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CABI Book Chapter

Biosecurity surveillance: quantitative approaches.

Book cover for Biosecurity surveillance: quantitative approaches.

Description

Biosecurity surveillance plays a vital role in protection against the introduction and spread of unwanted plants and animals. It involves not just collecting relevant information, but also analysing this information. This book focuses on methods for quantitative analysis of biosecurity surveillance data, where these data might arise from observations, sensors, remote imaging, expert opinion and so...

Chapter 14 (Page no: 253)

Inference and prediction with individual-based stochastic models of epidemics.

Stochastic models for the spread of epidemics in space and time are increasingly being used as predictive tools to help in the control of emergent pests and pathogens and as tools for the interpretation of observations of epidemics as they occur. This chapter provides an introduction to a particular class of stochastic model - the individual-based, spatio-temporal compartment model - that is frequently applied in this context. An overview of the techniques used to implement these models and to fit them to observations is provided. The main implications of different model formulations for biosecurity and the design of control strategies are given. The chapter aims to provide the reader, who already has some knowledge of mathematical and statistical approaches to modelling infectious diseases, with a technical overview of the Bayesian computational approach.

Other chapters from this book

Chapter: 1 (Page no: 1) Introduction to Biosecurity surveillance: quantitative approaches. Author(s): Jarrad, F.
Chapter: 2 (Page no: 9) Biosecurity surveillance in agriculture and environment: a review. Author(s): Quinlan, M. Stanaway, M. Mengersen, K.
Chapter: 3 (Page no: 43) Getting the story straight: laying the foundations for statistical evaluation of the performance of surveillance. Author(s): Low-Choy, S.
Chapter: 4 (Page no: 75) Hierarchical models for evaluating surveillance strategies: diversity within a common modular structure. Author(s): Low-Choy, S.
Chapter: 5 (Page no: 109) The relationship between biosecurity surveillance and risk analysis. Author(s): MacLeod, A.
Chapter: 6 (Page no: 123) Designing surveillance for emergency response. Author(s): Havre, Z. van Whittle, P.
Chapter: 7 (Page no: 137) The role of surveillance in evaluating and comparing international quarantine systems. Author(s): Mittinty, M. Whittle, P. Burgman, M. Mengersen, K.
Chapter: 8 (Page no: 151) Estimating detection rates and probabilities. Author(s): Hauser, C. E. Garrard, G. E. Moore, J. L.
Chapter: 9 (Page no: 167) Ad hoc solutions to estimating pathway non-compliance rates using imperfect and incomplete information. Author(s): Robinson, A. P. Chisholm, M. Mudford, R. Maillardet, R.
Chapter: 10 (Page no: 181) Surveillance for soilborne microbial biocontrol agents and plant pathogens. Author(s): Whittle, P. Sundh, I. Neate, S.
Chapter: 11 (Page no: 203) Design of a surveillance system for non-indigenous species on Barrow Island: plants case study. Author(s): Murray, J. Whittle, P. Jarrad, F. Barrett, S. Stoklosa, R. Mengersen, K.
Chapter: 12 (Page no: 217) Towards reliable mapping of biosecurity risk: incorporating uncertainty and decision makers' risk aversion. Author(s): Yemshanov, D. Koch, F. H. Ducey, M. Haack, R. A.
Chapter: 13 (Page no: 238) Detection survey design for decision making during biosecurity incursions. Author(s): Kean, J. M. Burnip, G. M. Pathan, A.
Chapter: 15 (Page no: 265) Evidence of absence for invasive species: roles for hierarchical Bayesian approaches in regulation. Author(s): Stanaway, M.
Chapter: 16 (Page no: 278) Using Bayesian networks to model surveillance in complex plant and animal health systems. Author(s): Johnson, S. Mengersen, K. Ormsby, M. Whittle, P.
Chapter: 17 (Page no: 296) Statistical emulators of simulation models to inform surveillance and response to new biological invasions. Author(s): Renton, M. Savage, D.
Chapter: 18 (Page no: 313) Animal, vegetable, or ...? A case study in using animal-health monitoring design tools to solve a plant-health surveillance problem. Author(s): Hester, S. Sergeant, E. Robinson, A. P. Schult, G.
Chapter: 19 (Page no: 334) Agent-based Bayesian spread model applied to red imported fire ants in Brisbane. Author(s): Keith, J. M. Spring, D.

Chapter details

  • Author Affiliation
  • Maxwell Institute for Mathematical Sciences, Heriot-Watt University, Edinburgh, EH14 4AS, UK.
  • Year of Publication
  • 2015
  • ISBN
  • 9781780643595
  • Record Number
  • 20153099602