The Costs of Future Polio Risk Management Policies

Decisionmakers need information about the anticipated future costs of maintaining polio eradication as a function of the policy options under consideration. Given the large portfolio of options, we reviewed and synthesized the existing cost data relevant to current policies to provide context for future policies. We model the expected future costs of different strategies for continued vaccination, surveillance, and other costs that require significant potential resource commitments. We estimate the costs of different potential policy portfolios for low-, middle-, and high-income countries to demonstrate the variability in these costs. We estimate that a global transition from routine immunization with oral poliovirus vaccine (OPV) to inactivated poliovirus vaccine (IPV) would increase the costs of managing polio globally, although routine IPV use remains less costly than routine OPV use with supplemental immunization activities. The costs of surveillance and a stockpile, while small compared to routine vaccination costs, represent important expenditures to ensure adequate response to potential outbreaks. The uncertainty and sensitivity analyses highlight important uncertainty in the aggregated costs and demonstrates that the discount rate and uncertainty in price and administration cost of IPV drives the expected incremental cost of routine IPV vs. OPV immunization.     

Evaluation of Response Scenarios to Potential Polio Outbreaks Using Mathematical Models

Appropriate response to polio outbreaks represents an important prerequisite for achieving and maintaining global polio eradication. We use an existing dynamic disease transmission model to evaluate the impact of different aspects of immunization campaigns in response to polio outbreaks occurring in previously polio-free areas. This analysis yields several important insights about response strategies. We find that delay in response represents a crucial risk factor for occurrence of large outbreaks and we characterize the tradeoffs associated with delaying the initial response to achieve better population coverage. We also demonstrate that controlling most potential outbreaks will likely require at least three immunization rounds, although the impact of the optimal interval between rounds varies. Finally, long after oral poliovirus vaccine cessation the choice of target age groups during a response represents an important consideration.     

Equity in Social Risk: Some Empirical Observations

In societal risk analysis the equity of the distribution of risks is often an important consideration owing to the special nature of health risks. We empirically validate some assumptions about equity that have been discussed in the decision analytic literature. Our results show that the way fatalities are distributed throughout a society is considered along with the number of fatalities in evaluating alternative policies involving mortality risks. The concepts of ex ante equity and ex post equity are both shown to be important in judgments of fairness. We next present a decision model based on multiattribute preference theory incorporating the number of fatalities, as well as ex ante equity and ex post equity. When ex ante equity and ex post equity are positively weighted in this fair-risk model , options with more equal risk distributions are ranked higher. Next we empirically show that the distribution of benefits has an impact on judgments of fairness. The fair-risk model does not include information on the benefits distribution, so it would apply when benefits are distributed equally or when the decision maker wishes to not include benefits in the model. We briefly discuss how the notion of proportional equity can incorporate benefits into judgments of the fairness of risk distributions. We then include benefits in a more general model in which fair risk-benefit combinations are those that are exchange equitable. A key implication of this envy-free risk–benefit model is that an unequal distribution of risks may be preferred if it is accompanied by a compensatory differential in benefits consistent with peoples' preference tradeoffs between received benefits and assumed risks. Finally, we discuss how perceived deservedness may influence judgments about equity. We conclude with a discussion of the implications of research on alternative notions of equity for policy makers dealing with social risks.     

Risk–risk governance in a low-carbon future: Exploring institutional,technological, and behavioral tradeoffs in climate geoengineering pathways

Deliberations are underway to utilize increasingly radical technological options to help address climate change and stabilize the climatic system. Collectively, these options are often referred to as “climate geoengineering.” Deployment of such options, however, can create wicked tradeoffs in governance and require adaptive forms of risk management. In this study, we utilize a large and novel set of qualitative expert interview data to more deeply and systematically explore the types of risk–risk tradeoffs that may emerge from the use of 20 different climate geoengineering options, 10 that focus on carbon dioxide or greenhouse gas removal, and 10 that focus on solar radiation management and reflecting sunlight. We specifically consider: What risks does the deployment of these options entail? What types of tradeoffs may emerge through their deployment? We apply a framework that clusters risk–risk tradeoffs into institutional and governance, technological and environmental, and behavioral and temporal dimensions. In doing so, we offer a more complete inventory of risk–risk tradeoffs than those currently available within the respective risk-assessment, energy-systems, and climate-change literatures, and we also point the way toward future research gaps concerning policy, deployment, and risk management.     

