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Coinfections by noninteracting pathogens are not independent and require new tests of interaction

Frédéric M Hamelin1, Linda J S Allen2, Vrushali A Bokil3

  • 1IGEPP, Agrocampus Ouest, INRA, Université de Rennes 1, Université Bretagne-Loire, Rennes, France.

Plos Biology
|December 4, 2019
PubMed

Insights

Even when pathogens do not interact, host deaths can create a false positive correlation. New epidemiological models and tests reveal true pathogen interactions, improving disease analysis in humans, plants, and animals.

Area of Science:

  • Epidemiology
  • Disease Ecology
  • Mathematical Biology

Background:

  • Statistical independence is commonly assumed when analyzing pathogen interactions using cross-sectional data.
  • This assumption is challenged by basic epidemiological models, particularly concerning coinfection dynamics.

Purpose of the Study:

  • To develop novel statistical tests for detecting pathogen interactions that account for non-independence.
  • To re-evaluate existing studies on pathogen interactions using updated methodologies.

Main Methods:

  • Modeling the dynamics of multiple non-interacting pathogens causing chronic infections.
  • Developing a pair of novel tests to identify true pathogen interactions by correcting for induced correlations.

Main Results:

  • Host mortality in coinfected individuals can create a positive correlation between pathogen prevalences, even without direct interaction.
  • The developed tests correctly identify interactions by properly accounting for this non-independence.

Conclusions:

  • Simple epidemiological models reveal that statistical independence is not always a valid assumption in coinfection studies.
  • The novel tests offer a more accurate approach to identifying pathogen interactions across various species, including humans, plants, and animals.

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