Cargando…

The mixed trunsored model with applications to SARS

The trunsored model, which is a new incomplete data model regarded as a unified model of the censored and truncated models in lifetime analysis, can not only estimate the ratio of the fragile population to the mixed fragile and durable populations or the cured and fatal mixed populations, but also t...

Descripción completa

Detalles Bibliográficos
Autor principal: Hirose, Hideo
Formato: Online Artículo Texto
Lenguaje:English
Publicado: IMACS. Published by Elsevier Ltd. 2007
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7125585/
https://www.ncbi.nlm.nih.gov/pubmed/32288112
http://dx.doi.org/10.1016/j.matcom.2006.06.031
_version_ 1783515975677640704
author Hirose, Hideo
author_facet Hirose, Hideo
author_sort Hirose, Hideo
collection PubMed
description The trunsored model, which is a new incomplete data model regarded as a unified model of the censored and truncated models in lifetime analysis, can not only estimate the ratio of the fragile population to the mixed fragile and durable populations or the cured and fatal mixed populations, but also test a hypothesis that the ratio is equal to a prescribed value with ease. Since SARS showed a severe case fatality ratio, our concern is to know such a case fatality ratio as soon as possible after a similar outbreak begins. The epidemiological determinants of spread of SARS can be dealt with as the probabilistic growth curve models, and the parameter estimation procedure for the probabilistic growth curve models may similarly be treated as the lifetime analysis. Thus, we try to do the parameter estimation to the SARS cases for the infected cases, fatal cases, and cured cases here, as we usually do it in the lifetime analysis. Using the truncated data models to the infected and fatal cases with some censoring time, we may estimate the total (or final) numbers of the patients and deaths, and the case fatality ratio may be estimated by these two numbers. We may also estimate the case fatality ratio using the numbers of the patients and recoveries, but this estimate differs from that using the numbers of the patients and deaths, especially when the censoring time is located at early stages. To circumvent this inconsistency, we propose a mixed trunsored model, an extension of the trunsored model, which can use the data of the patients, deaths, and recoveries simultaneously. The estimate of the case fatality ratio and its confidence interval are easily obtained in a numerical sense. This paper mainly treats the case in Hong Kong. The estimated epidemiological determinants of spread of SARS, fitted to the infected, fatal, and cured cases in Hong Kong, could be the logistic distribution function among the logistic, log-normal, gamma, and Weibull models. Using the proposed method, it would be appropriate that the SARS case fatality ratio is roughly estimated to be 17% in Hong Kong. Worldwide, it is roughly estimated to be about 12–18%, if we consider the safety side without the Chinese case. Unlike the questionably small confidence intervals for the case fatality ratio using the truncated models, the case fatality ratio in the proposed model provides a reasonable confidence interval.
format Online
Article
Text
id pubmed-7125585
institution National Center for Biotechnology Information
language English
publishDate 2007
publisher IMACS. Published by Elsevier Ltd.
record_format MEDLINE/PubMed
spelling pubmed-71255852020-04-08 The mixed trunsored model with applications to SARS Hirose, Hideo Math Comput Simul Article The trunsored model, which is a new incomplete data model regarded as a unified model of the censored and truncated models in lifetime analysis, can not only estimate the ratio of the fragile population to the mixed fragile and durable populations or the cured and fatal mixed populations, but also test a hypothesis that the ratio is equal to a prescribed value with ease. Since SARS showed a severe case fatality ratio, our concern is to know such a case fatality ratio as soon as possible after a similar outbreak begins. The epidemiological determinants of spread of SARS can be dealt with as the probabilistic growth curve models, and the parameter estimation procedure for the probabilistic growth curve models may similarly be treated as the lifetime analysis. Thus, we try to do the parameter estimation to the SARS cases for the infected cases, fatal cases, and cured cases here, as we usually do it in the lifetime analysis. Using the truncated data models to the infected and fatal cases with some censoring time, we may estimate the total (or final) numbers of the patients and deaths, and the case fatality ratio may be estimated by these two numbers. We may also estimate the case fatality ratio using the numbers of the patients and recoveries, but this estimate differs from that using the numbers of the patients and deaths, especially when the censoring time is located at early stages. To circumvent this inconsistency, we propose a mixed trunsored model, an extension of the trunsored model, which can use the data of the patients, deaths, and recoveries simultaneously. The estimate of the case fatality ratio and its confidence interval are easily obtained in a numerical sense. This paper mainly treats the case in Hong Kong. The estimated epidemiological determinants of spread of SARS, fitted to the infected, fatal, and cured cases in Hong Kong, could be the logistic distribution function among the logistic, log-normal, gamma, and Weibull models. Using the proposed method, it would be appropriate that the SARS case fatality ratio is roughly estimated to be 17% in Hong Kong. Worldwide, it is roughly estimated to be about 12–18%, if we consider the safety side without the Chinese case. Unlike the questionably small confidence intervals for the case fatality ratio using the truncated models, the case fatality ratio in the proposed model provides a reasonable confidence interval. IMACS. Published by Elsevier Ltd. 2007-04-30 2006-10-02 /pmc/articles/PMC7125585/ /pubmed/32288112 http://dx.doi.org/10.1016/j.matcom.2006.06.031 Text en Copyright © 2006 IMACS. Published by Elsevier Ltd. All rights reserved. Since January 2020 Elsevier has created a COVID-19 resource centre with free information in English and Mandarin on the novel coronavirus COVID-19. The COVID-19 resource centre is hosted on Elsevier Connect, the company's public news and information website. Elsevier hereby grants permission to make all its COVID-19-related research that is available on the COVID-19 resource centre - including this research content - immediately available in PubMed Central and other publicly funded repositories, such as the WHO COVID database with rights for unrestricted research re-use and analyses in any form or by any means with acknowledgement of the original source. These permissions are granted for free by Elsevier for as long as the COVID-19 resource centre remains active.
spellingShingle Article
Hirose, Hideo
The mixed trunsored model with applications to SARS
title The mixed trunsored model with applications to SARS
title_full The mixed trunsored model with applications to SARS
title_fullStr The mixed trunsored model with applications to SARS
title_full_unstemmed The mixed trunsored model with applications to SARS
title_short The mixed trunsored model with applications to SARS
title_sort mixed trunsored model with applications to sars
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7125585/
https://www.ncbi.nlm.nih.gov/pubmed/32288112
http://dx.doi.org/10.1016/j.matcom.2006.06.031
work_keys_str_mv AT hirosehideo themixedtrunsoredmodelwithapplicationstosars
AT hirosehideo mixedtrunsoredmodelwithapplicationstosars