Application Of Decision Analysis Research Articles

Evaluating the Performance of Taiwan Airport Renovation Projects: An Application of Multiple Attributes Intelligent Decision Analysis

Performance evaluation is a vital tool for measuring whether construction projects meet their established objectives, particularly in complex tasks. It helps identify key areas for improvement and enhances resource allocation efficiency. Through precise performance evaluation, managers can make optimal decisions regarding resource use, ultimately increasing project productivity and overall performance. The objective of this study is to measure the production efficiency of airport renovation projects in Taiwan using data envelopment analysis (DEA) and to apply artificial neural networks (ANN) for predicting project quality. DEA effectively handles scenarios with multiple inputs and outputs, providing relative efficiency comparisons among projects and quantifying the potential for improvement. ANN, on the other hand, can learn nonlinear patterns from the data, allowing for accurate predictions of project quality. As construction projects become more complex, ensuring efficient operation within limited resources becomes increasingly crucial. Traditional performance evaluation methods are inadequate for addressing scenarios involving multiple inputs and outputs; therefore, using DEA and ANN offers a more accurate framework for analysis and prediction. The results of this study indicate that, through the DEA model, five projects achieved an efficiency score of 1, while twelve inefficient projects need to reduce defect frequency by 54.76% and increase the progress and budget implementation efficiency by an average of 10.33% to improve performance. The ANN model achieved a classification accuracy of 94.1% and a mean squared error (MSE) of 0.11 in regression predictions. By adopting a data-driven approach, ANN facilitates intelligent decision making and forecasting throughout the construction process. This study provides construction managers with concrete guidelines for resource allocation and quality prediction, thus enhancing project management effectiveness.

Sustainability Assessment Methods for the Transport Sector Considering the Life Cycle Concept—A Review

This paper presents a summary and review of life cycle sustainability assessment (LCSA) methods for the transport sector. The paper provides a comprehensive overview of articles that employ a variety of methods for assessing sustainable development in the transport sector, taking into account the economic, social, and environmental dimensions. In the sustainability assessment of transport, three methods were evaluated: life cycle assessment (LCA), life cycle cost analysis (LCC), and social life cycle assessment (SLCA). An overview of sustainability assessment methods in transport and a review of the indicators used in the life cycle sustainability assessment was conducted. It was found that the selection of indicators within the LCSA for assessing various aspects of sustainable development is dependent on various geographic and policy contexts. An overview of the application of multi-criteria decision analysis (MCDA) methods to assess LCSA in the transport sector was performed. MCDA methods are used to support decision-making regarding the selection of the most sustainable transport options and allow for the simultaneous consideration of multiple criteria, enabling a more sustainable assessment of different transport options. MCDA methods help to rank alternative transportation fuels and help decision-makers consider indicators encompassing economic, environmental and social aspects.

Application of GIS-Based Multi-Criteria Decision Analysis of Hydro-Geomorphological Factors for Flash Flood Susceptibility Mapping in Bangladesh

Flash floods are one of the most prevalent natural disasters, triggering deadly damage to homesteads, crops, infrastructure, road networks, communications, and the natural environment in the Haor (Wetland) region of Bangladesh. The purpose of the study aims to identify eleven (11) hydro-geomorphological driving factors, namely elevation, slope, aspect, rainfall, land use and land cover (LULC), lithology, soil type, topographic wetness index (TWI), NDVI, distance from the river, and drainage density, which are being explored for mapping flood-prone areas. This research has produced a flash flood susceptibility map using the Analytical Hierarchy Process (AHP) and Analytical Network Process (ANP), which are interactive decision-making approaches under multi-criteria decision analysis (MCDA) in ArcGIS 10.8. The findings of this study showed that the susceptibility to flood hazards differs significantly among the seven Haor districts. As a result of the ANP and AHP, a more significant proportion of the Haor region is moderately susceptible to flooding (8685.09 to 9275.15 sq. km.), whereas 35.34% to 38.32% (7069.70 to 7668.67 sq. km.) accounts for high susceptible to flooding. Furthermore, 200 flood locations were identified in the northeast Haor region, where 140 (70%) randomly selected floods were used for training, and the remaining 60 (30%) were employed for validation purposes. The validation results showed that the AHP model had greater prediction accuracy (AUROC = 92.1%) than the ANP (AUROC = 88.5%) model. Therefore, the study findings can be helpful for researchers, academics, policymakers, and planners for sustainable flood mitigation strategies, particularly in Haor areas.

