Please use this identifier to cite or link to this item: http://hdl.handle.net/1893/25204
Appears in Collections:Computing Science and Mathematics Journal Articles
Peer Review Status: Refereed
Title: Breaking beta: deconstructing the parasite transmission function
Authors: McCallum, Hamish
Fenton, Andrew
Hudson, Peter J
Lee, Brian
Levick, Beth
Norman, Rachel
Perkins, Sarah
Viney, Mark
Wilson, Anthony
Lello, Joanne
Contact Email: r.a.norman@stir.ac.uk
Keywords: computational biology
ecology
evolution
health and disease and epidemiology
theoretical biology
Issue Date: 5-May-2017
Citation: McCallum H, Fenton A, Hudson PJ, Lee B, Levick B, Norman R, Perkins S, Viney M, Wilson A & Lello J (2017) Breaking beta: deconstructing the parasite transmission function, Philosophical Transactions B: Biological Sciences, 372 (1719), Art. No.: 20160084.
Abstract: Transmission is a fundamental step in the life cycle of every parasite but it is also one of the most challenging processes to model and quantify. In most host–parasite models, the transmission process is encapsulated by a single parameterβ. Many different biological processes and interactions, acting on both hosts and infectious organisms, are subsumed in this single term. There are, however, at least two undesirable consequences of this high level of abstraction. First, nonlinearities and heterogeneities that can be critical to the dynamic behaviour of infections are poorly represented; second, estimating the transmission coefficientβfrom field data is often very difficult. In this paper, we present a conceptual model, which breaks the transmission process into its component parts. This deconstruction enables us to identify circumstances that generate nonlinearities in transmission, with potential implications for emergent transmission behaviour at individual and population scales. Such behaviour cannot be explained by the traditional linear transmission frameworks. The deconstruction also provides a clearer link to the empirical estimation of key components of transmission and enables the construction of flexible models that produce a unified understanding of the spread of both micro- and macro-parasite infectious disease agents.
DOI Link: http://dx.doi.org/10.1098/rstb.2016.0084
Rights: © 2017 The Authors. Published by the Royal Society under the terms of the Creative Commons Attribution License http://creativecommons.org/licenses/by/4.0/, which permits unrestricted use, provided the original author and source are credited.

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