Modelling distortions in seroprevalence data using change-point fractional polynomials

Abstract

This paper shows how to model seroprevalence data using change-point fractional polynomials (FPs). The inclusion of a change point in the FP framework allows to detect distortions arising from common (often untestable) assumptions made in the estimation of the age-specific prevalence and force of infection from cross-sectional data.

The method is motivated using seroprevalence data on the parvovirus B19 and the varicella zoster virus in Belgium.

Keywords

• change point
• detecting distortions
• fractional polynomial
• model selection criteria
• seroprevalence data