Application of Bayesian Decision Analysis to Estimate Occupational Noise Levels from Limited Exposure Data

In recent years, Bayesian statistical modeling approaches have emerged as valuable tools in Industrial Hygiene (IH), particularly in situations with limited exposure data. This paper delves into the application of Bayesian Statistical Models, specifically Bayesian Decision Analysis (BDA), to analyze occupational noise exposure data within two Similar Exposure Groups (SEGs) of groundskeeping workers, addressing the pervasive issue of high noise levels exceeding 85 dBA. The primary aim is to estimate exposure levels based on the location of the exposure profiles for the SEGs within the AIHA® Exposure Control Categories (ECCs) 0-4, later modified and expanded to AIHA-ECC 0-5. This study employs BDA to estimate noise exposures among groundskeepers, emphasizing the scarcity of literature on BDA in noise exposure rating. Groundskeepers, facing potential risk of hearing impairment due to prolonged exposure, constitute a crucial and critical demographic, with nearly one million individuals in the United States alone. Noise data from the two SEGs was analyzed by using the Exposure Assessment Strategies Committee—EASC- AIHA® IHData Analyst for BDA modeling, complemented with the AIHA IHSTAT™ for certainty rating. The resulting posterior graphs were utilized to categorize exposure profiles into respective AIHA ECCs relative to noise OEL. These findings provide valuable insights, indicating a posterior probability of approximately 43.5% and 56.5% with 95% confidence that noise exposure levels fall within AIHA ECC 3 and AIHA ECC 4, respectively. However, while the 95% percentile decision statistics suggest exposure values lower than the OEL, the Upper Tolerance Limit (UTL) significantly surpass the OEL indicating low certainty regarding these exposure values for employees. Notably, the UTLs suggest that the SEGs are experiencing noise levels above the legally required 90 dBA for more than 5% of the time, which is an unacceptable scenario. Recommendations include implementing protective strategies such as regular training, surveillance, and monitoring, alongside engineering control strategies and mandatory use of personal protective equipment. These results underscore the critical need for refining judgments about noise exposures based on variability within and between employees, suggesting the use of additional statistical tools such as ANOVA (single-multivariate), MATLAB, and Python.

Application of Remote Sensing and Spatial Fuzzy Multi-criteria Decision Analysis to Identify Potential Dust Sources in Lake Urmia Basin, Northwest Iran

Application of Remote Sensing and Spatial Fuzzy Multi-criteria Decision Analysis to Identify Potential Dust Sources in Lake Urmia Basin, Northwest Iran

The application of multi-criteria decision analysis in evaluating the value of drug-oriented intervention: a literature review.

Objectives: Multi-Criteria Decision Analysis (MCDA) has gained increasing attention in supporting drug risk-benefit assessment, pricing and reimbursement, as well as optimization of clinical interventions. The objective of this study was to systematically collect and categorize evaluation criteria and techniques of weighting and scoring of MCDA for drug value assessment. Methods: A systematic review of the literature was conducted across seven databases to identify articles utilizing the MCDA frameworks for the evaluation of drug value. Evaluation criteria mentioned in the included studies were extracted and assigned to 5 dimensions including clinical, economic, innovative, societal and humanistic value. A descriptive statistical analysis was performed on the identified drug value evaluation criteria, as well as the weighting and scoring techniques employed. The more a criterion or technique were mentioned in articles, the more important we consider it. Results: Out of the 82 articles included, 111 unique criteria were identified to evaluate the value of drug. Among the 56 unique criteria (448 times) used to measure clinical value, the most frequently mentioned were "comparative safety/tolerability" (58 times), "comparative effectiveness/efficacy" (56 times), "comparative patient-perceived health/patient reported outcomes" (37 times), "disease severity" (34 times), and "unmet needs" (25 times). Regarding economic value measurement, out of the 20 unique criteria (124 times), the most frequently utilized criteria were "cost of intervention" (17 times), "comparative other medical costs" (16 times), and "comparative non-medical costs" (18 times). Out of the 10 criteria (18 times) for assessing innovative value, "a novel pharmacological mechanism" was the most frequently mentioned criterion (5 times). Among the 22 criteria (73 times) used to measure societal value, "system capacity and appropriate use of intervention" was the most frequently cited criterion (14 times). Out of the 3 criteria (15 times) utilized to measure humanistic value, "political/historical/cultural context" was the most frequently mentioned criterion (9 times). Furthermore, 11 scoring and 11 weighting techniques were found from various MCDA frameworks. "Swing weighting" and "a direct rating scale" were the most frequently used techniques in included articles. Conclusion: This study comprehensively presented the current evaluation dimensions, criteria, and techniques for scoring and weighting in drug-oriented MCDA articles. By highlighting the frequently cited evaluation criteria and techniques for scoring and weighting, this analysis will provide a foundation to reasonably select appropriate evaluation criteria and technique in constructing the MCDA framework that aligns with research objectives.

