A unified and flexible modelling framework for the analysis of malaria serology data

Abstract

Purpose: Serology data are an increasingly important tool in malaria surveillance, especially in low transmission settings where the estimation of parasite-based indicators is often problematic. Existing methods rely on the use of thresholds to identify seropositive individuals and estimate transmission intensity, while making assumptions about the temporal dynamics of malaria transmission that are rarely questioned. Here, we present a novel threshold-free approach for the analysis of malaria serology data which avoids dichotomization of continuous antibody measurements and allows us to model changes in the antibody distribution across age in a more flexible way. Methods & Materials: We develop a unified mechanistic model which combines the properties of reversible catalytic and antibody acquisition models, and allows for temporally varying boosting and seroconversion rates. Additionally, as an alternative to the unified mechanistic model, we also develop an empirical approach to analysis where modelling of the age-dependency is informed by the data rather than biological assumptions. Using serology data from Western Kenya, we demonstrate both the usefulness and limitations of the novel modelling framework. Results: The unified mechanistic model 1) eliminates the need to dichotomize continuous antibody measurements into seropositive and seronegative data, 2) removes assumptions about malaria transmission dynamics, 3) adds flexibility in how transmission intensity can be estimated using regression analysis, 4) incorporates age-dependency of the antibody distribution, and 5) allows for joint estimation of malaria transmission intensity from both the reversible catalytic and antibody acquisition models. Conclusion: Our framework makes the best possible use of the data by avoiding the dichotomization of the continuous antibody measurements, a common practice in the analysis of malaria serology data. More importantly, the unified framework allows us to critically assess and evaluate assumptions on the dynamics of biological indicators of malaria transmission using a principled likelihood-based framework.