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Tooling-up for infectious disease transmission modelling
In this introduction to the Special Issue on methods for modelling of infectious disease epidemiology we provide a commentary and overview of the field. We suggest that the field has been through three revolutions that have focussed on specific methodological developments; disease dynamics and heter...
Autores principales: | , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Published by Elsevier B.V.
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7219405/ https://www.ncbi.nlm.nih.gov/pubmed/32405321 http://dx.doi.org/10.1016/j.epidem.2020.100395 |
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author | Baguelin, Marc Medley, Graham F. Nightingale, Emily S. O’Reilly, Kathleen M. Rees, Eleanor M. Waterlow, Naomi R. Wagner, Moritz |
author_facet | Baguelin, Marc Medley, Graham F. Nightingale, Emily S. O’Reilly, Kathleen M. Rees, Eleanor M. Waterlow, Naomi R. Wagner, Moritz |
author_sort | Baguelin, Marc |
collection | PubMed |
description | In this introduction to the Special Issue on methods for modelling of infectious disease epidemiology we provide a commentary and overview of the field. We suggest that the field has been through three revolutions that have focussed on specific methodological developments; disease dynamics and heterogeneity, advanced computing and inference, and complexity and application to the real-world. Infectious disease dynamics and heterogeneity dominated until the 1980s where the use of analytical models illustrated fundamental concepts such as herd immunity. The second revolution embraced the integration of data with models and the increased use of computing. From the turn of the century an emergence of novel datasets enabled improved modelling of real-world complexity. The emergence of more complex data that reflect the real-world heterogeneities in transmission resulted in the development of improved inference methods such as particle filtering. Each of these three revolutions have always kept the understanding of infectious disease spread as its motivation but have been developed through the use of new techniques, tools and the availability of data. We conclude by providing a commentary on what the next revoluition in infectious disease modelling may be. |
format | Online Article Text |
id | pubmed-7219405 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | Published by Elsevier B.V. |
record_format | MEDLINE/PubMed |
spelling | pubmed-72194052020-05-13 Tooling-up for infectious disease transmission modelling Baguelin, Marc Medley, Graham F. Nightingale, Emily S. O’Reilly, Kathleen M. Rees, Eleanor M. Waterlow, Naomi R. Wagner, Moritz Epidemics Article In this introduction to the Special Issue on methods for modelling of infectious disease epidemiology we provide a commentary and overview of the field. We suggest that the field has been through three revolutions that have focussed on specific methodological developments; disease dynamics and heterogeneity, advanced computing and inference, and complexity and application to the real-world. Infectious disease dynamics and heterogeneity dominated until the 1980s where the use of analytical models illustrated fundamental concepts such as herd immunity. The second revolution embraced the integration of data with models and the increased use of computing. From the turn of the century an emergence of novel datasets enabled improved modelling of real-world complexity. The emergence of more complex data that reflect the real-world heterogeneities in transmission resulted in the development of improved inference methods such as particle filtering. Each of these three revolutions have always kept the understanding of infectious disease spread as its motivation but have been developed through the use of new techniques, tools and the availability of data. We conclude by providing a commentary on what the next revoluition in infectious disease modelling may be. Published by Elsevier B.V. 2020-09 2020-05-13 /pmc/articles/PMC7219405/ /pubmed/32405321 http://dx.doi.org/10.1016/j.epidem.2020.100395 Text en © 2020 Published by Elsevier B.V. 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 Baguelin, Marc Medley, Graham F. Nightingale, Emily S. O’Reilly, Kathleen M. Rees, Eleanor M. Waterlow, Naomi R. Wagner, Moritz Tooling-up for infectious disease transmission modelling |
title | Tooling-up for infectious disease transmission modelling |
title_full | Tooling-up for infectious disease transmission modelling |
title_fullStr | Tooling-up for infectious disease transmission modelling |
title_full_unstemmed | Tooling-up for infectious disease transmission modelling |
title_short | Tooling-up for infectious disease transmission modelling |
title_sort | tooling-up for infectious disease transmission modelling |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7219405/ https://www.ncbi.nlm.nih.gov/pubmed/32405321 http://dx.doi.org/10.1016/j.epidem.2020.100395 |
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