Estimating dengue transmission intensity from serological data: a comparative analysis using mixture and catalytic models

Abstract

AbstractBackgroundDengue virus (DENV) infection is a global health concern of increasing magnitude. To target intervention strategies, accurate estimates of the force of infection (FOI) are necessary. Catalytic models have been widely used to estimate DENV FOI and rely on a binary classification of serostatus as seropositive or seronegative, according to pre-defined antibody thresholds. Previous work has demonstrated the use of thresholds can cause serostatus misclassification and biased estimates. In contrast, mixture models do not rely on thresholds and use the full distribution of antibody titres. To date, there has been limited application of mixture models to estimate DENV FOI.MethodsWe compare the application of mixture models and time-constant and time-varying catalytic models to simulated data and to serological data collected in Vietnam from 2004 to 2009 (N ≥ 2178) and Indonesia in 2014 (N = 3194).ResultsThe simulation study showed greater estimate bias from the time-constant and time-varying catalytic models (FOI bias = 1.3% (0.05%, 4.6%) and 2.3% (0.06%, 7.8%), seroprevalence bias = 3.1% (0.25%, 9.4%) and 2.9% (0.26%, 8.7%), respectively) than from the mixture model (FOI bias = 0.41% (95% CI 0.02%, 2.7%), seroprevalence bias = 0.11% (0.01%, 3.6%)). When applied to real data from Vietnam, the mixture model frequently produced higher FOI and seroprevalence estimates than the catalytic models.ConclusionsOur results suggest mixture models represent valid, potentially less biased, alternatives to catalytic models, which could be particularly useful when estimating FOI and seroprevalence in low transmission settings, where serostatus misclassification tends to be higher.Author summaryCharacterising the transmission intensity of dengue virus in different geographic areas over time is essential to understand who is at greatest risk of infection, and to inform the implementation of interventions, such as vector control and vaccination. It is therefore important to understand how methodological differences and model choice may influence estimates of transmission intensity. We compared the application of catalytic and mixture models to calculate the force of infection (FOI) of dengue virus from antibody titre data. We observed greater bias in FOI estimates obtained from catalytic models than from mixture models in areas where the transmission intensity was low. In high transmission intensity areas, catalytic and mixture models produced consistent estimates. Our results indicate that in low transmission settings, when antibody titre data are available, mixture models could be preferential to estimate dengue virus FOI.