Surveillance and Control

Abstract

This work develops a new approach for delineating sites that are contaminated by multiple soil heavy metals and applies it to a case study. First a number of contaminant sample data are transformed into multiple spatially un-correlated factors using Uniformly Weighted Exhaustive Diagonalization with Gauss iterations (U-WEDGE). Sequential Gaussian simulation (sGs) is then used to generate sets of realizations of each resultant factor. These are then transformed into sets of sGs contaminant distribution realizations, which are then used to analyze the local and spatial (global) uncertainties in the distribution and concentration of contaminants via joint simulation. Finally, Info-Gap Decision Theory (IGDT) is used to consider different monitoring and or remediation regimes based on the analysis of contaminant realization spatial uncertainty. In our case study each heavy metal contaminant was considered individually and together with all other heavy metals; as the number of heavy metals considered increased, higher critical proportion values of local probability were chosen to obtain a low global uncertainty (to provide high reliability). Info-Gap Decision Theory (IGDT) yielded the most appropriate critical proportion values which minimized information loss in terms of specific goals. When the false negative rate is set to zero, meaning that it is necessary to monitor all potentially polluted areas, the corresponding false positive rates are at least 63%, 65%, 66%, 68%, 70%, and 78% to yield robustness levels of 0.50, 0.60, 0.70, 0.80, 0.90, and 1.00 respectively. However, when the false negative rate tolerance threshold is raised to 50%, the false positive rate tolerance which yields robustness levels of 0.50, 0.60, 0.70, 0.80, 0.90 and 1.00 drop to 12%, 14%, 15%, 18%, 20%, and 39%. The case study demonstrates the effectiveness of the developed approach at making robust decisions concerning the delineation of sites contaminated by multiple heavy metals.

Keywords

Decision-making, Robustness, Contaminated sites, Conditional simulation, Uncertainty, Heavy metals, Soil, GIS.

Abstract

New ideas or technologies are often advocated because of their purported improvements on existing methods. However, what is new is usually less well-known and less widely tested than what is old. New methods may entail greater unknown dangers as well as greater potential advantages. The policy maker who must choose between innovation and convention faces a dilemma of innovation. We present a methodology, based on info-gap robustness, to deal with the innovation dilemma. We illustrate the approach by examining the policy decisions for managing the Light Brown Apple Moth in California.

Keywords

Innovation, policy selection, robustness to uncertainty, info-gaps, Light Brown Apple Moth.

Abstract

Invasive species risk maps provide broad guidance on where to allocate resources for pest monitoring and regulation, but they often present individual risk components (such as climatic suitability, host abundance or introduction potential) as independent entities. These independent risk components are integrated using various multi-criteria analysis techniques that typically require prior knowledge of the risk components’ importance. Such information is often nonexistent for many invasive pests.

This study proposes a new approach for building integrated risk maps using the principle of a multi-attribute efficient frontier and analyzing the partial order of elements of a risk map as distributed in multidimensional criteria space. The integrated risks are estimated as subsequent multi-attribute frontiers in dimensions of individual risk criteria. We demonstrate the approach with the example of Agrilus biguttatus Fabricius, a high-risk pest that may threaten North American oak forests in the near future. Drawing on U.S. and Canadian data, we compare the performance of the multi-attribute ranking against a multi-criteria linear weighted averaging technique in the presence of uncertainties, using the concept of robustness from info-gap decision theory. The results show major geographic hotspots where the consideration of trade-offs between multiple risk components changes integrated risk rankings. Both methods delineate similar geographical regions of high and low risks. Overall, aggregation based on a delineation of multi-attribute efficient frontiers can be a useful tool to prioritize risks for anticipated invasive pests, which usually have an extremely poor prior knowledge base.

Keywords

Multi-attribute efficient frontier; Agrilus biguttatus; Non-dominant set, Multi-criteria aggregation; Pest risk mapping; Robustness to uncertainty.

