Mixed Models for Plant Epidemiology

The project **Mixed Models for Plant Epidemiology** focuses on the use of advanced statistical models to understand and predict the spread of plant diseases. By utilizing Bayesian Mixed Models, the project integrates spatial and temporal data to analyze distribution patterns of diseases such as **Citrus Black Spot**, **Circular Leaf Spot**, and *Xylella fastidiosa*. These models help identify climatic and geographic factors influencing disease propagation, aiding in the design of effective control and mitigation strategies. The insights gained from this project are crucial for developing sustainable agricultural practices and protecting plant health.
Joaquín Martínez-Minaya
Joaquín Martínez-Minaya
Assistant Professor in Statistics and Optimization

My research interests include Spatio-temporal Bayesian models using INLA and Stan, and Compositional Data methods