title: The problem of auto-correlation in parasitology.
abstract: Explaining the contribution of host and pathogen factors in driving infection dynamics is a major ambition in parasitology. There is increasing recognition that analyses based on single summary measures of an infection (e.g., peak parasitaemia) do not adequately capture infection dynamics and so, the appropriate use of statistical techniques to analyse dynamics is necessary to understand infections and, ultimately, control parasites. However, the complexities of within-host environments mean that tracking and analysing pathogen dynamics within infections and among hosts poses considerable statistical challenges. Simple statistical models make assumptions that will rarely be satisfied in data collected on host and parasite parameters. In particular, model residuals (unexplained variance in the data) should not be correlated in time or space. Here we demonstrate how failure to account for such correlations can result in incorrect biological inference from statistical analysis. We then show how mixed effects models can be used as a powerful tool to analyse such repeated measures data in the hope that this will encourage better statistical practices in parasitology.
authors.affiliation: Institute of Evolutionary Biology, University of Edinburgh, School of Biological Sciences, Edinburgh, United Kingdom. lcp12@psu.edu
authors.forename: Laura C
authors.initials: LC
authors.lastname: Pollitt
authors.forename: Sarah E
authors.initials: SE
authors.lastname: Reece
authors.forename: Nicole
authors.initials: N
authors.lastname: Mideo
authors.forename: Daniel H
authors.initials: DH
authors.lastname: Nussey
authors.forename: Nick
authors.initials: N
authors.lastname: Colegrave
mesh_terms: D000818:Animals; D006790:Host-Parasite Interactions / Q000502:physiology*; D006801:Humans; D008954:Models, Biological; D010272:Parasitic Diseases / Q000469:parasitology* / Q000635:transmission*