Prediction and Surveillance Sampling Assessment in Plant Nurseries and Fields

In this paper, we propose a structured additive regression (STAR) model for modeling the occurrence of a disease in fields or nurseries. The methodological approach involves a Gaussian field (GF) affected by a spatial process represented by an approximation to a Gaussian Markov random field (GMRF). This modeling allows the building of maps with prediction probabilities regarding the presence of a disease in plants using Bayesian kriging. The advantage of this modeling is its computational benefit when compared with known spatial hierarchical models and with the Bayesian inference based on Markov chain Monte Carlo (MCMC) methods. Inference through the use of the integrated nested Laplace approximation (INLA) with the stochastic partial differential equation (SPDE) approach facilitates the handling of large datasets in excellent computation times. Our approach allows the evaluation of different sampling strategies, from which we obtain inferences and prediction maps with similar behaviour to those obtained when we consider all subjects in the study population. The analysis of the different sampling strategies allows us to recognize the relevance of spatial components in the studied phenomenon. We demonstrate how Bayesian kriging can incorporate sources of uncertainty associated with the prediction parameters, which leads to more realistic and accurate estimation of the uncertainty. We illustrate the methodology with samplings of Citrus macrophylla affected by the tristeza virus (CTV) grown in a nursery.

Automated summary

This paper proposes a structured additive regression (STAR) model for modeling the occurrence of a disease in fields or nurseries. The model utilizes Bayesian kriging to build prediction probability maps, providing computational efficiency and accuracy. The use of the integrated nested Laplace approximation (INLA) with the stochastic partial differential equation (SPDE) approach allows for efficient computation with large datasets. The methodology also allows for evaluation of different sampling strategies and recognition of spatial components' relevance in the studied phenomenon.