Efficient frontiers for electricity procurement by an LDC with multiple purchase options

In meeting its retail sales obligations, management of a local distribution company (LDC) must determine the extent to which it should rely on spot markets, forward contracts, and the increasingly popular long-term tolling agreements under which it pays a fee to reserve generator capacity. We address these issues by solving a mathematical programming model to derive the efficient frontier that summarizes the optimal tradeoffs available to the LDC between procurement risk and expected cost. To illustrate the approach, we estimate the expected procurement costs and associated variances that proxy for risk through a spot-price regression for the spot-purchase alternative and a variable-cost regression for the tolling-agreement alternative. The estimated regressions yield the estimates required to determine the efficient frontier. We develop several such frontiers under alternative assumptions as to the forward-contract price and the tolling agreement's capacity payment, and discuss the implications of our results for LDC management.     

Robustness of Risk Maps and Survey Networks to Knowledge Gaps About a New Invasive Pest

In pest risk assessment it is frequently necessary to make management decisions regarding emerging threats under severe uncertainty. Although risk maps provide useful decision support for invasive alien species, they rarely address knowledge gaps associated with the underlying risk model or how they may change the risk estimates. Failure to recognize uncertainty leads to risk‐ignorant decisions and miscalculation of expected impacts as well as the costs required to minimize these impacts. Here we use the information gap concept to evaluate the robustness of risk maps to uncertainties in key assumptions about an invading organism. We generate risk maps with a spatial model of invasion that simulates potential entries of an invasive pest via international marine shipments, their spread through a landscape, and establishment on a susceptible host. In particular, we focus on the question of how much uncertainty in risk model assumptions can be tolerated before the risk map loses its value. We outline this approach with an example of a forest pest recently detected in North America, Sirex noctilio Fabricius. The results provide a spatial representation of the robustness of predictions of S. noctilio invasion risk to uncertainty and show major geographic hotspots where the consideration of uncertainty in model parameters may change management decisions about a new invasive pest. We then illustrate how the dependency between the extent of uncertainties and the degree of robustness of a risk map can be used to select a surveillance network design that is most robust to knowledge gaps about the pest.     

Expert Review on Poliovirus Immunity and Transmission

Successfully managing risks to achieve wild polioviruses (WPVs) eradication and address the complexities of oral poliovirus vaccine (OPV) cessation to stop all cases of paralytic poliomyelitis depends strongly on our collective understanding of poliovirus immunity and transmission. With increased shifting from OPV to inactivated poliovirus vaccine (IPV), numerous risk management choices motivate the need to understand the tradeoffs and uncertainties and to develop models to help inform decisions. The U.S. Centers for Disease Control and Prevention hosted a meeting of international experts in April 2010 to review the available literature relevant to poliovirus immunity and transmission. This expert review evaluates 66 OPV challenge studies and other evidence to support the development of quantitative models of poliovirus transmission and potential outbreaks. This review focuses on characterization of immunity as a function of exposure history in terms of susceptibility to excretion, duration of excretion, and concentration of excreted virus. We also discuss the evidence of waning of host immunity to poliovirus transmission, the relationship between the concentration of poliovirus excreted and infectiousness, the importance of different transmission routes, and the differences in transmissibility between OPV and WPV. We discuss the limitations of the available evidence for use in polio risk models, and conclude that despite the relatively large number of studies on immunity, very limited data exist to directly support quantification of model inputs related to transmission. Given the limitations in the evidence, we identify the need for expert input to derive quantitative model inputs from the existing data.     

Stepping Out of Your Own Shadow: A Didactic Example of How Facing Uncertainty Can Improve Decision-Making

Advocates of quantitative uncertainty analysis (QUA) have invested substantial effort in explaining why uncertainty is a crucial aspect of risk and yet have devoted much less effort to explaining how QUA can improve the risk manager's performance. This paper develops a teaching example, using a personal decision problem with subtle parallels to societal risk management, to show how choices made with increasing appreciation of uncertainty are superior ones. In the hypothetical, five analysts explain the same uncertain prospect (whether to invest in a volatile stock issue), with increasing attention to the nuances of uncertainty. The path through these different perspectives on the decision demonstrates four general points applicable to environmental risk management: (1) Various point estimates with equal claim to being "best estimates" can differ markedly from each other and lead to diametrically different choices; (2) "conservatism" has both relative and absolute meanings, with different implications for decision-making; (3) both inattention to and fixation on "outliers" in the uncertainty distribution can lead the manager astray; and (4) the best QUA is one that helps discriminate among real options, that points to optimum pathways toward new information, and that spurs on the iterative search for new decision options that may outperform any of the initial ones offered.     

