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PurposeDespite rapid success in bringing SARS-CoV-2 vaccines to distribution by multiple pharmaceutical corporations, supply chain failures in production and distribution can plague pandemic recovery. This review analyzes and addresses gaps in modeling supply chain resilience in general and specifically for vaccines in order to guide researchers and practitioners alike to improve critical function of vaccine supply chains in the face of inevitable disruptions.Design/methodology/approachSystematic review of the literature on modeling supply chain resilience from 2007 to 2020 is analyzed in tandem with the vaccine supply chain manufacturing literature. These trends are then used to apply a novel matrix analysis to seven Securities and Exchange Commission (SEC) annual filings of pharmaceutical corporations involved in COVID-19 vaccine manufacture and distribution.FindingsPharmaceutical corporations favor efficiency as they navigate regulatory, economic and other threats to their vaccine supply chains, neglecting resilience – absorption, adaptation and recovery from inevitable and unexpected disruptions. However, explicitly applying resilience analytics to the vaccine supply chain and further leveraging emerging network science tools found in the academic literature, such as artificial intelligence (AI), stress tests and digital twins, will help supply chain managers to better quantify efficiency/resilience tradeoffs across all associated networks/domains and support optimal system performance post disruption.Originality/valueThis is the first review addressing resilience analytics in vaccine supply chains and subsequent extension to operational management through novel matrix analyses of SEC Filings. The authors provide analyses and recommendations that facilitate resilience quantification capabilities for vaccine supply chain managers, regulatory agencies and corporate stakeholders and are especially relevant for pandemic response, including application to the SARS-CoV-2 vaccines.

The COVID-19 pandemic has illustrated the fragility of food security and associated supply chains for remote and Indigenous communities. Here we highlight challenges faced by the Tribal Population of Noepe (Martha’s Vineyard) and argue for the inclusion of Resilience-by-Design and Resilience-by-Intervention in supply chain management. Indigenous and remote communities face difficulties in times of supply chain disruption. Here the authors comment on challenges faced by the Tribal Population of Noepe (Martha’s Vineyard) and argue for the inclusion of Resilience-by-Design and Resilience-by-Intervention in supply chain management.

The COVID-19 pandemic has significantly disrupted influenza forecasting, making it challenging for hospitals to anticipate the severity of upcoming flu seasons relative to typical annual respiratory virus patterns. Even for influenza, health facilities often lack precise information on potential influenza surges, which hinders hospital management’s ability to anticipate necessary changes with respect to hospital staffing and resource allocation for an influx of patients. This study addresses this critical gap by developing an enhanced predictive model for pediatric influenza hospitalizations in Massachusetts. By integrating data from the Health and Human Services (HHS) Protect Public Data Hub, Centers for Disease Control and Prevention (CDC) FluSurv-NET, U.S. Department of Transportation (DOT) mobility data, and regional hospitalization rates, we demonstrate how recent improvements in data analytics and population tracking can amplify disease forecasts, and more precisely anticipate hospital burden relative to historical patient intake and hospital utilization trends.

The Arctic is experiencing substantial increases in human activity in areas ranging from fossil fuel and mineral extraction to transport along Arctic waterways. Such actions may yield new sources of economic benefits and further objectives to promote national defense, yet they may also generate potential risks to the Arctic environment. As such, concerns from various stakeholders have been raised regarding how to make Arctic operations better meet sustainability goals and balance defense and economic objectives with environmental degradation. This article describes how decision analytical tools, such as multicriteria decision analysis (MCDA), may help identify policies and project proposals that minimize the potential for environmental degradation within a framework of maximizing economic, industrial, and defense objectives. Specifically, MCDA conducts value tradeoffs to assess the utility of various decision alternatives against disparate criteria; for this case, this includes the evaluation of Arctic operation sustainability. This article demonstrates through an example of industrial mining in Greenland how MCDA might serve as a tool to guide uncertain decisions for various Arctic projects, and potentially indicate opportunities to structure such projects to provide greater sustainability for their longer-term operations.

