A Stochastic Economic Framework for Partitioning Biosecurity Surveillance Resources

Abstract Effective biosecurity systems are important for protecting trade and the environment from the introduction of exotic pests and diseases, particularly as the movement of goods and people increases worldwide. But systems are complex and the optimal division of resources between biosecurity operations is difficult to determine. In this paper we formulate tractable, stochastic, bio-economic models to guide the optimisation of cost-efficiency in decisions concerning biosecurity operations. In particular, to guide a tradeoff between effort afforded to preventing the introduction of pests and diseases, and post-border surveillance, although the approach has general relevance. The models offer a practical means of optimising resource partitioning, designed to transfer easily between disparate settings and a range of pest-types, and to enable the incorporation of uncertainty. For highly complex problems, tractable frameworks are not always available or efficient. However, using an application to Asian gypsy moth trapping and reference to applications in the literature, we demonstrate that the proposed approach is relevant, is straightforward to apply, and provides a comprehensive analysis for decision-makers.

[1]  J. Bridges Understanding the risks associated with resource allocation decisions in health: An illustration of the importance of portfolio theory , 2004 .

[2]  R. Morris,et al.  Application of portfolio theory to risk-based allocation of surveillance resources in animal populations. , 2007, Preventive veterinary medicine.

[3]  J. Bullock,et al.  Global trade networks determine the distribution of invasive non‐native species , 2017 .

[4]  A. Byrom,et al.  Bio-economic optimisation of surveillance to confirm broadscale eradications of invasive pests and diseases , 2017, Biological Invasions.

[5]  Colin W. Clark,et al.  Mathematical Bioeconomics: The Optimal Management of Renewable Resources. , 1993 .

[6]  Hugh P. Possingham,et al.  Protecting islands from pest invasion: optimal allocation of biosecurity resources between quarantine and surveillance , 2010 .

[7]  Dean P. Anderson,et al.  A modelling framework for predicting the optimal balance between control and surveillance effort in the local eradication of tuberculosis in New Zealand wildlife. , 2016, Preventive veterinary medicine.

[8]  James M. Walker,et al.  Optimal allocation of limited resources to biosecurity surveillance using a portfolio theory methodology , 2019, Ecological Economics.

[9]  A. Sharov,et al.  Bioeconomics of Managing the Spread of Exotic Pest Species with Barrier Zones , 2004, Risk analysis : an official publication of the Society for Risk Analysis.

[10]  N. Becker,et al.  Modeling to Inform Infectious Disease Control , 2015 .

[11]  A. Mastin,et al.  Quantifying the hidden costs of imperfect detection for early detection surveillance , 2019, Philosophical Transactions of the Royal Society B.

[12]  Bo Lu,et al.  Robust surveillance and control of invasive species using a scenario optimization approach , 2017 .

[13]  Peter W J Baxter,et al.  Optimal eradication: when to stop looking for an invasive plant. , 2006, Ecology letters.

[14]  K. Campbell,et al.  Quantifying the success of feral cat eradication, San Nicolas Island, California. , 2011 .

[15]  D. Yemshanov,et al.  There is no silver bullet: The value of diversification in planning invasive species surveillance , 2014 .