Application of Multi-Criteria Decision Analysis (MCDA) to Prioritize Real-World Evidence Studies for Health Technology Management: Outcomes and Lessons Learned by the Canadian Real-World Evidence for Value of Cancer Drugs (CanREValue) Collaboration.

Multi-criteria decision analysis (MCDA) is a value assessment tool designed to help support complex decision-making by incorporating multiple factors and perspectives in a transparent, structured approach. We developed an MCDA rating tool, consisting of seven criteria evaluating the importance and feasibility of conducting potential real-world evidence (RWE) studies aimed at addressing uncertainties stemming from initial cancer drug funding recommendations. In collaboration with the Canadian Agency for Drugs and Technologies in Health's Provincial Advisory Group, a validation exercise was conducted to further evaluate the application of the rating tool using RWE proposals varying in complexity. Through this exercise, we aimed to gain insight into consensus building and deliberation processes and to identify efficiencies in the application of the rating tool. An experienced facilitator led a multidisciplinary committee, consisting of 11 Canadian experts, through consensus building, deliberation, and prioritization. A total of nine RWE proposals were evaluated and prioritized as low (n = 4), medium (n = 3), or high (n = 2) priority. Through an iterative process, efficiencies and recommendations to improve the rating tool and associated procedures were identified. The refined MCDA rating tool can help decision-makers prioritize important and feasible RWE studies for research and can enable the use of RWE for the life-cycle evaluation of cancer drugs.

Aggregating midpoint-indicator results from Environmental Product Declarations for comprehensive evaluations of products' profiles, through the Dominance-based Rough Set Approach: An application in the Durum-Wheat Pasta Sector

To-date there is an unsolved methodological problem for the type-III labelling scheme, namely the Environmental Product Declaration (EPD), that essentially derives from the midpoint approach used for the Life Cycle Assessment (LCA) which the EPD itself is based upon. As a matter of fact, in the current EPD system, it is impossible to perform univocal aggregated evaluations of the product's profile, due to the different equivalent unit-of-measures which the midpoint impacts are expressed with, and to the absence of a scientifically-based, objective scheme for aggregation and weighing.The authors wanted to undertake this challenge with the aim of enhancing the effectiveness and utility of EPDs to guide managers, consumers and other stakeholders in the promotion, distribution, and consumption of sustainable products, in the context of green public procurement. To achieve this goal for the case of durum-wheat pasta, the EPD was complemented by the authors with the Dominance-based Rough Set Approach (DRSA). In result, an aggregate preference model considering all impact categories was obtained, thereby leading to comprehensive comparisons of complete environmental profile of products. The model has the form of a set of minimal decision rules, that can be used by producers for understanding the environmental performance level of their pasta products, and subsequently making strategic decisions for its improvement.Seven of those rules were determined from DRSA application, that regarded grain production and milling, package manufacturing, and a comprehensive downstream phase that included the disposal of the packaging system, and the pasta cooking with water waste disposal. In addition to this, those rules primarily considered the following midpoint categories within the ensemble of those provided by the EPD system for pasta products: acidification, eutrophication, global warming, and mineral element depletion.All of them were certain rules: three of the “at least” type (two at least good, and one at least medium); and four of the “at most” type (one at most bad, and three at most medium).By referring the given PCR-based LCA results to the threshold values of performance on criteria present in the premise of these rules, producers can know in advance the quality class (bad, medium, or good) which their pasta products belong to.In case of a negative evaluation, they can try to improve the value of some crucial indicators before applying for obtaining the EPD, and so enhance the environmental profile of their products.The authors believe that their study advances knowledge and enriches the literature concerning the application of multiple criteria decision analysis (MCDA), wherein DRSA stands out as an outstanding example. This contribution is particularly significant in enhancing and valorising sustainability assessment tools and certification schemes.Under this perspective, the study documented that leveraging DRSA to supplement the EPD system, for the particular case of pasta products, can contribute directly and indirectly to achieving Sustainable Development Goals (SDGs) and, so, to advancing environmental sustainability within the food sector.

