A new multicriteria risk mapping approach based on a multiattribute frontier concept

Managing uncertainty in decision-making for conservation science

Science-based decision-making is the ideal. However, scientific knowledge is incomplete, and sometimes wrong. Responsible science-based policy, planning, and action must exploit knowledge while managing uncertainty. I considered the info-gap method to manage deep uncertainty surrounding knowledge that is used for decision-making in conservation. A central concept is satisficing, which means satisfying a critical requirement. Alternative decisions are prioritized based on their robustness to uncertainty, and critical outcome requirements are satisficed. Robustness is optimized; outcome is satisficed. This is called robust satisficing. A decision with a suboptimal outcome may be preferred over a decision with a putatively optimal outcome if the former can more robustly achieve an acceptable outcome. Many biodiversity conservation applications employ info-gap theory, under which parameter uncertainty but not uncertainty in functional relations is considered. I considered info-gap models of functional uncertainty, widely used outside of conservation science, as applied to conservation of a generic endangered species by translocation to a new region. I focused on 2 uncertainties: the future temperature is uncertain due to climate change, and the shape of the reproductive output function is uncertain due to translocation to an unfamiliar region. The value of new information is demonstrated based on the robustness function, and the info-gap opportuneness function demonstrates the potential for better-than-anticipated outcomes.

Risk management options to contrast the introduction of citrus fruit bacterial canker through ornamental Rutaceae in the Mediterranean Basin: An Italian case study

Citrus bacterial canker (CBC) is a known disease caused by Xanthomonas citri subsp citri, which affects many species and varieties of Rutaceae. It causes evident damage on the epigeal parts of plant (leaves and branches) and, in particular, on the fruits, causing their fall and/or deterioration, making them unsuitable for sale. EPPO has signaled its presence in many Asian countries and in the Middle East, in South and Central America and in some regions of the African continent, but not yet in Europe. There are several possible ways of introducing this pathogen into the Mediterranean Basin and, among these, there is the trade of plant material for propagation and planting and the flow of tourism between the risk areas and the Mediterranean countries. This research demonstrates how the risk of invasion through ornamental Rutaceae is evident and identifies - in a participatory way through the involvement of stakeholders - some possible tools of phytosanitary protection. The methodological approach, with multi-criteria analysis, recognizes the interest in forms of protection represented by voluntary certification tools, rather than the introduction of new taxation that can finance the protection system.

A Dominance-Based Rough Set Approach for an Enhanced Assessment of Seasonal Influenza Risk

Accounting for about 290,000-650,000 deaths across the globe, seasonal influenza is estimated by the World Health Organization to be a major cause of mortality. Hence, there is a need for a reliable and robust epidemiological surveillance decision-making system to understand and combat this epidemic disease. In a previous study, the authors proposed a decision support system to fight against seasonal influenza. This system is composed of three subsystems: (i) modeling and simulation, (ii) data warehousing, and (iii) analysis. The analysis subsystem relies on spatial online analytical processing (S-OLAP) technology. Although the S-OLAP technology is useful in analyzing multidimensional spatial data sets, it cannot take into account the inherent multicriteria nature of seasonal influenza risk assessment by itself. Therefore, the objective of this article is to extend the existing decision support system by adding advanced multicriteria analysis capabilities for enhanced seasonal influenza risk assessment and monitoring. Bearing in mind the characteristics of the decision problem considered in this article, a well-known multicriteria classification method, the dominance-based rough set approach (DRSA), was selected to boost the existing decision support system. Combining the S-OLAP technology and the multicriteria classification method DRSA in the same decision support system will largely improve and extend the scope of analysis capabilities. The extended decision support system has been validated by its application to assess seasonal influenza risk in the northwest region of Algeria.

Combining Quantitative Risk Assessment of Human Health, Food Waste, and Energy Consumption: The Next Step in the Development of the Food Cold Chain?

