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The increasingly global context in which businesses operate supports innovation, but also increases uncertainty around supply chain disruptions. The COVID-19 pandemic clearly shows the lack of resilience in supply chains and the impact that disruptions may have on a global network scale as individual supply chain connections and nodes fail. This cascading failure underscores the need for the network analysis and advanced resilience analytics we find lacking in the existing supply chain literature. This paper reviews supply chain resilience literature that focuses on resilience modeling and quantification and connects the supply chain to other networks, including transportation and command and control. We observe a fast increase in the number of relevant papers (only 47 relevant papers were published in 2007–2016, while 94 were found in 2017–2019). We observe that specific disruption scenarios are used to develop and test supply chain resilience models, while uncertainty associated with threats including consideration of “unknown unknowns” remains rare. Publications that utilize more advanced models often focus just on supply chain networks and exclude associated system components such as transportation and command and control (C2) networks, which creates a gap in the research that needs to be bridged. The common goal of supply chain modeling is to optimize efficiency and reduce costs, but trade-offs of efficiency and leanness with flexibility and resilience may not be fully addressed. We conclude that a comprehensive approach to network resilience quantification encompassing the supply chain in the context of other social and physical networks is needed to address the emerging challenges in the field. The connection to systemic threats, such as disease pandemics, is specifically discussed.

Policy questions are often framed in popular discussion as situations where pulling the right levers will get the economy and society back on track after shocks and crises. This approach ignores how systems interact and how their systemic properties shape socioeconomic outcomes, leading to an over-emphasis on a limited set of characteristics, notably efficiency. We argue that this emphasis on efficiency in the operation, management and outcomes of various economic and social systems is not a conscious collective choice, but rather the response of the whole system to the incentives that individual components face. This has brought much of the world to rely upon complex, nested, and interconnected systems to deliver goods and services around the globe. While this approach has many benefits, the Covid-19 crisis shows how it has also reduced the resilience of key systems to shocks, and allowed failures to cascade from one system to others. This paper reviews the impact of COVID-19 on socioeconomic systems, discusses the notion of resilience, and provides specific recommendations on both integrating resilience analytics for recovery from the current crisis as well as on building resilient infrastructure to address future systemic challenges.

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.

Assessing and managing the impact of large-scale epidemics considering only the individual risk and severity of the disease is exceedingly difficult and could be extremely expensive. Economic consequences, infrastructure and service disruption, as well as the recovery speed, are just a few of the many dimensions along which to quantify the effect of an epidemic on society’s fabric. Here, we extend the concept of resilience to characterize epidemics in structured populations, by defining the system-wide critical functionality that combines an individual’s risk of getting the disease (disease attack rate) and the disruption to the system’s functionality (human mobility deterioration). By studying both conceptual and data-driven models, we show that the integrated consideration of individual risks and societal disruptions under resilience assessment framework provides an insightful picture of how an epidemic might impact society. In particular, containment interventions intended for a straightforward reduction of the risk may have net negative impact on the system by slowing down the recovery of basic societal functions. The presented study operationalizes the resilience framework, providing a more nuanced and comprehensive approach for optimizing containment schemes and mitigation policies in the case of epidemic outbreaks.

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.

Climate change may constrain future electricity supply adequacy by reducing electric transmission capacity and increasing electricity demand. The carrying capacity of electric power cables decreases as ambient air temperatures rise; similarly, during the summer peak period, electricity loads typically increase with hotter air temperatures due to increased air conditioning usage. As atmospheric carbon concentrations increase, higher ambient air temperatures may strain power infrastructure by simultaneously reducing transmission capacity and increasing peak electricity load. We estimate the impacts of rising ambient air temperatures on electric transmission ampacity and peak per-capita electricity load for 121 planning areas in the United States using downscaled global climate model projections. Together, these planning areas account for roughly 80% of current peak summertime load. We estimate climate-attributable capacity reductions to transmission lines by constructing thermal models of representative conductors, then forcing these models with future temperature projections to determine the percent change in rated ampacity. Next, we assess the impact of climate change on electricity load by using historical relationships between ambient temperature and utility-scale summertime peak load to estimate the extent to which climate change will incur additional peak load increases. We find that by mid-century (2040-2060), increases in ambient air temperature may reduce average summertime transmission capacity by 1.9%-5.8% relative to the 1990-2010 reference period. At the same time, peak per-capita summertime loads may rise by 4.2%-15% on average due to increases in ambient air temperature. In the absence of energy efficiency gains, demand-side management programs and transmission infrastructure upgrades, these load increases have the potential to upset current assumptions about future electricity supply adequacy.

