Forecasting Pathogen Dynamics with Bayesian Model-Averaging: Application to Xylella fastidiosa.

ABSTRACT: Forecasting invasive-pathogen dynamics is paramount to anticipate eradication and containment strategies. Such predictions can be obtained using a model grounded on partial differential equations (PDE; often exploited to model invasions) and fitted to surveillance data. This framework allows the construction of phenomenological but concise models relying on mechanistic hypotheses and real observations. However, it may lead to models with overly rigid behavior and possible data-model mismatches. Hence, to avoid drawing a forecast grounded on a single PDE-based model that would be prone to errors, we propose to apply Bayesian model averaging (BMA), which allows us to account for both parameter and model uncertainties. Thus, we propose a set of different competing PDE-based models for represe

SUBMITTER: Abboud C 

PROVIDER: S-EPMC10257384 | biostudies-literature | 2023 Jun

REPOSITORIES: biostudies-literature