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Inferring Pathogen Type Interactions Using Cross-sectional Prevalence Data: Opportunities and Pitfalls for Predicting

Irene Man1,2, Jacco Wallinga1,2, Johannes A Bogaards1,3

  • 1From the Center for Infectious Diseases Control, National Institute for Public Health and the Environment, Bilthoven, The Netherlands.

Epidemiology (Cambridge, Mass.)
|June 21, 2018
PubMed
Abstract

Insights

The odds ratio (OR) can predict pathogen type replacement, but only under specific conditions. Factors like common risk factors and cross-immunity can complicate OR interpretation, necessitating pathogen-specific data for accurate predictions.

Area of Science:

  • Epidemiology
  • Mathematical Biology
  • Infectious Disease Dynamics

Background:

  • Multivalent vaccines may not cover all pathogenic types, potentially leading to type replacement if vaccine and non-vaccine types compete.
  • The odds ratio (OR) is commonly used to assess type replacement risk from co-infection data, with OR > 1 suggesting low risk.
  • The reliability of OR as a type replacement predictor is debated due to a lack of theoretical justification and clear assumptions.

Purpose of the Study:

  • To investigate the behavior of the odds ratio (OR) in predicting pathogen type replacement.
  • To determine the conditions under which the OR accurately reflects the risk of type replacement.
  • To explore the influence of common risk factors and cross-immunity on OR values.

Main Methods:

  • Utilized deterministic Susceptible-Infected-Susceptible (SIS) and Susceptible-Infected-Recovered-Susceptible (SIRS) multitype transmission models.
  • Modeled various interaction mechanisms between pathogen types, from synergistic to competitive.
  • Analyzed parameter ranges to understand OR behavior under different interaction scenarios.

Main Results:

  • OR > 1 can mask competition due to confounding from unobserved common risk factors and cross-immunity.
  • Mathematical proofs demonstrate that common risk factors elevate the OR, and cross-immunity intuitively increases it.
  • OR < 1 predicts type replacement in the absence of immunity and remains predictive with immunity under specific biological assumptions.

Conclusions:

  • Using the OR to predict type replacement from cross-sectional data is justified but requires strict assumptions.
  • Accurate prediction of type replacement necessitates pathogen-specific knowledge regarding common risk factors and cross-immunity.
  • The interpretation of OR values for type replacement risk is context-dependent and requires careful consideration of underlying epidemiological factors.

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