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Five challenges for spatial epidemic models
Infectious disease incidence data are increasingly available at the level of the individual and include high-resolution spatial components. Therefore, we are now better able to challenge models that explicitly represent space. Here, we consider five topics within spatial disease dynamics: the constr...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Elsevier
2015
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4383807/ https://www.ncbi.nlm.nih.gov/pubmed/25843387 http://dx.doi.org/10.1016/j.epidem.2014.07.001 |
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author | Riley, Steven Eames, Ken Isham, Valerie Mollison, Denis Trapman, Pieter |
author_facet | Riley, Steven Eames, Ken Isham, Valerie Mollison, Denis Trapman, Pieter |
author_sort | Riley, Steven |
collection | PubMed |
description | Infectious disease incidence data are increasingly available at the level of the individual and include high-resolution spatial components. Therefore, we are now better able to challenge models that explicitly represent space. Here, we consider five topics within spatial disease dynamics: the construction of network models; characterising threshold behaviour; modelling long-distance interactions; the appropriate scale for interventions; and the representation of population heterogeneity. |
format | Online Article Text |
id | pubmed-4383807 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2015 |
publisher | Elsevier |
record_format | MEDLINE/PubMed |
spelling | pubmed-43838072015-04-07 Five challenges for spatial epidemic models Riley, Steven Eames, Ken Isham, Valerie Mollison, Denis Trapman, Pieter Epidemics Article Infectious disease incidence data are increasingly available at the level of the individual and include high-resolution spatial components. Therefore, we are now better able to challenge models that explicitly represent space. Here, we consider five topics within spatial disease dynamics: the construction of network models; characterising threshold behaviour; modelling long-distance interactions; the appropriate scale for interventions; and the representation of population heterogeneity. Elsevier 2015-03 /pmc/articles/PMC4383807/ /pubmed/25843387 http://dx.doi.org/10.1016/j.epidem.2014.07.001 Text en © 2014 The Authors http://creativecommons.org/licenses/by-nc-nd/3.0/ This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/3.0/). |
spellingShingle | Article Riley, Steven Eames, Ken Isham, Valerie Mollison, Denis Trapman, Pieter Five challenges for spatial epidemic models |
title | Five challenges for spatial epidemic models |
title_full | Five challenges for spatial epidemic models |
title_fullStr | Five challenges for spatial epidemic models |
title_full_unstemmed | Five challenges for spatial epidemic models |
title_short | Five challenges for spatial epidemic models |
title_sort | five challenges for spatial epidemic models |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4383807/ https://www.ncbi.nlm.nih.gov/pubmed/25843387 http://dx.doi.org/10.1016/j.epidem.2014.07.001 |
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