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Please use this identifier to cite or link to this item: http://hdl.handle.net/10373/190

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Title: Estimation of multiple transmission rates for epidemics in heterogeneous populations
Authors: Cook, Alex R.
Otten, Wilfred
Marion, Glenn
Gibson, Gavin J.
Gilligan, Christopher A.
Affiliation: University of Abertay Dundee. School of Contemporary Sciences
Keywords: Bayesian inference
Crop mixture
Susceptible-infected (S-I) epidemic
Spatially structured host populations
Markov chain Monte Carlo
Issue Date: Dec-2007
Publisher: The National Academy of Sciences of the USA
Type: Journal Article
Refereed: peer-reviewed
Rights: This is the author's final version of this article. Published version (c)the National Academy of Sciences of the USA, available from DOI: 10.1073/pnas.0706461104
Citation: Cook, A. R., et al. 2007. Estimation of multiple transmission rates for epidemics in heterogeneous populations. Proceedings of the National Academy of Sciences of the United States of America. 104(51): pp.20392-20397. [Online] Available from: DOI: 10.1073/pnas.0706461104
Abstract: One of the principal challenges in epidemiological modeling is to parameterize models with realistic estimates for transmission rates in order to analyze strategies for control and to predict disease outcomes. Using a combination of replicated experiments, Bayesian statistical inference, and stochastic modeling, we introduce and illustrate a strategy to estimate transmission parameters for the spread of infection through a two-phase mosaic, comprising favorable and unfavorable hosts. We focus on epidemics with local dispersal and formulate a spatially explicit, stochastic set of transition probabilities using a percolation paradigm for a susceptible–infected (S–I) epidemiological model. The S–I percolation model is further generalized to allow for multiple sources of infection including external inoculum and host-to-host infection. We fit the model using Bayesian inference and Markov chain Monte Carlo simulation to successive snapshots of damping-off disease spreading through replicated plant populations that differ in relative proportions of favorable and unfavorable hosts and with time-varying rates of transmission. Epidemiologically plausible parametric forms for these transmission rates are compared by using the deviance information criterion. Our results show that there are four transmission rates for a two-phase system, corresponding to each combination of infected donor and susceptible recipient. Knowing the number and magnitudes of the transmission rates allows the dominant pathways for transmission in a heterogeneous population to be identified. Finally, we show how failure to allow for multiple transmission rates can overestimate or underestimate the rate of spread of epidemics in heterogeneous environments, which could lead to marked failure or inefficiency of control strategies.
URI: http://hdl.handle.net/10373/190
ISSN: 1091-6490
Appears in Collections:Science Engineering & Technology Collection
SIMBIOS Collection

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