Development and Consideration of Global Policies for Managing the Future Risks of Poliovirus Outbreaks: Insights and Lessons Learned Through Modeling

The success of the Global Polio Eradication Initiative promises to bring large benefits, including sustained improvements in quality of life (i.e., cases of paralytic disease and deaths avoided) and costs saved from cessation of vaccination. Obtaining and maintaining these benefits requires that policymakers manage the transition from the current massive use of oral poliovirus vaccine (OPV) to a world without OPV and free of the risks of potential future reintroductions of live polioviruses. This article describes the analytical journey that began in 2001 with a retrospective case study on polio risk management and led to development of dynamic integrated risk, economic, and decision analysis tools to inform global policies for managing the risks of polio. This analytical journey has provided several key insights and lessons learned that will be useful to future analysts involved in similar complex decision-making processes.     

Environmental Surveillance System Characteristics and Impacts on Confidence About No Undetected Serotype 1 Wild Poliovirus Circulation

Surveillance for poliovirus during the polio endgame remains uncertain. Building on prior modeling of the potential for undetected poliovirus transmission for conditions like those in Pakistan and Afghanistan, we use a hypothetical model to explore several key characteristics of the poliovirus environmental surveillance (ES) system (e.g., number and quality of sites, catchment sizes, and sampling frequency) and characterize their impacts on the time required to reach high (i.e., 95%) confidence about no circulation (CNC95%) following the last detected case of serotype 1 wild poliovirus. The nature and quality of the existing and future acute flaccid paralysis (AFP) surveillance and ES system significantly impact the estimated CNC95% for places like Pakistan and Afghanistan. The analysis illustrates the tradeoffs between number of sites, sampling frequency, and catchments sizes, and suggests diminishing returns of increasing these three factors beyond a point that depends on site quality and the location of sites. Limitations in data quality and the hypothetical nature of the model reduce the ability to assess the extent to which actual ES systems offer benefits that exceed their costs. Thus, although poliovirus ES may help to reduce the time required to reach high confidence about the absence of undetected circulation, the effect strongly depends on the ability to establish effective ES sites in high‐risk areas. The costs and benefits of ES require further analysis.     

Model uncertainty and choices made by modelers: lessons learned from the International Atomic Energy Agency model intercomparisons.

The treatment of uncertainties associated with modeling and risk assessment has recently attracted significant attention. The methodology and guidance for dealing with parameter uncertainty have been fairly well developed and quantitative tools such as Monte Carlo modeling are often recommended. However, the issue of model uncertainty is still rarely addressed in practical applications of risk assessment. The use of several alternative models to derive a range of model outputs or risks is one of a few available techniques. This article addresses the often-overlooked issue of what we call "modeler uncertainty," i.e., difference in problem formulation, model implementation, and parameter selection originating from subjective interpretation of the problem at hand. This study uses results from the Fruit Working Group, which was created under the International Atomic Energy Agency (IAEA) BIOMASS program (BIOsphere Modeling and ASSessment). Model-model and model-data intercomparisons reviewed in this study were conducted by the working group for a total of three different scenarios. The greatest uncertainty was found to result from modelers' interpretation of scenarios and approximations made by modelers. In scenarios that were unclear for modelers, the initial differences in model predictions were as high as seven orders of magnitude. Only after several meetings and discussions about specific assumptions did the differences in predictions by various models merge. Our study shows that parameter uncertainty (as evaluated by a probabilistic Monte Carlo assessment) may have contributed over one order of magnitude to the overall modeling uncertainty. The final model predictions ranged between one and three orders of magnitude, depending on the specific scenario. This study illustrates the importance of problem formulation and implementation of an analytic-deliberative process in risk characterization.     

