Mixed models: getting the best use of parasitological data

Paterson, S. and Lello, Joanne ORCID: https://orcid.org/0000-0002-2640-1027 2003. Mixed models: getting the best use of parasitological data. Trends in Parasitology 19 (8) , pp. 370-375. 10.1016/S1471-4922(03)00149-1

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Abstract

Statistical analysis of parasitological data provides a powerful method for understanding the biological processes underlying parasite infection. However, robust and reliable analysis of parasitological data from natural and experimental infections is often difficult where: (1) the distribution of parasites between hosts is aggregated; (2) multiple measurements are made on the same individual host in longitudinal studies; or (3) data are from ‘noisy’ natural systems. Mixed models, which allow multiple error terms, provide an excellent opportunity to overcome these problems, and their application to the analysis of various types of parasitological data are reviewed here. Statistical models provide powerful tools to investigate the biological processes underlying parasite infection and disease, including exposure and susceptibility of hosts, and infectivity and virulence of parasites 1, 2, 3, 4 and 5.

Item Type:	Article
Date Type:	Publication
Status:	Published
Schools:	Biosciences
ISSN:	1471-4922
Last Modified:	27 Oct 2022 08:59
URI:	https://orca.cardiff.ac.uk/id/eprint/64011

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