Please use this identifier to cite or link to this item: https://doi.org/10.1890/ES10-00192.1
Title: Epidemiological modeling of invasion in heterogeneous landscapes: Spread of sudden oak death in California (1990-2030)
Authors: Meentemeyer, R.K.
Cunniffe, N.J.
Cook, A.R. 
N. Filipe, J.A.
Hunter, R.D.
Rizzo, D.M.
Gilligan, C.A.
Keywords: Computational biology
Emerging infectious disease
GIS
Landscape epidemiology
Markov chain Monte Carlo
Phytophthora ramorum
Spatial heterogeneity
Species distribution model
Issue Date: 16-Feb-2011
Citation: Meentemeyer, R.K., Cunniffe, N.J., Cook, A.R., N. Filipe, J.A., Hunter, R.D., Rizzo, D.M., Gilligan, C.A. (2011-02-16). Epidemiological modeling of invasion in heterogeneous landscapes: Spread of sudden oak death in California (1990-2030). Ecosphere 2 (2) : -. ScholarBank@NUS Repository. https://doi.org/10.1890/ES10-00192.1
Abstract: The spread of emerging infectious diseases (EIDs) in natural environments poses substantial risks to biodiversity and ecosystem function. As EIDs and their impacts grow, landscape- to regional-scale models of disease dynamics are increasingly needed for quantitative prediction of epidemic outcomes and design of practicable strategies for control. Here we use spatio-temporal, stochastic epidemiological modeling in combination with realistic geographical modeling to predict the spread of the sudden oak death pathogen (Phytophthora ramorum) through heterogeneous host populations in wildland forests, subject to fluctuating weather conditions. The model considers three stochastic processes: (1) the production of inoculum at a given site; (2) the chance that inoculum is dispersed within and among sites; and (3) the probability of infection following transmission to susceptible host vegetation. We parameterized the model using Markov chain Monte Carlo (MCMC) estimation from snapshots of local- and regional-scale data on disease spread, taking account of landscape heterogeneity and the principal scales of spread. Our application of the model to Californian landscapes over a 40-year period (1990-2030), since the approximate time of pathogen introduction, revealed key parameters driving the spatial spread of disease and the magnitude of stochastic variability in epidemic outcomes. Results show that most disease spread occurs via local dispersal (
Source Title: Ecosphere
URI: http://scholarbank.nus.edu.sg/handle/10635/105120
ISSN: 21508925
DOI: 10.1890/ES10-00192.1
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