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. Graphical Abstract Biosecurity policies and practices must be updated to accommodate the novel challenges associated with synthetic biology and to maximize technological benefits while minimizing its dual‐use potential. This Commentary proposes three strategies to improve biosecurity.

The rapid digitization of healthcare through electronic health records (EHRs) and artificial intelligence (AI) is transforming clinical decision-making, data integration, and healthcare delivery. However, increasing dependence on interconnected digital systems introduces significant cybersecurity, interoperability, ethical, and operational challenges that EHRs already face, further compounded by the operational complexity of military-civilian healthcare environments where information-sharing requirements are highly sensitive. AI-enabled systems intensify these concerns through opaque decision processes, extensive data demands, and expanding attack surfaces. This article introduces and applies an integrated cybersecurity framework that advances beyond traditional purple teaming by incorporating human factors, governance, ethical considerations, and operational continuity into resilience planning. The paper highlights how this approach can improve secure interoperability, identify systemic vulnerabilities earlier in the development and deployment lifecycle, and support more resilient military-civilian health data exchange architectures. The article further argues that resilience in digital healthcare systems cannot be achieved through technical safeguards alone but instead requires a sociotechnical framework that balances cybersecurity and clinical operations. Originally conceptualized in the context of the biotechnology governance, this paper extends violet teaming into the domain of military-civilian electronic health record interoperability through an illustrative vignette, demonstrating how the framework may support proactive resilience, governance, and cybersecurity integration within complex healthcare data ecosystems, ultimately mitigating risks to patient safety and human life.

State governments in the U.S. have been facing difficult decisions involving tradeoffs between economic and health-related outcomes during the COVID-19 pandemic. Despite evidence of the effectiveness of government-mandated restrictions mitigating the spread of contagion, these orders are stigmatized due to undesirable economic consequences. This tradeoff resulted in state governments employing mandates at widely different ways. We compare the different policies states implemented during periods of restriction (“lockdown”) and reopening with indicators of COVID-19 spread and consumer card spending at each state during the first “wave” of the pandemic in the U.S. between March and August 2020. We find that while some states enacted reopening decisions when the incidence rate of COVID-19 was minimal or sustained in its relative decline, other states relaxed socioeconomic restrictions near their highest incidence and prevalence rates experienced so far. Nevertheless, all states experienced similar trends in consumer card spending recovery, which was strongly correlated with reopening policies following the lockdowns and relatively independent from COVID-19 incidence rates at the time. Our findings suggest that consumer card spending patterns can be attributed to government mandates rather than COVID-19 incidence in the states. We estimate the recovery in states that reopened in late April was more than the recovery in states that did not reopen in the same period– 15% for consumer card spending and 18% for spending by high income households. This result highlights the important role of state policies in minimizing health impacts while promoting economic recovery and helps planning effective interventions in subsequent waves and immunization efforts.

Letter to the Editor

Technology innovation is inherently uncertain. The risk–benefit divide for such innovation is a classical debate within scholarly literature and is often framed on a monetary scale where innovation approval is granted if benefit outweighs risk. However, such discussion leaves out a critical yet subjective vein of discussion within the innovation evaluation process — stakeholder context. Specifically, regulators and technology developers are often described as having respective motivations that are often at odds with one another. In theory, efforts towards balancing risk and benefit for technology evaluation should be driven by relatively efficient, inexpensive, robust methods, and processes. In practice, however, technology evaluation is often expensive, slow, and often of questionable quality for new and emerging technologies. Literature often frames the innovation-regulation tradeoff as a zero-sum game driven by regulators and developers that are inherently at odds with one another. However, we argue that such a relationship is actually worse than zero-sum and is a classic framing problem as described by Kahneman and Tversky. Specifically, the divergent frames adopted by regulators and technology developers, respectively, can drastically affect their perception of risk and tolerance for further development and commercialization of a given technology. There are known and natural solutions to such problems that can smooth the path towards realizing the societal potential of emerging technologies.