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Abstract
Genetic variation in pathogen populations may be an important factor driving heterogeneity in disease dynamics within their host populations. However, to date, we understand poorly how genetic diversity in diseases impact on epidemiological dynamics because data and tools required to answer this questions are lacking. Here, we combine pathogen genetic data with epidemiological monitoring of disease progression, and introduce a statistical exploratory method to investigate differences among pathogen strains in their performance in the field. The method exploits epidemiological data providing a measure of disease progress in time and space, and genetic data indicating the relative spatial patterns of the sampled pathogen strains. Applying this method allows to assign ranks to the pathogen strains with respect to their contributions to natural epidemics and to assess the significance of the ranking. This method was first tested on simulated data, including data obtained from an original, stochastic, multi-strain epidemic model. It was then applied to epidemiological and genetic data collected during one natural epidemic of powdery mildew occurring in its wild host population. Based on the simulation study, we conclude that the method can achieve its aim of ranking pathogen strains if the sampling effort is sufficient. For powdery mildew data, the method indicated that one of the sampled strains tends to have a higher fitness than the four other sampled strains, highlighting the importance of strain diversity for disease dynamics. Our approach allowing the comparison of pathogen strains in natural epidemic is complementary to the classical practice of using experimental infections in controlled conditions to estimate fitness of different pathogen strains. Our statistical tool, implemented in the R package StrainRanking, is mainly based on regression and does not rely on mechanistic assumptions on the pathogen dynamics. Thus, the method can be applied to a wide range of pathogens.
Highlights
Development of epidemiological models has been driven by the need to understand and predict the dynamics, invasion, and persistence of plant and animal diseases [1,2,3]
The method presented here was developed to assign ranks to different pathogen strains with respect to their contribution to natural epidemics
The success of the method depends on the design of the field survey as the statistical power of our approach depends on the sampling size and on the sampling scale
Summary
Development of epidemiological models has been driven by the need to understand and predict the dynamics, invasion, and persistence of plant and animal diseases [1,2,3]. While variation in epidemics caused by abiotic environmental variation is relatively well understood [6,7], quantifying the effect of intraspecific diversity in pathogen populations on epidemic rates has remained a challenge This is non-trivial as diversity in traits affecting infection and transmission is a ubiquitous feature of pathogen populations [8]. At the very fine-scale of within host populations, molecular tools can reveal heterogeneities in transmission generated by differences in infectivity and subsequent growth and reproduction of different parasite strains [8], and their interactions with their hosts (genotype-by-genotype interactions; [15,16]) and environment (genotype-by-environment interactions; [17,18])
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