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Unknown age in health disorders: A method to account for its cumulative effect and an application to feline viruses

Eléonore Hellard1, Dominique Pontier1, Aurélie Siberchicot1

  • 1Université de Lyon, Université Lyon1, CNRS, UMR 5558, Laboratoire de Biométrie et Biologie Evolutive, 43 Bd du 11 novembre 1918, F-69622, Villeurbanne, France.

Epidemics
|May 17, 2015
PubMed

Insights

Host age can create false parasite interactions in field studies. A new R package, Interatrix, helps correct for this confounding factor to accurately identify real parasite relationships.

Area of Science:

  • Ecology
  • Epidemiology
  • Parasitology

Background:

  • Parasite interactions are well-documented experimentally but rarely studied in natural field settings.
  • Field studies are crucial for understanding co-infection dynamics but face challenges, including confounding factors.
  • Host age is a significant covariate influencing infection susceptibility and exposure duration, yet difficult to assess in wild populations.

Purpose of the Study:

  • To investigate how host age, as a confounding factor, can lead to false parasite interactions in field studies.
  • To develop and validate a statistical method to correct for the confounding effect of host age on parasite interaction detection.
  • To introduce the R package 'Interatrix' for addressing confounding factors in parasite interaction analyses.

Main Methods:

  • Utilized a Susceptible-Infected (SI) type dynamic model for theoretical (deterministic) and empirical (stochastic) analysis.
  • Applied the modeling approach to a real-world dataset of feline viral infections.
  • Developed a correction method to account for age-related confounding and other shared risk factors.

Main Results:

  • Demonstrated that the cumulative effect of host age can generate statistically significant, yet false, parasite interactions.
  • Identified that the risk of false positives is highest when parasites induce long-lasting antibodies and share similar infection rates.
  • Successfully applied the developed correction method to the feline virus dataset, improving interaction detection.

Conclusions:

  • Host age is a critical, often overlooked, confounding variable in field-based parasite interaction studies.
  • The 'Interatrix' R package provides a valuable tool for correcting age-related biases and identifying true parasite interactions.
  • Accurate identification of parasite interactions in the field is essential for understanding their ecological and evolutionary consequences.
Keywords:
Felis silvestris catusMultiple infectionsParametric boostrapSI modelSerology

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