Reassessing the human health benefits from cleaner air

Recent proposals to further reduce permitted levels of air pollution emissions are supported by high projected values of resulting public health benefits. For example, the Environmental Protection Agency recently estimated that the 1990 Clean Air Act Amendment (CAAA) will produce human health benefits in 2020, from reduced mortality rates, valued at nearly $2 trillion per year, compared to compliance costs of $65 billion ($0.065 trillion). However, while compliance costs can be measured, health benefits are unproved: they depend on a series of uncertain assumptions. Among these are that additional life expectancy gained by a beneficiary (with median age of about 80 years) should be valued at about $80,000 per month; that there is a 100% probability that a positive, linear, no-threshold, causal relation exists between PM(2.5) concentration and mortality risk; and that progress in medicine and disease prevention will not greatly diminish this relationship. We present an alternative uncertainty analysis that assigns a positive probability of error to each assumption. This discrete uncertainty analysis suggests (with probability >90% under plausible alternative assumptions) that the costs of CAAA exceed its benefits. Thus, instead of suggesting to policymakers that CAAA benefits are almost certainly far larger than its costs, we believe that accuracy requires acknowledging that the costs purchase a relatively uncertain, possibly much smaller, benefit. The difference between these contrasting conclusions is driven by different approaches to uncertainty analysis, that is, excluding or including discrete uncertainties about the main assumptions required for nonzero health benefits to exist at all.     

Using Structured Decision Making to Help Implement a Precautionary Approach to Endangered Species Management

Endangered species protection is a significant risk management concern throughout North America. An extensive conceptual literature emphasizes the role to be played by precautionary approaches. Risk managers, typically working in concert with concerned stakeholders, frequently cite the concept as key to their efforts to prevent extinctions. Little has been done, however, to evaluate the multidimensional impacts of precautionary frameworks or to assist in the examination of competing precautionary risk management options as part of an applied risk management decision framework. In this article we describe how decision-aiding techniques can assist in the creation and analysis of alternative precautionary strategies, using the example of a multistakeholder committee charged with protection of endangered Cultus Lake salmon on the Canadian west coast. Although managers were required to adopt a precautionary approach, little attention had been given to how quantitative analyses could be used to help define the concept or to how a precautionary approach might be implemented in the face of difficult economic, social, and biological tradeoffs. We briefly review key steps in a structured decision-making (SDM) process and discuss how this approach was implemented to help bound the management problem, define objectives and performance measures, develop management alternatives, and evaluate their consequences. We highlight the role of strategy tables, employed to help participants identify, alternative management options. We close by noting areas of agreement and disagreement among participants and discuss the implications of decision-focused processes for other precautionary resource management efforts.     

Modeling Poliovirus Risks and the Legacy of Polio Eradication

This introduction to the special issue on modeling poliovirus risks provides context about historical efforts to manage polioviruses and reviews the insights from models developed to support risk management and policy development. Following an overview of the contents of the special issue, the introduction explores the road ahead and offers perspective on the legacy of polio eradication.     

Public Perceptions of Regulatory Costs,Their Uncertainty and Interindividual Distribution

Public perceptions of both risks and regulatory costs shape rational regulatory choices. Despite decades of risk perception studies, this article is the first on regulatory cost perceptions. A survey of 744 U.S. residents probed: (1) How knowledgeable are laypeople about regulatory costs incurred to reduce risks? (2) Do laypeople see official estimates of cost and benefit (lives saved) as accurate? (3) (How) do preferences for hypothetical regulations change when mean‐preserving spreads of uncertainty replace certain cost or benefit? and (4) (How) do preferences change when unequal interindividual distributions of hypothetical regulatory costs replace equal distributions? Respondents overestimated costs of regulatory compliance, while assuming agencies underestimate costs. Most assumed agency estimates of benefits are accurate; a third believed both cost and benefit estimates are accurate. Cost and benefit estimates presented without uncertainty were slightly preferred to those surrounded by “narrow uncertainty” (a range of costs or lives entirely within a personally‐calibrated zone without clear acceptance or rejection of tradeoffs). Certain estimates were more preferred than “wide uncertainty” (a range of agency estimates extending beyond these personal bounds, thus posing a gamble between favored and unacceptable tradeoffs), particularly for costs as opposed to benefits (but even for costs a quarter of respondents preferred wide uncertainty to certainty). Agency‐acknowledged uncertainty in general elicited mixed judgments of honesty and trustworthiness. People preferred egalitarian distributions of regulatory costs, despite skewed actual cost distributions, and preferred progressive cost distributions (the rich pay a greater than proportional share) to regressive ones. Efficient and socially responsive regulations require disclosure of much more information about regulatory costs and risks.     

