The project focuses on modelling the transmission dynamics of Echinococcus multilocularis in the definitive host, the red fox (Vulpes vulpes). The larval stage of this parasitic tapeworm causes human alveolar echinococcosis (AE), which is a rare but lethal emerging disease in Europe. Age-based models were adapted to analyse E. multilocularis prevalence and abundance field data of foxes in Zurich (Switzerland) to estimate transmission parameters and assess the existence of spatio-temporal variations in parasite infection.
Good model results depend on how accurate is the input data in diagnosing E. multilocularis in foxes. Unfortunately, there is not a perfect diagnostic test for E. multilocularis detection. Using combinations of diagnostic tests, in the absence of a perfect gold standard can, to a certain extent, be overcome using Bayesian techniques and latent class models. Through a Bayesian approach we modelled the results of four diagnostic tests carried out on Swiss foxes to determine the true parasite prevalence and tests performance.