COMPUTATIONAL MODELLING AND PREDICTING SPREAD OF ARBOREAL EPIDEMIC
DOI:
https://doi.org/10.22159/ajpcr.2017.v10s1.19649Keywords:
Arboreal epidemic modelling, Huanglongbing, infection time, host age and season effectsAbstract
This study presents a modelling solution for the arboreal epidemic like Huanglongbing. Usually, the spread of such plant disease is modelled based upon the four parameters such as: susceptibility, exposure, infectiousness, detection and removed but such a model is deprived from the time as a dimension to model such variations. Owing to this the time for which infection, exposure, detection and removal time is censored form modelling studies of disease spread through heterogeneous plant species. The research presented in this work characterize such heterogeneous transmission with integration of temporal spatial modelling of latent period of season and effects on the host, infection period and dispersal parameters corresponding to the host age. The outcome form this research will enable to control arboreal epidemic.Downloads
References
Gibson GJ, et al. (2006) Bayesian estimation for percolation models of disease spread in plant populations. Stat Comput 16(4):391–402.
Cook A, Marion G, Butler A, Gibson G (2007) Bayesian inference for the spatio-temporal invasion of alien species. Bull Math Biol 69(6):2005–2025.
Kadoya T, Washitani I (2010) Predicting the rate of range expansion of an invasive alien bumblebee (Bombus terrestris) using a stochastic spatio-temporal model. Biol Conserv 143(5):1228–1235.
Meentemeyer RK, et al. (2011) Epidemiological modeling of invasion in heterogeneous landscapes: Spread of sudden oak death in California (1990–2030). Ecosphere 2(2):a17.
Streftaris G, Gibson GJ (2004) Bayesian analysis of experimental epidemics of footand- mouth disease. Proc Biol Sci 271(1544):1111–1117.
Cauchemez S, et al. (2006) Real-time estimates in early detection of SARS. Emerg Infect Dis 12(1):110–113.
Boys RJ, Giles PR (2007) Bayesian inference for stochastic epidemic models with time in homogeneous removal rates. J Math Biol 55(2):223–247.
Bettencourt LMA, Ribeiro RM (2008) Real time bayesian estimation of the epidemic potential of emerging infectious diseases. PLoS ONE 3(5):e2185.
Deardon R, et al. (2010) Inference for individual-level models of infectious diseases in large populations. Statist Sinica 20(1):239–261.
Gottwald TR (2010) Current epidemiological understanding of citrus Huanglongbing. Annu Rev Phytopathol 48:119–139.
Bové J (2006) Huanglongbing: A destructive, newly-emerging, century-old disease of citrus. Plant Pathol 88(1):7–37.
Bassanezi RB, Montesino LH, Stuchi ES (2009) Effects of huanglongbing on fruit quality of sweet orange cultivars in Brazil. Eur J Plant Pathol 125(4):565–572.
Bassanezi RB, Montesino LH, Gasparoto MCG, Filho AB, Amorim L (2011) Yield loss caused by huanglongbing in different sweet orange cultivars in São Paulo, Brazil. Eur J Plant Pathol 130(4):577–586.
Rai, Ankush, and Sakkaravarthi Ramanathan. "DISTRIBUTED LEARNING IN NETWORKED CONTROLLED CYBER PHYSICAL SYSTEM." International Journal of Pharmacy and Technology 8 (3) (2016), 18537-18546.
Rai, Ankush. "Attribute based Level Adaptive Thresholding Algorithm for Object Extraction." Journal of Advancements in Robotics 1.2 (2015): 64-68.
Rai, Ankush. "Attribute Based Level Adaptive Thresholding Algorithm (ABLATA) for Image Compression and Transmission." Journal of mathematics and computer science
(2014), 211-218.
Published
How to Cite
Issue
Section
The publication is licensed under CC By and is open access. Copyright is with author and allowed to retain publishing rights without restrictions.