Optimal Strategies for Interception,Detection, and Eradication in Plant Biosecurity

The introduction of invasive species causes damages from the economic and ecological point of view. Interception of plant pests and eradication of the established populations are two management options to prevent or limit the risk posed by an invasive species. Management options generate costs related to the interception at the point of entry, and the detection and eradication of established field populations. Risk managers have to decide how to allocate resources between interception, field detection, containment, and eradication minimizing the expected total costs. In this work is considered an optimization problem aiming at determining the optimal allocation of resources to minimize the expected total costs of the introduction of Bemisia tabaci‐transmitted viruses in Europe. The optimization problem takes into account a probabilistic model for the estimation of the percentage of viruliferous insect populations arriving through the trade of commodities, and a population dynamics model describing the process of the vector populations' establishment and spread. The time of field detection of viruliferous insect populations is considered as a random variable. The solution of the optimization problem allows to determine the optimal allocation of the search effort between interception and detection/eradication. The behavior of the search effort as a function of efficacy or search in interception and in detection is then analyzed. The importance of the vector population growth rate and the probability of virus establishment are also considered in the analysis of the optimization problem.     

Risk‐Based Approach for Microbiological Food Safety Management in the Dairy Industry: The Case of Listeria monocytogenes in Soft Cheese Made from Pasteurized Milk

According to Codex Alimentarius Commission recommendations, management options applied at the process production level should be based on good hygiene practices, HACCP system, and new risk management metrics such as the food safety objective. To follow this last recommendation, the use of quantitative microbiological risk assessment is an appealing approach to link new risk‐based metrics to management options that may be applied by food operators. Through a specific case study, Listeria monocytogenes in soft cheese made from pasteurized milk, the objective of the present article is to practically show how quantitative risk assessment could be used to direct potential intervention strategies at different food processing steps. Based on many assumptions, the model developed estimates the risk of listeriosis at the moment of consumption taking into account the entire manufacturing process and potential sources of contamination. From pasteurization to consumption, the amplification of a primo‐contamination event of the milk, the fresh cheese or the process environment is simulated, over time, space, and between products, accounting for the impact of management options, such as hygienic operations and sampling plans. A sensitivity analysis of the model will help orientating data to be collected prioritarily for the improvement and the validation of the model. What‐if scenarios were simulated and allowed for the identification of major parameters contributing to the risk of listeriosis and the optimization of preventive and corrective measures.     

Modeling intelligent adversaries for terrorism risk assessment: some necessary conditions for adversary models

Intelligent adversary modeling has become increasingly important for risk analysis, and a number of different approaches have been proposed for incorporating intelligent adversaries in risk analysis models. However, these approaches are based on a range of often-implicit assumptions about the desirable properties of intelligent adversary models. This "Perspective" paper aims to further risk analysis for situations involving intelligent adversaries by fostering a discussion of the desirable properties for these models. A set of four basic necessary conditions for intelligent adversary models is proposed and discussed. These are: (1) behavioral accuracy to the degree possible, (2) computational tractability to support decision making, (3) explicit consideration of uncertainty, and (4) ability to gain confidence in the model. It is hoped that these suggested necessary conditions foster discussion about the goals and assumptions underlying intelligent adversary modeling in risk analysis.     

A Multicriteria Decision Analysis Model and Risk Assessment Framework for Carbon Capture and Storage

Multicriteria decision analysis (MCDA) has been applied to various energy problems to incorporate a variety of qualitative and quantitative criteria, usually spanning environmental, social, engineering, and economic fields. MCDA and associated methods such as life‐cycle assessments and cost‐benefit analysis can also include risk analysis to address uncertainties in criteria estimates. One technology now being assessed to help mitigate climate change is carbon capture and storage (CCS). CCS is a new process that captures CO₂ emissions from fossil‐fueled power plants and injects them into geological reservoirs for storage. It presents a unique challenge to decisionmakers (DMs) due to its technical complexity, range of environmental, social, and economic impacts, variety of stakeholders, and long time spans. The authors have developed a risk assessment model using a MCDA approach for CCS decisions such as selecting between CO₂ storage locations and choosing among different mitigation actions for reducing risks. The model includes uncertainty measures for several factors, utility curve representations of all variables, Monte Carlo simulation, and sensitivity analysis. This article uses a CCS scenario example to demonstrate the development and application of the model based on data derived from published articles and publicly available sources. The model allows high‐level DMs to better understand project risks and the tradeoffs inherent in modern, complex energy decisions.