To be or not to be associated: power study of four statistical modeling approaches to identify parasite associations in cross-sectional studies

Elise Vaumourin*, Gwenael Vourc'h, Sandra Telfer, Xavier Lambin, Diaeldin Salih, Ulrike Seitzer, Serge Morand, Nathalie Charbonnel, Muriel Vayssier-Taussat, Patrick Gasqui

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

18 Citations (Scopus)
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A growing number of studies are reporting simultaneous infections by parasites in many different hosts. The detection of whether these parasites are significantly associated is important in medicine and epidemiology. Numerous approaches to detect associations are available, but only a few provide statistical tests. Furthermore, they generally test for an overall detection of association and do not identify which parasite is associated with which other one. Here, we developed a new approach, the association screening approach, to detect the overall and the detail of multi-parasite associations. We studied the power of this new approach and of three other known ones (i.e., the generalized chi-square, the network and the multinomial GLM approaches) to identify parasite associations either due to parasite interactions or to confounding factors. We applied these four approaches to detect associations within two populations of multi-infected hosts: (1) rodents infected with Bartonella sp., Babesia microfi and Anaplasma phagocytophilum and (2) bovine population infected with Theileria sp. and Babesia sp. We found that the best power is obtained with the screening model and the generalized chi-square test. The differentiation between associations, which are due to confounding factors and parasite interactions was not possible. The screening approach significantly identified associations between Bartonella doshiae and B. microti, and between T parva, T mutans, and T velifera. Thus, the screening approach was relevant to test the overall presence of parasite associations and identify the parasite combinations that are significantly over- or under-represented. Unraveling whether the associations are due to real biological interactions or confounding factors should be further investigated. Nevertheless, in the age of genomics and the advent of new technologies, it is a considerable asset to speed up researches focusing on the mechanisms driving interactions between parasites.

Original languageEnglish
Article number62
Number of pages11
JournalFrontiers in cellular and infection microbiology
Publication statusPublished - 15 May 2014

Bibliographical note

We are grateful to the « Tiques et Maladies à Tiques » working group of the « Réseau Ecologie des Interactions Durables » for discussion and support. This modeling work was supported by the Animal Health department of National Institute of Agronomic Research (, Auvergne region (, the Metaprogramme MEM (projet Patho-ID) of INRA and the EU grant FP7-261504 EDENext. It is cataloged by the EDENext Steering Committee as EDENext208 ( The contents of this publication are the sole responsibility of the authors and do not necessarily reflect the views of the European Commission. The field vole fieldwork was supported by funding from the Natural Environment Research Council (grant GR3/13051) and the Wellcome Trust (grants 075202/Z/04/Z and 070675/Z/03/Z). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.


  • associations
  • interactions
  • modeling
  • parasite community
  • screening
  • GLM approach
  • network model
  • chi-square test
  • component community structure
  • central equatoria state
  • Lake District Region
  • molecular-detection
  • Southern Sudan
  • Northern Spain
  • networks
  • Babesia
  • population
  • Theileria


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