An age-structured extension to the vectorial capacity model.

Novoseltsev, Vasiliy N

Michalski, Anatoli I

Novoseltseva, Janna A

Yashin, Anatoliy I

Carey, James R

Ellis, Alicia M

United States

2017-06-09T18:36:35Z

2017-06-09T18:36:35Z

2012

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.

https://www.ncbi.nlm.nih.gov/pubmed/22724022

PONE-D-12-07295

1932-6203

https://hdl.handle.net/10161/14911

eng

Public Library of Science (PLoS)

PLoS One

10.1371/journal.pone.0039479

Age Factors

Algorithms

Animals

Basic Reproduction Number

Communicable Diseases

Disease Vectors

Humans

Longevity

Models, Statistical

Population Dynamics

An age-structured extension to the vectorial capacity model.

Journal article

https://www.ncbi.nlm.nih.gov/pubmed/22724022

e39479

6

Center for Population Health & Aging

Duke

Duke Cancer Institute

Duke Population Research Center

Duke Population Research Institute

Institutes and Centers

Institutes and Provost's Academic Units

Sanford School of Public Policy

School of Medicine

Social Science Research Institute

University Institutes and Centers

Published

7