An Alternative Sensitivity Analysis for the Evaluation of MCDA Applications: The Significance of Brand Value in the Comparative Financial Performance Analysis of BIST High-End Companies

Multi-criteria decision analysis (MCDA) applications consist of techniques that enable the decision maker to make clearer decisions in scenarios where there is more than one alternative and criterion. The general approach for sensitivity analysis in MCDA applications implies sensitivity to the weight coefficient. In this study, as an alternative approach, we reinterpret sensitivity by using the statistical relationship between the final ranking produced by an MCDA method and a constant external factor. Thus, we both verify through an anchor and reveal to what extent the change in the weight coefficient changes the external relations of MCDA. The motivation for this study is to propose an alternative sensitivity methodology. On the other hand, brand value is a parameter that contains critical information about the future of the company, which has not integrated into financial performance studies made with MCDAs before. To that end, the financial performance of 31 companies with the highest brand value in Turkey and trading on Borsa Istanbul between 2013 and 2022 was analyzed with seven different MCDA applications via integrating brand value into the criteria for the first time. The study’s findings revealed that the proposed innovative sensitivity tests produced similarly robust results as traditional tests. In addition, brand value has been proved to be an advantageous criterion to be implemented into MCDAs for financial performance problems through the sensitivity analysis made.

Application of multicriteria decision analysis to assess the greenness of molecularly imprinted polymers synthesis components

The study applies multicriteria decision analysis (MCDA) to rank the components of molecularly imprinted polymers (MIPs) synthesis according to their greenness. The components are taken from papers that are describing synthesis of MIPs for persistent organic pollutants (POPs) sorbents. Functional monomers (n = 14), target/surrogate molecules (n = 10), porogens (n = 8), cross-linking agents (n = 8) and initiators (n = 4) are described with 10 criteria referring to their safety of application, toxicity, environmental persistence and bioaccumulability. The rankings are performed according to three scenarios, with the application of different weights. The ranking results give a specific guide on the selection of green synthesis components for polybrominated biphenyl ethers (PBDEs) as representatives of POPs, by identification of problematic and recommended chemicals. The results are useful in designing MIP synthesis protocols.

The application of multi-criteria decision analysis in gaining a premier sort of stability in airplane safety

Improving the aeronautical safety has been of high importance for the experts. Different factors influence the static and dynamic stability of the plane. These factors play important roles in the plane safety. The static and dynamic safety include several factors. Taking all the technical criteria and elements into account, this article tries to find a suitable method for evaluating and eventually deciding on the best alternative. One of the methods of multi-criteria decision analysis is the concordance analysis (CA) which is an important way of evaluation in solving issues of different miscellaneous criteria that cannot be incorporated into one another. Also, Best-worst method (BWM), one of the most practical decision-making methods, is used in this article. Using the CA and BWMs and based on different criteria, this research prioritises the premier stability alternatives and uses them in the design and finally suggests a suitable alternative out of the influential linear and lateral ones. To do this, a combination of extant varieties including 15 criteria and three alternatives is used and the concordance and non-concordance collections and criteria are defined. The results showed that in addition to the fact that the use of both methods led to the same results; as a result, the linear, lateral and directional became the best options for aeroplane safety, respectively, for design purposes.

Multiple criteria decision analysis to support the design of safe and sustainable chemicals and materials

