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An age-structured extension to the vectorial capacity model.

dc.contributor.author Carey, JR
dc.contributor.author Ellis, AM
dc.contributor.author Michalski, AI
dc.contributor.author Novoseltsev, VN
dc.contributor.author Novoseltseva, JA
dc.contributor.author Yashin, Anatoli I
dc.coverage.spatial United States
dc.date.accessioned 2017-06-09T18:36:35Z
dc.date.available 2017-06-09T18:36:35Z
dc.date.issued 2012
dc.identifier https://www.ncbi.nlm.nih.gov/pubmed/22724022
dc.identifier PONE-D-12-07295
dc.identifier.uri https://hdl.handle.net/10161/14911
dc.description.abstract BACKGROUND: Vectorial capacity and the basic reproductive number (R(0)) have been instrumental in structuring thinking about vector-borne pathogen transmission and how best to prevent the diseases they cause. One of the more important simplifying assumptions of these models is age-independent vector mortality. A growing body of evidence indicates that insect vectors exhibit age-dependent mortality, which can have strong and varied affects on pathogen transmission dynamics and strategies for disease prevention. METHODOLOGY/PRINCIPAL FINDINGS: Based on survival analysis we derived new equations for vectorial capacity and R(0) that are valid for any pattern of age-dependent (or age-independent) vector mortality and explore the behavior of the models across various mortality patterns. The framework we present (1) lays the groundwork for an extension and refinement of the vectorial capacity paradigm by introducing an age-structured extension to the model, (2) encourages further research on the actuarial dynamics of vectors in particular and the relationship of vector mortality to pathogen transmission in general, and (3) provides a detailed quantitative basis for understanding the relative impact of reductions in vector longevity compared to other vector-borne disease prevention strategies. CONCLUSIONS/SIGNIFICANCE: Accounting for age-dependent vector mortality in estimates of vectorial capacity and R(0) was most important when (1) vector densities are relatively low and the pattern of mortality can determine whether pathogen transmission will persist; i.e., determines whether R(0) is above or below 1, (2) vector population growth rate is relatively low and there are complex interactions between birth and death that differ fundamentally from birth-death relationships with age-independent mortality, and (3) the vector exhibits complex patterns of age-dependent mortality and R(0) ∼ 1. A limiting factor in the construction and evaluation of new age-dependent mortality models is the paucity of data characterizing vector mortality patterns, particularly for free ranging vectors in the field.
dc.language eng
dc.relation.ispartof PLoS One
dc.relation.isversionof 10.1371/journal.pone.0039479
dc.subject Age Factors
dc.subject Algorithms
dc.subject Animals
dc.subject Basic Reproduction Number
dc.subject Communicable Diseases
dc.subject Disease Vectors
dc.subject Humans
dc.subject Longevity
dc.subject Models, Statistical
dc.subject Population Dynamics
dc.title An age-structured extension to the vectorial capacity model.
dc.type Journal article
pubs.author-url https://www.ncbi.nlm.nih.gov/pubmed/22724022
pubs.begin-page e39479
pubs.issue 6
pubs.organisational-group Center for Population Health & Aging
pubs.organisational-group Duke
pubs.organisational-group Duke Cancer Institute
pubs.organisational-group Duke Population Research Center
pubs.organisational-group Duke Population Research Institute
pubs.organisational-group Institutes and Centers
pubs.organisational-group Institutes and Provost's Academic Units
pubs.organisational-group Sanford School of Public Policy
pubs.organisational-group School of Medicine
pubs.organisational-group Social Science Research Institute
pubs.organisational-group University Institutes and Centers
pubs.publication-status Published
pubs.volume 7
dc.identifier.eissn 1932-6203


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