Background. Dengue 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.
Methods. We 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).
Results. The 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.
Conclusions. Our 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.