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Opportunities and challenges of Integral Projection Models for modelling host–parasite dynamics

1. Epidemiological dynamics are shaped by and may in turn shape host demography. These feedbacks can result in hard to predict patterns of disease incidence. Mathematical models that integrate infection and demography are consequently a key tool for informing expectations for disease burden and iden...

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Autores principales: Metcalf, C. Jessica E., Graham, Andrea L., Martinez‐Bakker, Micaela, Childs, Dylan Z.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: John Wiley and Sons Inc. 2016
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4991293/
https://www.ncbi.nlm.nih.gov/pubmed/26620440
http://dx.doi.org/10.1111/1365-2656.12456
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author Metcalf, C. Jessica E.
Graham, Andrea L.
Martinez‐Bakker, Micaela
Childs, Dylan Z.
author_facet Metcalf, C. Jessica E.
Graham, Andrea L.
Martinez‐Bakker, Micaela
Childs, Dylan Z.
author_sort Metcalf, C. Jessica E.
collection PubMed
description 1. Epidemiological dynamics are shaped by and may in turn shape host demography. These feedbacks can result in hard to predict patterns of disease incidence. Mathematical models that integrate infection and demography are consequently a key tool for informing expectations for disease burden and identifying effective measures for control. 2. A major challenge is capturing the details of infection within individuals and quantifying their downstream impacts to understand population‐scale outcomes. For example, parasite loads and antibody titres may vary over the course of an infection and contribute to differences in transmission at the scale of the population. To date, to capture these subtleties, models have mostly relied on complex mechanistic frameworks, discrete categorization and/or agent‐based approaches. 3. Integral Projection Models (IPMs) allow variance in individual trajectories of quantitative traits and their population‐level outcomes to be captured in ways that directly reflect statistical models of trait–fate relationships. Given increasing data availability, and advances in modelling, there is considerable potential for extending this framework to traits of relevance for infectious disease dynamics. 4. Here, we provide an overview of host and parasite natural history contexts where IPMs could strengthen inference of population dynamics, with examples of host species ranging from mice to sheep to humans, and parasites ranging from viruses to worms. We discuss models of both parasite and host traits, provide two case studies and conclude by reviewing potential for both ecological and evolutionary research.
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spelling pubmed-49912932016-09-06 Opportunities and challenges of Integral Projection Models for modelling host–parasite dynamics Metcalf, C. Jessica E. Graham, Andrea L. Martinez‐Bakker, Micaela Childs, Dylan Z. J Anim Ecol British Ecological Society Special Feature: Demography Beyond the Population 1. Epidemiological dynamics are shaped by and may in turn shape host demography. These feedbacks can result in hard to predict patterns of disease incidence. Mathematical models that integrate infection and demography are consequently a key tool for informing expectations for disease burden and identifying effective measures for control. 2. A major challenge is capturing the details of infection within individuals and quantifying their downstream impacts to understand population‐scale outcomes. For example, parasite loads and antibody titres may vary over the course of an infection and contribute to differences in transmission at the scale of the population. To date, to capture these subtleties, models have mostly relied on complex mechanistic frameworks, discrete categorization and/or agent‐based approaches. 3. Integral Projection Models (IPMs) allow variance in individual trajectories of quantitative traits and their population‐level outcomes to be captured in ways that directly reflect statistical models of trait–fate relationships. Given increasing data availability, and advances in modelling, there is considerable potential for extending this framework to traits of relevance for infectious disease dynamics. 4. Here, we provide an overview of host and parasite natural history contexts where IPMs could strengthen inference of population dynamics, with examples of host species ranging from mice to sheep to humans, and parasites ranging from viruses to worms. We discuss models of both parasite and host traits, provide two case studies and conclude by reviewing potential for both ecological and evolutionary research. John Wiley and Sons Inc. 2016-03 2015-12-01 /pmc/articles/PMC4991293/ /pubmed/26620440 http://dx.doi.org/10.1111/1365-2656.12456 Text en © 2016 The Authors. Journal of Animal Ecology published by John Wiley & Sons Ltd on behalf of British Ecological Society. This is an open access article under the terms of the Creative Commons Attribution (http://creativecommons.org/licenses/by/4.0/) License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.
spellingShingle British Ecological Society Special Feature: Demography Beyond the Population
Metcalf, C. Jessica E.
Graham, Andrea L.
Martinez‐Bakker, Micaela
Childs, Dylan Z.
Opportunities and challenges of Integral Projection Models for modelling host–parasite dynamics
title Opportunities and challenges of Integral Projection Models for modelling host–parasite dynamics
title_full Opportunities and challenges of Integral Projection Models for modelling host–parasite dynamics
title_fullStr Opportunities and challenges of Integral Projection Models for modelling host–parasite dynamics
title_full_unstemmed Opportunities and challenges of Integral Projection Models for modelling host–parasite dynamics
title_short Opportunities and challenges of Integral Projection Models for modelling host–parasite dynamics
title_sort opportunities and challenges of integral projection models for modelling host–parasite dynamics
topic British Ecological Society Special Feature: Demography Beyond the Population
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4991293/
https://www.ncbi.nlm.nih.gov/pubmed/26620440
http://dx.doi.org/10.1111/1365-2656.12456
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