: Based on the collected data, we found that the conditional quantiles of the incubation period distribution of COVID-19 vary by age. In detail, the high conditional quantiles of people in the middle age group are shorter than those of others while the low quantiles did not show the same differences. We estimated that the 0.95-th quantile related to people in the age group 23 ∼55 is less than 15 days. CONCLUSIONS: Observing that the conditional quantiles vary across age, we may take more precise measures for people of different ages. For example, we may consider carrying out an age-dependent quarantine duration in practice, rather than a uniform 14-days quarantine period. Remarkably, we may need to extend the current quarantine duration for people aged 0 ∼22 and over 55 because the related 0.95-th quantiles are much greater than 14 days. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at (10.1186/s12889-021-11761-1). BioMed Central 2021-09-27 /pmc/articles/PMC8474900/ /pubmed/34579681 http://dx.doi.org/10.1186/s12889-021-11761-1 Text en © The Author(s) 2021 https://creativecommons.org/licenses/by/4.0/Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/ (https://creativecommons.org/publicdomain/zero/1.0/) ) applies to the data made available in this article, unless otherwise stated in a credit line to the data. |
spellingShingle | Research Article Liu, Xiaohui Wang, Lei Ma, Xiansi Wang, Jiewen Wu, Liwen Modeling the effect of age on quantiles of the incubation period distribution of COVID-19 |
title | Modeling the effect of age on quantiles of the incubation period distribution of COVID-19 |
title_full | Modeling the effect of age on quantiles of the incubation period distribution of COVID-19 |
title_fullStr | Modeling the effect of age on quantiles of the incubation period distribution of COVID-19 |
title_full_unstemmed | Modeling the effect of age on quantiles of the incubation period distribution of COVID-19 |
title_short | Modeling the effect of age on quantiles of the incubation period distribution of COVID-19 |
title_sort | modeling the effect of age on quantiles of the incubation period distribution of covid-19 |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8474900/ https://www.ncbi.nlm.nih.gov/pubmed/34579681 http://dx.doi.org/10.1186/s12889-021-11761-1 |
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