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Stochastic modeling of empirical time series of childhood infectious diseases data before and after mass vaccination
The goal of this paper is to analyze the stochastic dynamics of childhood infectious disease time series. We present an univariate time series analysis of pertussis, mumps, measles and rubella based on Box-Jenkins or AutoRegressive Integrated Moving Average (ARIMA) modeling. The method, which enable...
Autores principales: | , , |
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Formato: | Texto |
Lenguaje: | English |
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BioMed Central
2006
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC1584232/ https://www.ncbi.nlm.nih.gov/pubmed/16895599 http://dx.doi.org/10.1186/1742-7622-3-9 |
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author | Trottier, Helen Philippe, Pierre Roy, Roch |
author_facet | Trottier, Helen Philippe, Pierre Roy, Roch |
author_sort | Trottier, Helen |
collection | PubMed |
description | The goal of this paper is to analyze the stochastic dynamics of childhood infectious disease time series. We present an univariate time series analysis of pertussis, mumps, measles and rubella based on Box-Jenkins or AutoRegressive Integrated Moving Average (ARIMA) modeling. The method, which enables the dependency structure embedded in time series data to be modeled, has potential research applications in studies of infectious disease dynamics. Canadian chronological series of pertussis, mumps, measles and rubella, before and after mass vaccination, are analyzed to characterize the statistical structure of these diseases. Despite the fact that these infectious diseases are biologically different, it is found that they are all represented by simple models with the same basic statistical structure. Aside from seasonal effects, the number of new cases is given by the incidence in the previous period and by periodically recurrent random factors. It is also shown that mass vaccination does not change this stochastic dependency. We conclude that the Box-Jenkins methodology does identify the collective pattern of the dynamics, but not the specifics of the diseases at the biological individual level. |
format | Text |
id | pubmed-1584232 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2006 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-15842322006-10-02 Stochastic modeling of empirical time series of childhood infectious diseases data before and after mass vaccination Trottier, Helen Philippe, Pierre Roy, Roch Emerg Themes Epidemiol Methodology The goal of this paper is to analyze the stochastic dynamics of childhood infectious disease time series. We present an univariate time series analysis of pertussis, mumps, measles and rubella based on Box-Jenkins or AutoRegressive Integrated Moving Average (ARIMA) modeling. The method, which enables the dependency structure embedded in time series data to be modeled, has potential research applications in studies of infectious disease dynamics. Canadian chronological series of pertussis, mumps, measles and rubella, before and after mass vaccination, are analyzed to characterize the statistical structure of these diseases. Despite the fact that these infectious diseases are biologically different, it is found that they are all represented by simple models with the same basic statistical structure. Aside from seasonal effects, the number of new cases is given by the incidence in the previous period and by periodically recurrent random factors. It is also shown that mass vaccination does not change this stochastic dependency. We conclude that the Box-Jenkins methodology does identify the collective pattern of the dynamics, but not the specifics of the diseases at the biological individual level. BioMed Central 2006-08-08 /pmc/articles/PMC1584232/ /pubmed/16895599 http://dx.doi.org/10.1186/1742-7622-3-9 Text en Copyright © 2006 Trottier et al; licensee BioMed Central Ltd. http://creativecommons.org/licenses/by/2.0 This is an Open Access article distributed under the terms of the Creative Commons Attribution License ( (http://creativecommons.org/licenses/by/2.0) ), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. |
spellingShingle | Methodology Trottier, Helen Philippe, Pierre Roy, Roch Stochastic modeling of empirical time series of childhood infectious diseases data before and after mass vaccination |
title | Stochastic modeling of empirical time series of childhood infectious diseases data before and after mass vaccination |
title_full | Stochastic modeling of empirical time series of childhood infectious diseases data before and after mass vaccination |
title_fullStr | Stochastic modeling of empirical time series of childhood infectious diseases data before and after mass vaccination |
title_full_unstemmed | Stochastic modeling of empirical time series of childhood infectious diseases data before and after mass vaccination |
title_short | Stochastic modeling of empirical time series of childhood infectious diseases data before and after mass vaccination |
title_sort | stochastic modeling of empirical time series of childhood infectious diseases data before and after mass vaccination |
topic | Methodology |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC1584232/ https://www.ncbi.nlm.nih.gov/pubmed/16895599 http://dx.doi.org/10.1186/1742-7622-3-9 |
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