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Parameter estimation of the incubation period of COVID-19 based on the doubly interval-censored data model
With the spread of the novel coronavirus disease 2019 (COVID-19) around the world, the estimation of the incubation period of COVID-19 has become a hot issue. Based on the doubly interval-censored data model, we assume that the incubation period follows lognormal and Gamma distribution, and estimate...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Springer Netherlands
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8211977/ https://www.ncbi.nlm.nih.gov/pubmed/34177117 http://dx.doi.org/10.1007/s11071-021-06587-w |
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author | Yin, Ming-Ze Zhu, Qing-Wen Lü, Xing |
author_facet | Yin, Ming-Ze Zhu, Qing-Wen Lü, Xing |
author_sort | Yin, Ming-Ze |
collection | PubMed |
description | With the spread of the novel coronavirus disease 2019 (COVID-19) around the world, the estimation of the incubation period of COVID-19 has become a hot issue. Based on the doubly interval-censored data model, we assume that the incubation period follows lognormal and Gamma distribution, and estimate the parameters of the incubation period of COVID-19 by adopting the maximum likelihood estimation, expectation maximization algorithm and a newly proposed algorithm (expectation mostly conditional maximization algorithm, referred as ECIMM). The main innovation of this paper lies in two aspects: Firstly, we regard the sample data of the incubation period as the doubly interval-censored data without unnecessary data simplification to improve the accuracy and credibility of the results; secondly, our new ECIMM algorithm enjoys better convergence and universality compared with others. With the framework of this paper, we conclude that 14-day quarantine period can largely interrupt the transmission of COVID-19, however, people who need specially monitoring should be isolated for about 20 days for the sake of safety. The results provide some suggestions for the prevention and control of COVID-19. The newly proposed ECIMM algorithm can also be used to deal with the doubly interval-censored data model appearing in various fields. |
format | Online Article Text |
id | pubmed-8211977 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | Springer Netherlands |
record_format | MEDLINE/PubMed |
spelling | pubmed-82119772021-06-21 Parameter estimation of the incubation period of COVID-19 based on the doubly interval-censored data model Yin, Ming-Ze Zhu, Qing-Wen Lü, Xing Nonlinear Dyn Original Paper With the spread of the novel coronavirus disease 2019 (COVID-19) around the world, the estimation of the incubation period of COVID-19 has become a hot issue. Based on the doubly interval-censored data model, we assume that the incubation period follows lognormal and Gamma distribution, and estimate the parameters of the incubation period of COVID-19 by adopting the maximum likelihood estimation, expectation maximization algorithm and a newly proposed algorithm (expectation mostly conditional maximization algorithm, referred as ECIMM). The main innovation of this paper lies in two aspects: Firstly, we regard the sample data of the incubation period as the doubly interval-censored data without unnecessary data simplification to improve the accuracy and credibility of the results; secondly, our new ECIMM algorithm enjoys better convergence and universality compared with others. With the framework of this paper, we conclude that 14-day quarantine period can largely interrupt the transmission of COVID-19, however, people who need specially monitoring should be isolated for about 20 days for the sake of safety. The results provide some suggestions for the prevention and control of COVID-19. The newly proposed ECIMM algorithm can also be used to deal with the doubly interval-censored data model appearing in various fields. Springer Netherlands 2021-06-18 2021 /pmc/articles/PMC8211977/ /pubmed/34177117 http://dx.doi.org/10.1007/s11071-021-06587-w Text en © The Author(s), under exclusive licence to Springer Nature B.V. 2021 This article is made available via the PMC Open Access Subset for unrestricted research re-use and secondary analysis in any form or by any means with acknowledgement of the original source. These permissions are granted for the duration of the World Health Organization (WHO) declaration of COVID-19 as a global pandemic. |
spellingShingle | Original Paper Yin, Ming-Ze Zhu, Qing-Wen Lü, Xing Parameter estimation of the incubation period of COVID-19 based on the doubly interval-censored data model |
title | Parameter estimation of the incubation period of COVID-19 based on the doubly interval-censored data model |
title_full | Parameter estimation of the incubation period of COVID-19 based on the doubly interval-censored data model |
title_fullStr | Parameter estimation of the incubation period of COVID-19 based on the doubly interval-censored data model |
title_full_unstemmed | Parameter estimation of the incubation period of COVID-19 based on the doubly interval-censored data model |
title_short | Parameter estimation of the incubation period of COVID-19 based on the doubly interval-censored data model |
title_sort | parameter estimation of the incubation period of covid-19 based on the doubly interval-censored data model |
topic | Original Paper |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8211977/ https://www.ncbi.nlm.nih.gov/pubmed/34177117 http://dx.doi.org/10.1007/s11071-021-06587-w |
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