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How Modelling Can Enhance the Analysis of Imperfect Epidemic Data

Mathematical models play an increasingly important role in our understanding of the transmission and control of infectious diseases. Here, we present concrete examples illustrating how mathematical models, paired with rigorous statistical methods, are used to parse data of different levels of detail...

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Detalles Bibliográficos
Autores principales: Cauchemez, Simon, Hoze, Nathanaël, Cousien, Anthony, Nikolay, Birgit, ten bosch, Quirine
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Published by Elsevier Ltd. 2019
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7106457/
https://www.ncbi.nlm.nih.gov/pubmed/30738632
http://dx.doi.org/10.1016/j.pt.2019.01.009
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author Cauchemez, Simon
Hoze, Nathanaël
Cousien, Anthony
Nikolay, Birgit
ten bosch, Quirine
author_facet Cauchemez, Simon
Hoze, Nathanaël
Cousien, Anthony
Nikolay, Birgit
ten bosch, Quirine
author_sort Cauchemez, Simon
collection PubMed
description Mathematical models play an increasingly important role in our understanding of the transmission and control of infectious diseases. Here, we present concrete examples illustrating how mathematical models, paired with rigorous statistical methods, are used to parse data of different levels of detail and breadth and estimate key epidemiological parameters (e.g., transmission and its determinants, severity, impact of interventions, drivers of epidemic dynamics) even when these parameters are not directly measurable, when data are limited, and when the epidemic process is only partially observed. Finally, we assess the hurdles to be taken to increase availability and applicability of these approaches in an effort to ultimately enhance their public health impact.
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spelling pubmed-71064572020-03-31 How Modelling Can Enhance the Analysis of Imperfect Epidemic Data Cauchemez, Simon Hoze, Nathanaël Cousien, Anthony Nikolay, Birgit ten bosch, Quirine Trends Parasitol Article Mathematical models play an increasingly important role in our understanding of the transmission and control of infectious diseases. Here, we present concrete examples illustrating how mathematical models, paired with rigorous statistical methods, are used to parse data of different levels of detail and breadth and estimate key epidemiological parameters (e.g., transmission and its determinants, severity, impact of interventions, drivers of epidemic dynamics) even when these parameters are not directly measurable, when data are limited, and when the epidemic process is only partially observed. Finally, we assess the hurdles to be taken to increase availability and applicability of these approaches in an effort to ultimately enhance their public health impact. Published by Elsevier Ltd. 2019-05 2019-02-06 /pmc/articles/PMC7106457/ /pubmed/30738632 http://dx.doi.org/10.1016/j.pt.2019.01.009 Text en © 2019 Published by Elsevier Ltd. 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
Cauchemez, Simon
Hoze, Nathanaël
Cousien, Anthony
Nikolay, Birgit
ten bosch, Quirine
How Modelling Can Enhance the Analysis of Imperfect Epidemic Data
title How Modelling Can Enhance the Analysis of Imperfect Epidemic Data
title_full How Modelling Can Enhance the Analysis of Imperfect Epidemic Data
title_fullStr How Modelling Can Enhance the Analysis of Imperfect Epidemic Data
title_full_unstemmed How Modelling Can Enhance the Analysis of Imperfect Epidemic Data
title_short How Modelling Can Enhance the Analysis of Imperfect Epidemic Data
title_sort how modelling can enhance the analysis of imperfect epidemic data
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7106457/
https://www.ncbi.nlm.nih.gov/pubmed/30738632
http://dx.doi.org/10.1016/j.pt.2019.01.009
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