Digitalization is changing society by the increased connectivity and networking that digital technologies enable, such as enhancing communication, services, and trade. Increasingly, policymakers within various national governments and international organizations such as the United Nations (UN) and Organization for Economic Co-operation and Development (OECD) are examining the original sustainability policy concepts applied within the Brundtland Report of 1987 through the lens of digitalization. While the growth of a digital economy may increase productivity and benefit local and global economies, digitalization also raises potential sustainability challenges pertaining to social (i.e., the benefits or costs imposed by disruptive digital technologies upon social networks and ways of life, including threats to economic sustainability and the rise of economic disparity) and environmental wellbeing (i.e., natural resource stewardship and concern for future generations) driven by the automation of information processing and delivery of services. Various perspectives have been raised regarding how the process of digitalization might be governed, and national governments remain at odds regarding a single best strategy to promote sustainable digitalization using the Brundtland concept to meet the development needs of the present without compromising the needs of future generations (i.e., social and environmental well-being). This paper reviews three governance strategies that countries can use in conjunction with adaptive governance to respond to digitalization sustainability threats: (i) a laissez-faire, industry-driven approach; (ii) a precautionary and preemptive strategy on the part of government; and (iii) a stewardship and “active surveillance” approach by government agencies that reduce the risks derived from digitalization while promoting private sector innovation. Regardless of a state’s digital governance response and how it is shaped by political and institutional realities, adaptive governance approaches are likely necessary to address the economic and social sustainability challenges posed within differing manifestations of digitalization.

Building resilience into today’s complex infrastructures is critical to the daily functioning of society and its ability to withstand and recover from natural disasters, epidemics and cyber-threats. This study proposes quantitative measures that capture and implement the definition of engineering resilience advanced by the National Academy of Sciences. The approach is applicable across physical, information and social domains. It evaluates the critical functionality, defined as a performance function of time set by the stakeholders. Critical functionality is a source of valuable information, such as the integrated system resilience over a time interval and its robustness. The paper demonstrates the formulation on two classes of models: 1) multi-level directed acyclic graphs and 2) interdependent coupled networks. For both models synthetic case studies are used to explore trends. For the first class, the approach is also applied to the Linux operating system. Results indicate that desired resilience and robustness levels are achievable by trading off different design parameters, such as redundancy, node recovery time and backup supply available. The nonlinear relationship between network parameters and resilience levels confirms the utility of the proposed approach, which is of benefit to analysts and designers of complex systems and networks.

Objective Multimorbidity is recognized as a serious health condition faced by a majority of older adults. Research investigating adaptive responses to multimorbidity, termed multimorbidity resilience, has been growing. This paper examines protective and risk factors, with a focus on health behaviours, socio-economic resources, and social support using an established measure of resilience (Connor-Davidson Resilience Scale) among older adults, focusing on older persons with two or more concurrent chronic conditions. Methods Using Baseline (2011–2015), Follow-up One (2015–2018), and Follow-up Two (2018–2021) data from the Comprehensive Cohort of the Canadian Longitudinal Study on Aging, we tested hypotheses using 13,064 participants aged 65 years and older, who completed all waves and reported two or more of 27 chronic conditions, for the full sample of multimorbid individuals and three multimorbidity clusters: Cardiovascular/Metabolic, Musculoskeletal, and Mental Health. Associations between protective and risk factors and resilience were examined using linear regression to model the Connor-Davidson resilience scale, adjusting for illness context and social determinants of health. Results Among all multimorbid individuals, the strongest associations with resilience were found for higher self-rated health, greater sleep satisfaction, better appetite, higher household income, more relatives and friends, being overweight (compared to normal weight), fewer housing problems, and fewer skipped meals. Weaker associations were found for non-smokers, less alcohol consumption, less pain, sedentary behaviour, being non-married (compared to married), and among Canadian born (compared to foreign). The analyses for the three multimorbidity clusters were largely replicated for the three multimorbidity clusters, but with some nuances depending on the cluster. Discussion This research provides confirmatory evidence for several protective and risk factors affecting the ability to cope and recover from multimorbidity adversity among older adults. There are consistent patterns for the multimorbidity disease clusters, but some distinct relationships arise that are worthy of attention. The implications of the findings for modifiable health behaviours and socio-economic factors are discussed for their public health and clinical relevance.

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.