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

Citation

Orlofske, Sarah A. et al. (2018), Data from: Experimental investigation of alternative transmission functions: quantitative evidence for the importance of non-linear transmission dynamics in host-parasite systems, Dryad, Dataset, https://doi.org/10.5061/dryad.1pk42

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 ten 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 anesthesia addressed the effects of host behaviour on transmission form. 4. Across all experiments, non-linear 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 non-linear 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, non-linear functions may often more accurately represent transmission dynamics and thus provide more realistic predictions for infection.

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Funding

National Science Foundation, Award: DEB-0841758, DEB-1149308