Evaluation of individual and ensemble probabilistic forecasts of COVID-19 mortality in the United States.

Short-term probabilistic forecasts of the trajectory of the COVID-19 pandemic in the United States have served as a visible and important communication channel between the scientific modeling community and both the general public and decision-makers. Forecasting models provide specific, quantitative, and evaluable predictions that inform short-term decisions such as healthcare staffing needs, school closures, and allocation of medical supplies. Starting in April 2020, the US COVID-19 Forecast Hub (https://covid19forecasthub.org/) collected, disseminated, and synthesized tens of millions of specific predictions from more than 90 different academic, industry, and independent research groups. A multimodel ensemble forecast that combined predictions from dozens of groups every week provided the most consistently accurate probabilistic forecasts of incident deaths due to COVID-19 at the state and national level from April 2020 through October 2021. The performance of 27 individual models that submitted complete forecasts of COVID-19 deaths consistently throughout this year showed high variability in forecast skill across time, geospatial units, and forecast horizons. Two-thirds of the models evaluated showed better accuracy than a naïve baseline model. Forecast accuracy degraded as models made predictions further into the future, with probabilistic error at a 20-wk horizon three to five times larger than when predicting at a 1-wk horizon. This project underscores the role that collaboration and active coordination between governmental public-health agencies, academic modeling teams, and industry partners can play in developing modern modeling capabilities to support local, state, and federal response to outbreaks.

Framework for multirisk climate scenarios across system receptors with application to the Metropolitan City of Venice.

Climate change influences the frequency of extreme events that affect both human and natural systems. It requires systemic climate change adaptation to address the complexity of risks across multiple domains and tackle the uncertainties of future scenarios. This paper introduces a multirisk analysis of climate hazard, exposure, vulnerability, and risk severity, specifically designed to hotspot geographic locations and prioritize system receptors that are affected by climate-related extremes. The analysis is demonstrated for the Metropolitan City of Venice. Representative scenarios (RCP4.5 and RCP8.5) of climate threats (i.e., storm surges, pluvial flood, heat waves, and drought) are selected and represented by projections of Regional Climate Models for a 30-year period (2021-2050). A sample of results is as follows. First, an increase in the risk is largely due to drought, pluvial flood, and storm surge, depending on the areas of interest, with the overall situation worsening under the RCP8.5 scenario. Second, particular locations have colocated vulnerable receptors at higher risk, concentrated in the urban centers (e.g., housing, railways, roads) and along the coast (e.g., beaches, wetlands, primary sector). Third, risk communication of potential environmental and socio-economic losses via the multirisk maps is useful to stakeholders and public administration. Fourth, the multirisk maps recommend priorities for future investigation and risk management, such as collection of sensor data, elaboration of mitigations, and adaptation plans at hotspot locations.

Sensitivity analysis for a participatory approach to enhance the climate resilience of Venice, Italy.

Increases in the magnitudes and frequencies of climate-related extreme events are redistributing risk across coastal systems, including their environmental, economic, and social components. Consequently, stakeholders (SHs) are faced with long-term challenges and complex information when managing assets, services, and uses of the coast. In this context, SH engagement is a key step for risk management and in the preparation of resilience plans to respond and adapt to climate change. This paper develops a participatory method to identify and prioritize a set of risk measures, combining multi-criteria analysis with sensitivity analysis. The process involved local and regional authorities of the Veneto region testing the method, including national, regional, and local government, catchment officers, research organizations, natural parks managers and Non-Governmental Organizations (NGOs). SHs identified and ranked a range of adaptation measures to increase climate resilience, with a focus on coastal risk in the Venice lagoon. Results demonstrate that the sensitivity analysis provides useful information on how different sectors of expertise can influence the ranking of the identified risk management measures, highlighting the value of investigating the preferences or priorities of different SH groups within the definition of adaptation plans.

Prioritization of Resilience Initiatives for Climate-Related Disasters in the Metropolitan City of Venice.

