An age-structured extension to the vectorial capacity model

• Published in: PloS one Vol. 7; no. 6; p. e39479
• Main Authors: Novoseltsev, Vasiliy N, Michalski, Anatoli I, Novoseltseva, Janna A, Yashin, Anatoliy I, Carey, James R, Ellis, Alicia M
• Format: Journal Article
• Language: English
• Published: United States Public Library of Science 19.06.2012; Public Library of Science (PLoS)
• Subjects: Aedes aegypti; Age; Age Factors; Aging; Algorithms; Animals; Anopheles gambiae; Basic Reproduction Number; Biology; Birth; Communicable Diseases - mortality; Communicable Diseases - transmission; Computer Science; Culicidae; Demography; Diptera; Disease; Disease prevention; Disease transmission; Disease Vectors; Encyclopedias; Gerontology; Growth rate; Health aspects; Humans; Infections; Insecticides; Insects; Longevity; Malaria; Mathematical models; Medicine; Models, Statistical; Mortality; Mortality patterns; Mosquitoes; Pathogens; Population; Population Dynamics; Population growth; Prevention; Senescence; Vector-borne diseases; Vectors; Vectors (Biology); Zoonoses; United States > US; California
• ISSN: 1932-6203; 1932-6203
• DOI: 10.1371/journal.pone.0039479

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. 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. 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.