The preservation of perishable food via refrigeration in the supply chain is essential to extend shelf life and provide consumers with safe food. However, electricity consumed in refrigeration processes has an economical and an environmental impact. This study focuses on the cold chain of cooked ham, including transport, cold room in supermarket, display cabinet, transport by consumer, and domestic refrigerator, and aims to predict the risk for human health associated with Listeria monocytogenes, the amount of food wasted due to the growth of spoilage bacteria, and the electrical consumption to maintain product temperature through the cold chain. A set of eight intervention actions were tested to evaluate their impact on the three criteria. Results show that the modification of the thermostat of the domestic refrigerator has a high impact on food safety and food waste and a limited impact on the electrical consumption. Inversely, the modification of the airflow rate in the display cabinet has a high impact on electrical consumption and a limited impact on food safety and food waste. A cost-benefit analysis approach and two multicriteria decision analysis methods were used to rank the intervention actions. These three methodologies show that setting the thermostat of the domestic refrigerator to 4 °C presents the best compromise between the three criteria. The impact of decisionmaker preferences (criteria weight) and limitations of these three approaches are discussed. The approaches proposed by this study may be useful in decision making to evaluate global impact of intervention actions in issues involving conflicting outputs.

A new hypervolume approach for assessing environmental risks

Assessing risks of uncertain but potentially damaging events, such as environmental disturbances, disease outbreaks and pest invasions, is a key analytical step that informs subsequent decisions about how to respond to these events. We present a continuous risk measure that can be used to assess and prioritize environmental risks from uncertain data in a geographical domain. The metric is influenced by both the expected magnitude of risk and its uncertainty. We demonstrate the approach by assessing risks of human-mediated spread of Asian longhorned beetle (ALB, Anoplophora glabripennis) in Greater Toronto (Ontario, Canada). Information about the human-mediated spread of ALB through this urban environment to individual geographical locations is uncertain, so each location was characterized by a set of probabilistic rates of spread, derived in this case using a network model. We represented the sets of spread rates for the locations by their cumulative distribution functions (CDFs) and then, using the first-order stochastic dominance rule, found ordered non-dominant subsets of these CDFs, which we then used to define different classes of risk across the geographical domain, from high to low. Because each non-dominant subset was estimated with respect to all elements of the distribution, the uncertainty in the underlying data was factored into the delineation of the risk classes; essentially, fewer non-dominant subsets can be defined in portions of the full set where information is sparse. We then depicted each non-dominant subset as a point cloud, where points represented the CDF values of each subset element at specific sampling intervals. For each subset, we then defined a hypervolume bounded by the outermost convex frontier of that point cloud. This resulted in a collection of hypervolumes for every non-dominant subset that together serve as a continuous measure of risk, which may be more practically useful than averaging metrics or ordinal rank measures. Overall, the approach offers a rigorous depiction of risk in a geographical domain when the underlying estimates of risk for individual locations are represented by sets or distributions of uncertain estimates. Our hypervolume-based approach can be used to compare assessments made with different datasets and assumptions.

Spatial and temporal epidemiological analysis in the Big Data era

Concurrent with global economic development in the last 50 years, the opportunities for the spread of existing diseases and emergence of new infectious pathogens, have increased substantially. The activities associated with the enormously intensified global connectivity have resulted in large amounts of data being generated, which in turn provides opportunities for generating knowledge that will allow more effective management of animal and human health risks. This so-called Big Data has, more recently, been accompanied by the Internet of Things which highlights the increasing presence of a wide range of sensors, interconnected via the Internet. Analysis of this data needs to exploit its complexity, accommodate variation in data quality and should take advantage of its spatial and temporal dimensions, where available. Apart from the development of hardware technologies and networking/communication infrastructure, it is necessary to develop appropriate data management tools that make this data accessible for analysis. This includes relational databases, geographical information systems and most recently, cloud-based data storage such as Hadoop distributed file systems. While the development in analytical methodologies has not quite caught up with the data deluge, important advances have been made in a number of areas, including spatial and temporal data analysis where the spectrum of analytical methods ranges from visualisation and exploratory analysis, to modelling. While there used to be a primary focus on statistical science in terms of methodological development for data analysis, the newly emerged discipline of data science is a reflection of the challenges presented by the need to integrate diverse data sources and exploit them using novel data- and knowledge-driven modelling methods while simultaneously recognising the value of quantitative as well as qualitative analytical approaches. Machine learning regression methods, which are more robust and can handle large datasets faster than classical regression approaches, are now also used to analyse spatial and spatio-temporal data. Multi-criteria decision analysis methods have gained greater acceptance, due in part, to the need to increasingly combine data from diverse sources including published scientific information and expert opinion in an attempt to fill important knowledge gaps. The opportunities for more effective prevention, detection and control of animal health threats arising from these developments are immense, but not without risks given the different types, and much higher frequency, of biases associated with these data.