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

Journal article

Abstract 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. The proposed unified mechanistic model 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 propose 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.

Authors: Kyomuhangi, I; Giorgi, E

• Publication status: Published
• Peer review status: Peer reviewed
• Publisher: Cambridge University Press
• Journal: Epidemiology & Infection
• Volume: 149
• Pages: e99-e99
• Publication date: 2021-04-12
• DOI: 10.1017/s0950268821000753
• EISSN: 1469-4409
• ISSN: 0950-2688
• Language: English
• Keywords: Malaria; Medicine; Computer science; Machine learning; Mathematics; Transmission (telecommunications); Serology; Dependency (UML); Seroconversion; Antibody; Boosting (machine learning); Econometrics; Artificial intelligence; Immunology