Parameter estimation and prediction for the course of a single epidemic outbreak of a plant disease

A. Kleczkowski, C. A. Gilligan

Research output: Contribution to journalArticle

13 Citations (Scopus)

Abstract

Many epidemics of plant diseases are characterized by large variability among individual outbreaks. However, individual epidemics often follow a well-defined trajectory which is much more predictable in the short term than the ensemble (collection) of potential epidemics. In this paper, we introduce a modelling framework that allows us to deal with individual replicated outbreaks, based upon a Bayesian hierarchical analysis. Information about 'similar' replicate epidemics can be incorporated into a hierarchical model, allowing both ensemble and individual parameters to be estimated. The model is used to analyse the data from a replicated experiment involving spread of Rhizoctonia solani on radish in the presence or absence of a biocontrol agent, Trichoderma viride. The rate of primary (soil-to-plant) infection is found to be the most variable factor determining the final size of epidemics. Breakdown of biological control in some replicates results in high levels of primary infection and increased variability. The model can be used to predict new outbreaks of disease based upon knowledge from a 'library' of previous epidemics and partial information about the current outbreak. We show that forecasting improves significantly with knowledge about the history of a particular epidemic, whereas the precision of hindcasting to identify the past course of the epidemic is largely independent of detailed knowledge of the epidemic trajectory. The results have important consequences for parameter estimation, inference and prediction for emerging epidemic outbreaks.

Original languageEnglish
Pages (from-to)867-877
Number of pages11
JournalJournal of the Royal Society Interface
Volume4
Issue number16
DOIs
Publication statusPublished - 22 Oct 2007

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Keywords

  • Bayesian inference
  • biological control
  • compartmental modelling
  • epidemiology
  • Markov chain methods
  • plant-pathogen systems

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