Increases in the magnitude and frequency of climate and other disruptive factors are placing environmental, economic, and social stresses on coastal systems. This is further exacerbated by land use transformations, urbanization, over-tourism, sociopolitical tensions, technological innovations, among others. A scenario-informed multicriteria decision analysis (MCDA) was applied in the Metropolitan City of Venice integrating qualitative (i.e., local stakeholder preferences) and quantitative information (i.e., climate-change projections) with the aim of enhancing system resilience to multiple climate-related threats. As part of this analysis, different groups of local stakeholders (e.g., local authorities, civil protection agencies, SMEs, NGOs) were asked to identify critical functions that needs to be sustained. Various policy initiatives were considered to support these critical functions. The MCDA was used to rank the initiatives across several scenarios describing main climate threats (e.g., storm surges, floods, heatwaves, drought). We found that many climate change scenarios were considered to be disruptive to stakeholders and influence alternative ranking. The management alternatives acting on physical domain generally enhance resilience across just a few scenarios while cognitive and informative initiatives provided resilience enhancement across most scenarios considered. With uncertainty of multiple stressors along with projected climate variability, a portfolio of cognitive and physical initiatives is recommended to enhance resilience.

Building biosecurity for synthetic biology.

The fast-paced field of synthetic biology is fundamentally changing the global biosecurity framework. Current biosecurity regulations and strategies are based on previous governance paradigms for pathogen-oriented security, recombinant DNA research, and broader concerns related to genetically modified organisms (GMOs). Many scholarly discussions and biosecurity practitioners are therefore concerned that synthetic biology outpaces established biosafety and biosecurity measures to prevent deliberate and malicious or inadvertent and accidental misuse of synthetic biology's processes or products. This commentary proposes three strategies to improve biosecurity: Security must be treated as an investment in the future applicability of the technology; social scientists and policy makers should be engaged early in technology development and forecasting; and coordination among global stakeholders is necessary to ensure acceptable levels of risk.

System models for resilience in gerontology: application to the COVID-19 pandemic.

The care needs for aging adults are increasing burdens on health systems around the world. Efforts minimizing risk to improve quality of life and aging have proven moderately successful, but acute shocks and chronic stressors to an individual's systemic physical and cognitive functions may accelerate their inevitable degradations. A framework for resilience to the challenges associated with aging is required to complement on-going risk reduction policies, programs and interventions. Studies measuring resilience among the elderly at the individual level have not produced a standard methodology. Moreover, resilience measurements need to incorporate external structural and system-level factors that determine the resources that adults can access while recovering from aging-related adversities. We use the National Academies of Science conceptualization of resilience for natural disasters to frame resilience for aging adults. This enables development of a generalized theory of resilience for different individual and structural contexts and populations, including a specific application to the COVID-19 pandemic.

Use and Misuse of MCDA to Support Decision Making Informed by Risk.

Recent guidelines for risk-informed decision making (RIDM) provide a gold-standard for how to incorporate probabilistic risk models in conjunction with other considerations in a decision process. Nevertheless, risk quantification using probabilistic and statistical methods is difficult in situations where threat, vulnerability, and consequences are highly uncertain and risk quantification. In such situations a wider variety of methods could be employed, which we call decision making informed by risk (DMIR) combining risk and decision analytics. Risk informed decision making (RIDM) can be considered as a special case of DMIR. Multi criteria decision analysis (MCDA) often serves as a basis for DMIR in order to flexibly accommodate different levels of analytical detail. DMIR often involves artful use of proxy variables that correlate with, and are more measurable than, underlying factors of interest. This article introduces the notion of DMIR and discusses the use of MCDA in its application in the context of risk-based problems. MCDA-based risk analyses identify metrics associated with threats of concern and system vulnerabilities, characterize the way in which alternative actions can affect these threats and vulnerabilities, and ultimately synthesize this information to compare, prioritize, or select alternative mitigation strategies. Simple linear additive MCDA models often integrate these inputs, but the same simplicity can limit such approaches and create pitfalls and more advanced models including multiplicative relationships can be warranted. This essay qualitatively explores the critical practitioner questions of how and when the use of linear multicriteria models creates significant problems, and how to avoid them.

The Need to Reconcile Concepts that Characterize Systems Facing Threats.