The development of safe and sustainable chemicals and materials is essential to achieve the Zero-Pollution Ambition for a Toxic Free Environment stated in the EU Green Deal. For that, criteria need to be defined and considered since early stage of development. A Safe and Sustainable by Design (SSbD) framework is proposed in an EU Recommendation suggesting the assessment of multiple safety and sustainability aspects of chemicals and materials leaving open how the evaluation and selection of the preferable option should be done. This paper presents a proposal with different options for the use of multiattribute aggregation in an evaluation procedure for the SSbD assessment of chemicals and materials. This proposal is based on i) a review of the literature focusing on Multi-Criteria Decision Analysis (MCDA) application in the SSbD context (i.e. applications considering simultaneously safety and sustainability attributes) and ii) the definition of requisites for MCDA to be applied to the SSBD framework. In the latter, an absolute rather than a relative assessment is preferred as it should be possible for an organization developing a new chemical or material to assess if it is SSbD, without needing to obtain data on all of its possible competitors. Moreover, rank-reversals caused by the introduction of other options are avoided, i.e., assessments of one alternative that depends on other alternatives being assessed simultaneously are not the most adequate. Different options for the aggregation of attributes at different levels are discussed as well as for the consideration of data quality in the evaluation procedure. Regardless the approach selected, the use of multiattribute aggregation does not rule out a richer dashboard presenting not only the overall aggregate result, but also the results obtained in other levels of the hierarchy. Such complementary information is important to understand the strengths and weaknesses that an aggregate result might hide.

Decision Analysis to Advance Environmental Sustainability

Decision analysis provides a robust framework for complex decisions related to environmental sustainability and conservation, including for energy and water, fisheries and wildlife management, agriculture, and climate change response. The complexities of these problems stem from their large scope and scale, which leads to multiple decision makers, stakeholders, rightsholders, and other entities with potentially competing objectives. These problems often are time limited (e.g., urgent action is required to prevent species’ extinction), involve management interventions over long time scales and delayed responses to management (deep uncertainty), and are impeded by limited resources (funding, capacity, etc.). In this Special Issue on “Decision Analysis to Advance Environmental Sustainability,” we present five case studies of applications of decision analysis to complex problems in environmental sustainability and conservation. These case studies incorporate multiple objectives related to ecological and environmental sustainability, economic and social concerns, and logistics of implementation. They showcase a wide range of tools and applications to these problems. We also provide suggestions for new avenues of research and application of decision analysis to problems of environmental sustainability and conservation, including how to incorporate other decision-making tools into decision analysis processes, how to broaden the reach of decision analysis to other sustainability problems, how to incorporate more stakeholders and rightsholders into the decision process, the potential to incorporate new technology into these processes, identifying more creative alternatives, how to secure more funding, ways to move from decision to action, and how to move beyond status quo to make big transitions necessary to achieve sustainability.

Monitoring the Performance of Sustainable Development Goals in the Italian Regions

The Sustainable Development Goals (SDGs) are a shared agenda among countries but also a rallying point for forward-looking policy dialogue. Analysis and monitoring of the SDGs are decisive steps in evaluating possible corrective actions. This paper aims to reach two research objectives: (i) providing methodological insights for the application of multicriteria decision analysis (MCDA) in the evaluation of the SDGs and (ii) emphasizing the relevance of monitoring the outcomes of the SDGs by evaluating the Italian regions. For the first objective, an online survey among twenty academics is used, while for the second, an MCDA is proposed that compares the temporal performance of a sustainability score for each Italian region. The results, based on 27 targets, show that in 2021 the northern regions showed better performance, with the province of Trento topping the list. This is followed by Valle d’Aosta and the province of Bolzano, confirming the trio that emerged in the previous year. A very interesting fact is the growth of the central regions, which overall tend to reach a value close to that of the northern regions. In particular, Toscana, Marche and Lazio stand out for a good performance. It is also confirmed that the southern regions occupy the last places in the ranking with the only exception of Abruzzo. The implications of this paper suggest collaboration between different regions in order to achieve a social community in which resources and skills can be enhanced.

Application of multi-criteria decision analysis techniques and decision support framework for informing plant select agent designation and decision making.

The United States Department of Agriculture (USDA) Division of Agricultural Select Agents and Toxins (DASAT) established a list of biological agents (Select Agents List) that threaten crops of economic importance to the United States and regulates the procedures governing containment, incident response, and the security of entities working with them. Every 2years the USDA DASAT reviews their select agent list, utilizing assessments by subject matter experts (SMEs) to rank the agents. We explored the applicability of multi-criteria decision analysis (MCDA) techniques and a decision support framework (DSF) to support the USDA DASAT biennial review process. The evaluation includes both current and non-select agents to provide a robust assessment. We initially conducted a literature review of 16 pathogens against 9 criteria for assessing plant health and bioterrorism risk and documented the findings to support this analysis. Technical review of published data and associated scoring recommendations by pathogen-specific SMEs was found to be critical for ensuring accuracy. Scoring criteria were adopted to ensure consistency. The MCDA supported the expectation that select agents would rank high on the relative risk scale when considering the agricultural consequences of a bioterrorism attack; however, application of analytical thresholds as a basis for designating select agents led to some exceptions to current designations. A second analytical approach used agent-specific data to designate key criteria in a DSF logic tree format to identify pathogens of low concern that can be ruled out for further consideration as select agents. Both the MCDA and DSF approaches arrived at similar conclusions, suggesting the value of employing the two analytical approaches to add robustness for decision making.

