Experimental investigation of alternative transmission functions: quantitative evidence for the importance of non-linear transmission dynamics in host-parasite systems

Orlofske, SA, Flaxman, SM, Joseph, MB, Fenton, A ORCID: 0000-0002-7676-917X, Melbourne, BA and Johnson, PTJ (2018) Experimental investigation of alternative transmission functions: quantitative evidence for the importance of non-linear transmission dynamics in host-parasite systems. Journal of Animal Ecology, 87 (3). pp. 703-715.

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Abstract

1. Understanding pathogen transmission is crucial for predicting and managing disease. Nonetheless, experimental comparisons of alternative functional forms of transmission remain rare, and those experiments that are conducted are often not designed to test the full range of possible forms. 2. To differentiate among 10 candidate transmission functions, we used a novel experimental design in which we independently varied four factors—duration of exposure, numbers of parasites, numbers of hosts and parasite density—in laboratory infection experiments. 3. We used interactions between amphibian hosts and trematode parasites as a model system and all candidate models incorporated parasite depletion. An additional manipulation involving anaesthesia addressed the effects of host behaviour on transmission form. 4. Across all experiments, nonlinear transmission forms involving either a power law or a negative binomial function were the best‐fitting models and consistently outperformed the linear density‐dependent and density‐independent functions. By testing previously published data for two other host–macroparasite systems, we also found support for the same nonlinear transmission forms. 5. Although manipulations of parasite density are common in transmission studies, the comprehensive set of variables tested in our experiments revealed that variation in density alone was least likely to differentiate among competing transmission functions. Across host–pathogen systems, nonlinear functions may often more accurately represent transmission dynamics and thus provide more realistic predictions for infection.

Item Type:	Article
Uncontrolled Keywords:	behaviour, epidemiology, infectious disease, macroparasite, mathematical model, Ribeiroia ondatrae
Depositing User:	Symplectic Admin
Date Deposited:	13 Nov 2017 10:20
Last Modified:	18 Feb 2023 05:16
DOI:	10.1111/1365-2656.12783
Open Access URL:	http://onlinelibrary.wiley.com/doi/10.1111/1365-26...
Related URLs:	Author Publisher
URI:	https://livrepository.liverpool.ac.uk/id/eprint/3012053