Desirable system performance in the face of threats has been characterized by various management concepts. Through semistructured interviews with editors of journals in the fields of emergency response and systems management, a literature review, and professional judgment, we identified nine related and often interchangeably used system performance concepts: adaptability, agility, reliability, resilience, resistance, robustness, safety, security, and sustainability. A better understanding of these concepts will allow system planners to pursue management strategies best suited to their unique system dynamics and specific objectives of good performance. We analyze expert responses and review the linguistic definitions and mathematical framing of these concepts to understand their applications. We find a lack of consensus on their usage between interview subjects, but by using the mathematical framing to enrich the linguistic definitions, we formulate comparative visualizations and propose distinct definitions for the nine concepts. We present a conceptual framing to relate the concepts for management purposes.

The challenges of data usage for the United States' COVID-19 response.

During the coronavirus pandemic, policy makers need to interpret available public health data to make decisions affecting public health. However, the United States' coronavirus response faced data gaps, inadequate and inconsistent definitions of data across different governmental jurisdictions, ambiguous timing in reporting, problems in accessing data, and changing interpretations from scientific institutions. These present numerous problems for the decision makers relying on this information. This paper documents some of the data pitfalls in coronavirus public health data reporting, as identified by the authors in the course of supporting data management for New England's coronavirus response. We provide recommendations for individuals to collect data more effectively during emergency situations such as a COVID-19 surge, as well as recommendations for institutions to provide more meaningful data for various users to access. Through this, we hope to motivate action to avoid data pitfalls during public health responses in the future.

Risk Governance of Emerging Technologies Demonstrated in Terms of its Applicability to Nanomaterials.

Nanotechnologies have reached maturity and market penetration that require nano-specific changes in legislation and harmonization among legislation domains, such as the amendments to REACH for nanomaterials (NMs) which came into force in 2020. Thus, an assessment of the components and regulatory boundaries of NMs risk governance is timely, alongside related methods and tools, as part of the global efforts to optimise nanosafety and integrate it into product design processes, via Safe(r)-by-Design (SbD) concepts. This paper provides an overview of the state-of-the-art regarding risk governance of NMs and lays out the theoretical basis for the development and implementation of an effective, trustworthy and transparent risk governance framework for NMs. The proposed framework enables continuous integration of the evolving state of the science, leverages best practice from contiguous disciplines and facilitates responsive re-thinking of nanosafety governance to meet future needs. To achieve and operationalise such framework, a science-based Risk Governance Council (RGC) for NMs is being developed. The framework will provide a toolkit for independent NMs' risk governance and integrates needs and views of stakeholders. An extension of this framework to relevant advanced materials and emerging technologies is also envisaged, in view of future foundations of risk research in Europe and globally.

Multicriteria Decision Framework for Cybersecurity Risk Assessment and Management.

Risk assessors and managers face many difficult challenges related to novel cyber systems. Among these challenges are the constantly changing nature of cyber systems caused by technical advances, their distribution across the physical, information, and sociocognitive domains, and the complex network structures often including thousands of nodes. Here, we review probabilistic and risk-based decision-making techniques applied to cyber systems and conclude that existing approaches typically do not address all components of the risk assessment triplet (threat, vulnerability, consequence) and lack the ability to integrate across multiple domains of cyber systems to provide guidance for enhancing cybersecurity. We present a decision-analysis-based approach that quantifies threat, vulnerability, and consequences through a set of criteria designed to assess the overall utility of cybersecurity management alternatives. The proposed framework bridges the gap between risk assessment and risk management, allowing an analyst to ensure a structured and transparent process of selecting risk management alternatives. The use of this technique is illustrated for a hypothetical, but realistic, case study exemplifying the process of evaluating and ranking five cybersecurity enhancement strategies. The approach presented does not necessarily eliminate biases and subjectivity necessary for selecting countermeasures, but provides justifiable methods for selecting risk management actions consistent with stakeholder and decisionmaker values and technical data.

Advances on a Decision Analytic Approach to Exposure-Based Chemical Prioritization.