Applicability of multi-criteria decision analysis in the site selection procedure for high-level radioactive waste final disposal: robustness-oriented comparison of emplacement concepts

Abstract. The verbal and argumentative approach predominantly practiced in previous repository projects is only comprehensible to the general public to a limited extent (FKT, 2021; Blattman et al., 2023). One option to improve the transparency and the comprehensibility of decision-making processes is to supplement verbal and argumentative approaches with multiple-criteria decision analysis (MCDA) (Geldermann and Lerche, 2014; Frieling et al., 2020; Blattman et al., 2023). In nuclear waste management, MCDA has been used sporadically with respect to disposal strategies and site selection procedures (Achillas et al., 2013; Frieling et al., 2020). For the German site selection procedure, Frieling et al. (2020) identified the potential suitability as well as the development and testing needs of MCDA. Collaborators of the Chair of Repository Safety at RWTH Aachen University are investigating the applicability of MCDA for specific decision problems in the German site selection procedure. One decision problem is the emplacement of high-level radioactive waste (HLW) packages, which is being researched in the context of a doctoral thesis. For this decision problem, influencing factors and interdependencies between decision criteria, for example operational and long-term safety, and between other decision processes in the site selection procedure need to be considered. In the research project presented here, the emplacement of radioactive waste packages is categorized into three concepts: (1) drift emplacement and (2) horizontal and (3) vertical borehole emplacement. All concepts are tracked internationally. In principle, the Bundesgesellschaft für Endlagerung (BGE) evaluates the three concepts as feasible. However, for the performance of the representative preliminary safety investigation (rvSU), the drift emplacement concept is preferred, which is verbally and argumentatively weighed in BGE (2022). In this present research, the applicability of MCDA to the decision of the emplacement concept with respect to the German site selection process is explored in four main iterative steps based on Belton and Stewart (2003): (1) decision problem definition, (2) problem structuring, (3) decision model creation, and (4) verification. Currently the decision problem is being structured. Preliminarily, testing of the applicability of the sequential MCDA methods Preference Ranking Organization Method for Enrichment Evaluation (PROMETHEE) I and II is planned, although the method selection has not been finalized (Brans, 1982; Brans and de Smet, 2016). Furthermore, the combination of different MCDA methods will be elicited. The expected outcome of the research project is a basic decision model for robustness-based comparisons of disposal concepts of HLW. The decision model has the potential to provide decision support and to identify areas for further research regarding safety investigations as well as an increase in transparency, understanding, and acceptance in the site selection procedure. When MCDA methods are adequately applied by the decision maker, benefits are possible in terms of problem structuring, information processing, integration of qualitative and quantitative factors, deeper insights into relevant factors, and identification of compromises (Geldermann and Lerche, 2014; Frieling et al., 2020). Furthermore, the research results are particularly significant for international site selection procedures that are at the beginning of the selection procedure or at a comparable level in the German procedure.

Risk mapping for Taenia solium: Applying multicriteria decision analysis in Lao PDR.

Taenia solium is ranked the most significant global foodborne parasite and the leading cause of epilepsy in low and middle-income countries. Diagnostic challenges have hampered disease control efforts to date and WHO has called for the development of risk mapping tools to assist endemic countries. This study describes the application of multicriteria decision analysis to map the risk of T. solium in Lao PDR and acts as a case study for other endemic countries. Multicriteria decision analysis was completed using census data on relevant risk factors in Lao PDR. Factors were weighted using an analytical hierarchy process. Village risk scores were calculated using a weighted linear combination and categorised using the Fisher-Jenks algorithm into low, medium, and high risk. District risk scores and categories were calculated using the mean village risk score for a district. Sensitivity analysis was completed by doubling and halving risk factor weights, one at a time, and assessing the standard deviation of scores and categories across all scenarios. A total of 2017 (23.7%) villages were classified as high risk, with 3312 (39.0%) medium and 3170 (37.3%) low risk. This resulted in 21 (14.2%) high-risk districts, 83 (56.1%) medium and 44 (29.7%) low-risk districts. The risk maps highlight two areas of interest which are high risk and low variation. The first is the northern province of Phongsaly, which is consistent with literature and anecdotal reports. The second is the southern Salavan and Xekong provinces, which have yet to be investigated in detail. Multicriteria decision analysis has provided a simple, rapid, and flexible approach to mapping the risk of T. solium in Lao PDR. The nature of the method means that it can be completed in any endemic country with available and appropriate risk factor data.

