Evaluation of spatio-temporal Bayesian models for the spread of infectious diseases in oil palm. (February 2018)

• Record Type: Journal Article
• Authors: Denis, Marie; Cochard, Benoît; Syahputra, Indra; de Franqueville, Hubert; Tisné, Sébastien
• Is Part Of: Spatial and spatio-temporal epidemiology. Volume 24(2018)
• Page Start: 63
• Page End: 74
• Publication Date: 2018-02
• Subjects: Bayesian spatio-temporal analysis -- INLA -- Infectious diseases; Epidemiology -- Statistical methods -- Periodicals; Epidemiology -- Periodicals; 614.4072
• DOI: 10.1016/j.sste.2017.12.002
• Languages: English
• ISSNs: 1877-5845

Abstract

Abstract: In the field of epidemiology, studies are often focused on mapping diseases in relation to time and space. Hierarchical modeling is a common flexible and effective tool for modeling problems related to disease spread. In the context of oil palm plantations infected by the fungal pathogen Ganoderma boninense, we propose and compare two spatio-temporal hierarchical Bayesian models addressing the lack of information on propagation modes and transmission vectors. We investigate two alternative process models to study the unobserved mechanism driving the infection process. The models help gain insight into the spatio-temporal dynamic of the infection by identifying a genetic component in the disease spread and by highlighting a spatial component acting at the end of the experiment. In this challenging context, we propose models that provide assumptions on the unobserved mechanism driving the infection process while making short-term predictions using ready-to-use software.