The volume and variety of manufactured chemicals is increasing, although little is known about the risks associated with the frequency and extent of human exposure to most chemicals. The EPA and the recent signing of the Lautenberg Act have both signaled the need for high-throughput methods to characterize and screen chemicals based on exposure potential, such that more comprehensive toxicity research can be informed. Prior work of Mitchell et al. using multicriteria decision analysis tools to prioritize chemicals for further research is enhanced here, resulting in a high-level chemical prioritization tool for risk-based screening. Reliable exposure information is a key gap in currently available engineering analytics to support predictive environmental and health risk assessments. An elicitation with 32 experts informed relative prioritization of risks from chemical properties and human use factors, and the values for each chemical associated with each metric were approximated with data from EPA's CP_CAT database. Three different versions of the model were evaluated using distinct weight profiles, resulting in three different ranked chemical prioritizations with only a small degree of variation across weight profiles. Future work will aim to include greater input from human factors experts and better define qualitative metrics.

Co-evolution of physical and social sciences in synthetic biology.

Emerging technologies research often covers various perspectives in disciplines and research areas ranging from hard sciences, engineering, policymaking, and sociology. However, the interrelationship between these different disciplinary domains, particularly the physical and social sciences, often occurs many years after a technology has matured and moved towards commercialization. Synthetic biology may serve an exception to this idea, where, since 2000, the physical and the social sciences communities have increasingly framed their research in response to various perspectives in biological engineering, risk assessment needs, governance challenges, and the social implications that the technology may incur. This paper reviews a broad collection of synthetic biology literature from 2000-2016, and demonstrates how the co-development of physical and social science communities has grown throughout synthetic biology's earliest stages of development. Further, this paper indicates that future co-development of synthetic biology scholarship will assist with significant challenges of the technology's risk assessment, governance, and public engagement needs, where an interdisciplinary approach is necessary to foster sustainable, risk-informed, and societally beneficial technological advances moving forward.

A Definition and Categorization System for Advanced Materials: The Foundation for Risk-Informed Environmental Health and Safety Testing.

Novel materials with unique or enhanced properties relative to conventional materials are being developed at an increasing rate. These materials are often referred to as advanced materials (AdMs) and they enable technological innovations that can benefit society. Despite their benefits, however, the unique characteristics of many AdMs, including many nanomaterials, are poorly understood and may pose environmental safety and occupational health (ESOH) risks that are not readily determined by traditional risk assessment methods. To assess these risks while keeping up with the pace of development, technology developers and risk assessors frequently employ risk-screening methods that depend on a clear definition for the materials that are to be assessed (e.g., engineered nanomaterial) as well as a method for binning materials into categories for ESOH risk prioritization. The term advanced material lacks a consensus definition and associated categorization or grouping system for risk screening. In this study, we aim to establish a practitioner-driven definition for AdMs and a practitioner-validated framework for categorizing AdMs into conceptual groupings based on material characteristics. Results from multiple workshops and interviews with practitioners provide consistent differentiation between AdMs and conventional materials, offer functional nomenclature for application science, and provide utility for future ESOH risk assessment prioritization. The definition and categorization framework established here serve as a first step in determining if and when there is a need for specific ESOH and regulatory screening for an AdM as well as the type and extent of risk-related information that should be collected or generated for AdMs and AdM-enabled technologies.

Community-Driven Hypothesis Testing: A Solution for the Tragedy of the Anticommons.

Shared ownership of property and resources is a longstanding challenge throughout history that has been amplifying with the increasing development of industrial and postindustrial societies. Where governments, project planners, and commercial developers seek to develop new infrastructure, industrial projects, and various other land-and resource-intensive tasks, veto power shared by various local stakeholders can complicate or halt progress. Risk communication has been used as an attempt to address stakeholder concerns in these contexts, but has demonstrated shortcomings. These coordination failures between project planners and stakeholders can be described as a specific kind of social dilemma that we describe as the "tragedy of the anticommons." To overcome such dilemmas, we demonstrate how a two-step process can directly address public mistrust of project planners and public perceptions of limited decision-making authority. This approach is examined via two separate empirical field experiments in Portugal and Tunisia, where public resistance and anticommons problems threatened to derail emerging industrial projects. In both applications, an intervention is undertaken to address initial public resistance to such projects, where specific public stakeholders and project sponsors collectively engaged in a hypothesis-testing process to identify and assess human and environmental health risks associated with proposed industrial facilities. These field experiments indicate that a rigorous attempt to address public mistrust and perceptions of power imbalances and change the pay-off structure of the given dilemma may help overcome such anticommons problems in specific cases, and may potentially generate enthusiasm and support for such projects by local publics moving forward.