Cookies on Environmental Impact

Like most websites we use cookies. This is to ensure that we give you the best experience possible.

 

Continuing to use www.cabi.org  means you agree to our use of cookies. If you would like to, you can learn more about the cookies we use.

Environmental Impact

From climate change to biodiversity loss - documenting human impacts on the environment

>>> Sign up to receive our Environmental Sciences newsletter, book alerts and offers <<<

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 17 (Page no: 296)

Statistical emulators of simulation models to inform surveillance and response to new biological invasions.

When a new biosecurity incursion is detected, rapid response is critical to maximize the chance of containment and eradication and minimize the threat to important industries. However, inappropriate response can be extremely costly. For example, we might waste resources on trying to eradicate a pest that has already spread too far to be contained, or use a management strategy that has a lower chance of success than another possibility, and thus allow the pest to escape and establish permanently. Simulation modelling is a tool that can be used to evaluate different management options in the light of available knowledge about the pest's dispersal and population dynamics and its new environment, but simulation models typically take a long time to develop, parameterize, test, run and analyse. How can modelling be used to provide valuable predictions when rapid response is critical? Emulators, or meta-models, are relatively simple and empirical models that capture the important characteristics of more complex and realistic process-based simulation models, and thus 'emulate' their predictions. However, the meta-model is much simpler than the simulation model, making it much quicker to run and analyse. It can also be used to make predictions for a wide range of organisms, environments and management options, and to evaluate which characteristics of these organisms and environments are most important to the final outcome, thus focusing expensive and time-consuming collection of new data where it is most needed.

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: 14 (Page no: 253) Inference and prediction with individual-based stochastic models of epidemics. Author(s): Gibson, G. Gilligan, C. 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: 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
  • School of Plant Biology, The University of Western Australia, 35 Stirling Highway, Crawley, Western Australia 6009, Australia.
  • Year of Publication
  • 2015
  • ISBN
  • 9781780643595
  • Record Number
  • 20153099605