An improved regulatory sampling method for mapping and representing plant disease from a limited number of samples

Abstract

A key challenge for plant pathologists is to develop efficient methods to describe spatial patterns of disease spread accurately from a limited number of samples. Knowledge of disease spread is essential for informing and justifying plant disease management measures. A mechanistic modelling approach is adopted for disease mapping which is based on disease dispersal gradients and consideration of host pattern. The method is extended to provide measures of uncertainty for the estimates of disease at each host location. In addition, improvements have been made to increase computational efficiency by better initialising the disease status of unsampled hosts and speeding up the optimisation process of the model parameters. These improvements facilitate the practical use of the method by providing information on: (a) mechanisms of pathogen dispersal, (b) distance and pattern of disease spread, and (c) prediction of infection probabilities for unsampled hosts. Two data sets of disease observations, Huanglongbing (HLB) of citrus and strawberry powdery mildew, were used to evaluate the performance of the new method for disease mapping. The result showed that our method gave better estimates of precision for unsampled hosts, compared to both the original method and spatial interpolation. This enables decision makers to understand the spatial aspects of disease processes, and thus formulate regulatory actions accordingly to enhance disease control.