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Mathematical models of infectious disease transmission
Mathematical analysis and modelling is central to infectious disease epidemiology. Here, we provide an intuitive introduction to the process of disease transmission, how this stochastic process can be represented mathematically and how this mathematical representation can be used to analyse the emer...
Autores principales: | , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2008
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7097581/ https://www.ncbi.nlm.nih.gov/pubmed/18533288 http://dx.doi.org/10.1038/nrmicro1845 |
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author | Grassly, Nicholas C. Fraser, Christophe |
author_facet | Grassly, Nicholas C. Fraser, Christophe |
author_sort | Grassly, Nicholas C. |
collection | PubMed |
description | Mathematical analysis and modelling is central to infectious disease epidemiology. Here, we provide an intuitive introduction to the process of disease transmission, how this stochastic process can be represented mathematically and how this mathematical representation can be used to analyse the emergent dynamics of observed epidemics. Progress in mathematical analysis and modelling is of fundamental importance to our growing understanding of pathogen evolution and ecology. The fit of mathematical models to surveillance data has informed both scientific research and health policy. This Review is illustrated throughout by such applications and ends with suggestions of open challenges in mathematical epidemiology. |
format | Online Article Text |
id | pubmed-7097581 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2008 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
spelling | pubmed-70975812020-03-26 Mathematical models of infectious disease transmission Grassly, Nicholas C. Fraser, Christophe Nat Rev Microbiol Article Mathematical analysis and modelling is central to infectious disease epidemiology. Here, we provide an intuitive introduction to the process of disease transmission, how this stochastic process can be represented mathematically and how this mathematical representation can be used to analyse the emergent dynamics of observed epidemics. Progress in mathematical analysis and modelling is of fundamental importance to our growing understanding of pathogen evolution and ecology. The fit of mathematical models to surveillance data has informed both scientific research and health policy. This Review is illustrated throughout by such applications and ends with suggestions of open challenges in mathematical epidemiology. Nature Publishing Group UK 2008-05-13 2008 /pmc/articles/PMC7097581/ /pubmed/18533288 http://dx.doi.org/10.1038/nrmicro1845 Text en © Nature Publishing Group 2008 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 | Article Grassly, Nicholas C. Fraser, Christophe Mathematical models of infectious disease transmission |
title | Mathematical models of infectious disease transmission |
title_full | Mathematical models of infectious disease transmission |
title_fullStr | Mathematical models of infectious disease transmission |
title_full_unstemmed | Mathematical models of infectious disease transmission |
title_short | Mathematical models of infectious disease transmission |
title_sort | mathematical models of infectious disease transmission |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7097581/ https://www.ncbi.nlm.nih.gov/pubmed/18533288 http://dx.doi.org/10.1038/nrmicro1845 |
work_keys_str_mv | AT grasslynicholasc mathematicalmodelsofinfectiousdiseasetransmission AT fraserchristophe mathematicalmodelsofinfectiousdiseasetransmission |