Computational modeling and predicting spread of arboreal epidemic

Abstract

Objective: This study presents a modeling solution for the arboreal epidemic like Huanglongbing. Usually, the spread of such plant disease is modeled based on the four parameters such as susceptibility, exposure, infectiousness, detection, and removed, but such a model is deprived by the time as a dimension to model such variations. Due to this, the time for which infection, exposure, detection, and removal time is censored form modeling studies of disease spread through heterogeneous plant species. Methods: Here, we computationally modeled those key factors for Huanglongbing (HLB) spread and used image processing technique for aerial images for segmenting field which can be utilized for cut-off the prodigiously infected field regions Results and Discussion: The research presented in this work characterize such heterogeneous transmission with the integration of temporal, spatial modeling of latent period of season and effects on the host, infection period, and dispersal parameters corresponding to the hostage. The outcome form this research will enable to control the arboreal epidemic.