ArticleEpidemiology and infection2021
A unified and flexible modelling framework for the analysis of malaria serology data.
Article in Epidemiology and infection, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed.
- Optimizing age-structured sampling for estimating the seroconversion rate in malaria seroepidemiology: a simulation study.Scientific reports · 2026Article
- A flexible class of latent variable models for the analysis of antibody response data.Biostatistics (Oxford, England) · 2026Article
- An integrative review of the combined use of mathematical and statistical models for estimating malaria transmission parameters.Malaria journal · 2025Review
- Serology reveals comparable patterns in the transmission intensities ofFrontiers in cellular and infection microbiology · 2025Article
- A threshold-free approach with age-dependency for estimating malaria seroprevalence.Malaria journal · 2022Article
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Authors and funding
2 authors.
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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.
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