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Modelling microbial infection to address global health challenges
The continued growth of the world’s population and increased interconnectivity heighten the risk that infectious diseases pose for human health worldwide. Epidemiological modelling is a tool that can be used to mitigate this risk by predicting disease spread or quantifying the impact of different in...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2019
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6800015/ https://www.ncbi.nlm.nih.gov/pubmed/31541212 http://dx.doi.org/10.1038/s41564-019-0565-8 |
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author | Fitzpatrick, Meagan C. Bauch, Chris T. Townsend, Jeffrey P. Galvani, Alison P. |
author_facet | Fitzpatrick, Meagan C. Bauch, Chris T. Townsend, Jeffrey P. Galvani, Alison P. |
author_sort | Fitzpatrick, Meagan C. |
collection | PubMed |
description | The continued growth of the world’s population and increased interconnectivity heighten the risk that infectious diseases pose for human health worldwide. Epidemiological modelling is a tool that can be used to mitigate this risk by predicting disease spread or quantifying the impact of different intervention strategies on disease transmission dynamics. We illustrate how four decades of methodological advances and improved data quality have facilitated the contribution of modelling to address global health challenges, exemplified by models for the HIV crisis, emerging pathogens and pandemic preparedness. Throughout, we discuss the importance of designing a model that is appropriate to the research question and the available data. We highlight pitfalls that can arise in model development, validation and interpretation. Close collaboration between empiricists and modellers continues to improve the accuracy of predictions and the optimization of models for public health decision-making. |
format | Online Article Text |
id | pubmed-6800015 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2019 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
spelling | pubmed-68000152020-03-26 Modelling microbial infection to address global health challenges Fitzpatrick, Meagan C. Bauch, Chris T. Townsend, Jeffrey P. Galvani, Alison P. Nat Microbiol Perspective The continued growth of the world’s population and increased interconnectivity heighten the risk that infectious diseases pose for human health worldwide. Epidemiological modelling is a tool that can be used to mitigate this risk by predicting disease spread or quantifying the impact of different intervention strategies on disease transmission dynamics. We illustrate how four decades of methodological advances and improved data quality have facilitated the contribution of modelling to address global health challenges, exemplified by models for the HIV crisis, emerging pathogens and pandemic preparedness. Throughout, we discuss the importance of designing a model that is appropriate to the research question and the available data. We highlight pitfalls that can arise in model development, validation and interpretation. Close collaboration between empiricists and modellers continues to improve the accuracy of predictions and the optimization of models for public health decision-making. Nature Publishing Group UK 2019-09-20 2019 /pmc/articles/PMC6800015/ /pubmed/31541212 http://dx.doi.org/10.1038/s41564-019-0565-8 Text en © Springer Nature Limited 2019 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 | Perspective Fitzpatrick, Meagan C. Bauch, Chris T. Townsend, Jeffrey P. Galvani, Alison P. Modelling microbial infection to address global health challenges |
title | Modelling microbial infection to address global health challenges |
title_full | Modelling microbial infection to address global health challenges |
title_fullStr | Modelling microbial infection to address global health challenges |
title_full_unstemmed | Modelling microbial infection to address global health challenges |
title_short | Modelling microbial infection to address global health challenges |
title_sort | modelling microbial infection to address global health challenges |
topic | Perspective |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6800015/ https://www.ncbi.nlm.nih.gov/pubmed/31541212 http://dx.doi.org/10.1038/s41564-019-0565-8 |
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