Abstract

When animals are transported and pass through customs, some of them may have dangerous infectious diseases. Typically, due to the cost of testing, not all animals are tested: a reasonable selection must be made. How to test effectively whilst avoiding costly disease outbreaks? First, we extend a model proposed in the literature for the detection of invasive species to suit our purpose, and we discuss the main sources of model uncertainty, many of which are hard to quantify. Secondly, we explore and compare three decision methodologies on the problem at hand, namely, Bayesian statistics, info-gap theory and imprecise probability theory, all of which are designed to handle severe uncertainty. We show that, under rather general conditions, every info-gap solution is maximal with respect to a suitably chosen imprecise probability model, and that therefore, perhaps surprisingly, the set of maximal options can be inferred at least partly – and sometimes entirely – from an info-gap analysis.

Keywords

Exotic disease, Lower prevision, Info-gap, Maximality, Minimax, Robustness

Abstract

One of the challenges in the design and selection of remediation activities for subsurface contamination is dealing with manifold uncertainties. A scientifically defensible decision process demands consideration of the uncertainties involved. A nonprobabilistic approach based on information gap (info-gap) decision theory is employed to study the robustness of alternative remediation activities. This approach incorporates both parametric and nonparametric (conceptual) uncertainty in predicting contaminant concentrations that are effected by natural processes and the remediation activities. Two remedial scenarios are explored to demonstrate the applicability of the info-gap approach to decision making related to groundwater remediation.

Keywords

Decision theory; Groundwater remediation; Uncertainty analysis

Abstract

Abstract Control of endemic, exotic, and emerging animal diseases critically depends on their early detection and timely management. This paper proposes a novel approach to evaluate alternative surveillance programs based on info-gap theory. A general modeling framework is developed explicitly accounting for severe uncertainty about the incursion, detection, spread, and control of exotic and emergent diseases. The model is illustrated by an evaluation of bluetongue disease surveillance strategies. Key results indicate that, when available, vaccination of the entire population is the most robust strategy. If vaccines are not available then active reporting of suspect clinical signs by farmers is a very robust surveillance policy.

Keywords

Surveillance; Exotic animal disease; Knightian uncertainty; Info-gap theory

Abstract

Two important features of real-world port inspections of shipping containers for invasive species are the general absence of underlying economic considerations and the climate of severe uncertainty that surrounds the likelihood of invasive species introductions. In this article we propose and illustrate a method for determining inspection protocols that address both of these issues. We seek inspection protocols that are robust in the sense that they maximize the range of uncertainty over which the expected loss from the introduction of an invasive species plus the costs of inspections do not exceed some critical value. These inspection strategies are practical and provide ready alternatives to existing protocols.

Abstract

Managing biological invasions often necessitates decision making by public officials facing severe uncertainty about many important factors influencing eventual outcomes. Use of relatively sparse decision models is typically necessary and significant lead time for analysis is unfortunately an uncommon luxury. Moreover, development of relevant information to reduce uncertainty and promote more informed decisions in a timely manner is only rarely feasible. Such an uncertain decision making environment can often engender strategies that are simplistic, highly inefficient, and hard to justify in a rigorous manner. In this paper, info-gap decision theory is leveraged to examine a management strategy for light brown apple moth (Epiphyas postvittana) which was discovered in California during 2007. The guiding principle of the analysis is that robustness to uncertainty is an appropriate management objective when facing the severe uncertainty associated with biological invasions. The info-gap strategy is contrasted with strategies following from other popular decision criteria under uncertainty.

Abstract

Surveillance for invasive non-indigenous species (NIS) is an integral part of a quarantine system. Estimating the efficiency of a surveillance strategy relies on many uncertain parameters estimated by experts, such as the efficiency of its components in face of the specific NIS, the ability of the NIS to inhabit different environments, and so on. Due to the importance of detecting an invasive NIS within a critical period of time, it is crucial that these uncertainties be accounted for in the design of the surveillance system. We formulate a detection model that takes into account, in addition to structured sampling for incursive NIS, incidental detection by untrained workers. We use info-gap theory for satisficing (not minimizing) the probability of detection, while at the same time maximizing the robustness to uncertainty. We demonstrate the trade-off between robustness to uncertainty, and an increase in the required probability of detection. An empirical example based on the detection of Pheidole megacephala on Barrow Island demonstrates the use of info-gap analysis to select a surveillance strategy.

Keywords

Biosurveillance, info-gap, uncertainty, Barrow Island, non-indigenous species, invasion, detection.