Inferring Pathogen Type Interactions Using Cross-sectional Prevalence Data: Opportunities and Pitfalls for Predicting Type Replacement.
Man, Irene Wallinga, Jacco Bogaards, Johannes A
Man, Irene
Wallinga, Jacco
Bogaards, Johannes A
Citations
Altmetric:
Series / Report no.
Open Access
Type
Article
Language
en
Date of publication
2018-09
Year of publication
Research Projects
Organizational Units
Journal Issue
Title
Inferring Pathogen Type Interactions Using Cross-sectional Prevalence Data: Opportunities and Pitfalls for Predicting Type Replacement.
Translated Title
Published in
Epidemiology 2018; 29(5):666-74
Abstract
Many multivalent vaccines target only a subset of all pathogenic types. If vaccine and nonvaccine types compete, vaccination may lead to type replacement. The plausibility of type replacement has been assessed using the odds ratio (OR) of co-infections in cross-sectional prevalence data, with OR > 1 being interpreted as low risk of type replacement. The usefulness of the OR as a predictor for type replacement is debated, as it lacks a theoretical justification, and there is no framework explaining under which assumptions the OR predicts type replacement.