Reconstructing prevalence dynamics of wildlife pathogens from pooled and individual samples

Abstract

AbstractPathogen transmission studies require sample collection over extended periods, which can be challenging and costly, especially in the case of wildlife. A useful strategy can be to collect pooled samples, but this presents challenges when the goal is to estimate infection prevalence dynamics. In particular, pooling typically results in a dilution effect where mixing positive and negative or lower-concentration samples lowers the overall concentration. Simultaneously, a pooled sample is more likely to test positive as this requires only one or a few positives. The concentration of a pooled sample can be used to infer the most likely proportion of positive individuals, and thus improve overall prevalence reconstruction, but few methods exist that account for the sample mixing process and none can handle common but non-standard frequency distributions of concentrations.We present a Bayesian multilevel model that estimates prevalence dynamics over time using pooled and individual samples. The model explicitly accounts for the mixing process that determines pooled sample concentration, thus enabling accurate prevalence estimation even from pooled samples only. As it is nearly impossible to link individual-level metrics such as age, sex, or immune markers to infection status when using pooled samples, the model also allows the incorporation of individual-level samples. These are used to further improve prevalence estimates and estimate variable correlations. Crucially, when individual samples can test false negative, a potentially strong bias is introduced that results in wrong regression coefficient estimates. The model, however, can use the combination of pooled and individual samples to estimate false negative rate and account for it so that regression coefficients are estimated correctly. Last, the model enables estimation of extrinsic environmental effects on prevalence dynamics.Using a simulated dataset based on virus transmission in flying foxes, we show that the model is able to accurately estimate prevalence dynamics, false negative rate, and covariate effects. Using a range of scenarios based on real study systems we show that the model is highly robust.The model presents an important advance in the use of pooled samples for estimating prevalence dynamics, can be used with any biomarker of infection (Ct values, antibody levels, other infection biomarkers) and can be applied to a wide range of human and wildlife pathogen study systems.