Application of multi-criteria decision analysis techniques and decision support framework for informing select agent designation for agricultural animal pathogens.

The United States Department of Agriculture (USDA), Division of Agricultural Select Agents and Toxins (DASAT) established a list of biological agents and toxins (Select Agent List) that potentially threaten agricultural health and safety, the procedures governing the transfer of those agents, and training requirements for entities working with them. Every 2years the USDA DASAT reviews the Select Agent List, using subject matter experts (SMEs) to perform an assessment and rank the agents. To assist the USDA DASAT biennial review process, we explored the applicability of multi-criteria decision analysis (MCDA) techniques and a Decision Support Framework (DSF) in a logic tree format to identify pathogens for consideration as select agents, applying the approach broadly to include non-select agents to evaluate its robustness and generality. We conducted a literature review of 41 pathogens against 21 criteria for assessing agricultural threat, economic impact, and bioterrorism risk and documented the findings to support this assessment. The most prominent data gaps were those for aerosol stability and animal infectious dose by inhalation and ingestion routes. Technical review of published data and associated scoring recommendations by pathogen-specific SMEs was found to be critical for accuracy, particularly for pathogens with very few known cases, or where proxy data (e.g., from animal models or similar organisms) were used to address data gaps. The MCDA analysis supported the intuitive sense that select agents should rank high on the relative risk scale when considering agricultural health consequences of a bioterrorism attack. However, comparing select agents with non-select agents indicated that there was not a clean break in scores to suggest thresholds for designating select agents, requiring subject matter expertise collectively to establish which analytical results were in good agreement to support the intended purpose in designating select agents. The DSF utilized a logic tree approach to identify pathogens that are of sufficiently low concern that they can be ruled out from consideration as a select agent. In contrast to the MCDA approach, the DSF rules out a pathogen if it fails to meet even one criteria threshold. Both the MCDA and DSF approaches arrived at similar conclusions, suggesting the value of employing the two analytical approaches to add robustness for decision making.

From the Editor and Chair of the Award Committee: 2022 Clemen–KleinmuntzDecision AnalysisBest Paper Award

From the Editor and Chair of the Award Committee: 2022 Clemen–Kleinmuntz<i>Decision Analysis</i>Best Paper Award

Closing the Gap Between Decision Analysis and Policy Analysts Before the Next Pandemic

Decision analysis (DA) is an explicitly prescriptive discipline that separates beliefs about uncertainties from value preferences in modeling to support decision making. Researchers have been advancing DA tools for the last 60 years to support decision makers handling complex decisions requiring subjective judgments. Recently, some DA researchers and practitioners wondered whether the difficult decisions made during the COVID-19 pandemic regarding testing, masking, closing and reopening businesses, allocating ventilators, and prioritizing vaccines would have been improved with more DA involvement. With its focus on quantifying uncertainties, value trade-offs, and risk attitudes, DA should have been a valuable tool for decision makers during the pandemic. To influence decisions, DA applications require interactions with policymakers and experts to construct formal representations of the decision frame, elicit uncertainties, and assess risk tolerances and trade-offs among competing objectives. Unfortunately, such involvement of decision analysts in the process of decision making and policy setting did not occur during much of the COVID-19 pandemic. This lack of participation may have been partly because many decision makers were unaware of when DA could be valuable in helping with the challenges of the COVID-19 pandemic. In addition, decision analysts were perhaps not sufficiently adept at inserting themselves into the policy process at critical junctures when their expertise could have been helpful. Funding: This research was partially supported by the U.S. Department of Homeland Security through the Center for Accelerating Operational Efficiency at Arizona State University.