Regional managers require adaptive strategies to enhance resilience against severe weather events and disasters. Critical infrastructure sectors are interconnected such that disruptions in one sector to cascade through others, exposing system-wide vulnerabilities. This paper presents a scenario-based framework that integrates network theory and scenario analysis to assess resilience within regional infrastructure networks of a metropolitan region. The framework quantifies disruptions in system orders by evaluating how critical infrastructure sector priorities change across scenarios. Scenarios are various timeframes following or preceding a disruptive event, from a few hours to a few months. Inserting features of a large flood in Nashville, TN, USA, as a case study, the analysis examines how disruptions alter the order of sectors and interdependencies, identifying which sectors are most vulnerable to cascading failures, as well as those with greater stability. Results indicate that sectors such as healthcare, communications, and energy remain consistently critical to resilience of the cyber-physical system, while transportation and water services show higher sensitivity to disruption. By assessing the disruptiveness of each scenario, this framework provides a greater understanding of system dynamics and supports strategic resilience planning by prioritizing sectors critical to regional stability.

Around 1200 BCE, the societies of the Late Bronze Age (LBA) in the Eastern Mediterranean experienced a collective collapse, evident in the archeological remains of destroyed and abandoned cities. Following our prior explorations in this topic, we hypothesize that the network structure between the LBA societies amplified compounding threats, producing a cascade of failures that culminated in a precipitous broad systemic collapse. The network, so often seen as a conduit for prosperity, propagated the problems of individual nodes. Herein we discuss the findings of Linkov et al.’s (2024) network analysis of the LBA collapse and its implications regarding vulnerabilities in our current global context as our systems surpass carrying capacity in our pursuit of societal complexity.

Disruption of complex infrastructures systems involves cascading failures and interdependencies. This paper presents a network-based approach to assessing infrastructure resilience using scenario-based disruptions that remove entire sectors from the network. This approach evaluates system-wide vulnerabilities by modeling structural failures through the removal of nodes from the infrastructure graph. The framework uses a directed graph to represent interdependencies and uses eigenvector centrality to rank sector influence. Disruptive scenarios, including power outages, communication failures, and hybrid threats are applied to evaluate changes in system order. Spearman's rank correlation quantifies the disruptiveness of each scenario, identifying which sectors experience the most significant shifts in importance. Results show that disruptions to the communications sector cause the greatest reordering of system orders, while disruptions to water & wastewater have a lower impact. The analysis demonstrates how different hazards affect regional resilience and provides insights for decision-makers to schedule the risk countermeasures.

Military logistics rely heavily on public infrastructure, such as highways and railways, to transport troops, equipment, and supplies, linking critical installations through the Department of Defense's Strategic Highway Network and Strategic Rail Corridor Network. However, these networks are vulnerable to disruptions that can jeopardize operational readiness, particularly in contested environments where adversaries employ non-traditional threats to disrupt logistics, even within the homeland. This paper presents a contested logistics model that utilizes network science and Geographic Information System (GIS) to evaluate the robustness and resilience of strategic transportation networks under various disruption scenarios. By integrating GIS data to model logistics networks, simulating disruptions, and quantifying their impacts, we identified vulnerabilities in US power projection routes and assessed the resilience and robustness of highways and railways. Our findings reveal that highways are more resilient than railways, with greater capacity to absorb targeted disruptions. These findings underscore the importance of prioritizing investments in highway infrastructure and reinforcing vulnerable road and rail segments, particularly in high-risk regions, to enhance the resilience of military logistics and maintain operational effectiveness in contested conditions.

We consider random hyperbolic graphs in hyperbolic spaces of any dimension d+1≥2. We present a rescaling of model parameters that casts the random hyperbolic graph model of any dimension to a unified mathematical framework, leaving the degree distribution invariant with respect to the dimension. Unlike the degree distribution, clustering does depend on the dimension, decreasing to 0 at d→∞. We analyze all of the other limiting regimes of the model, and we release a software package that generates random hyperbolic graphs and their limits in hyperbolic spaces of any dimension.

As the world faces multiple crises, lessons from humanity's past can potentially suggest ways to decrease disruptions and increase societal resilience. From 1200 to 1100 BCE, several advanced societies in the Eastern Mediterranean suffered dramatic collapse. Though the causes of the Late Bronze Age Collapse are still debated, contributing factors may include a “perfect storm” of multiple stressors: social and economic upheaval, earthquake clusters, climate change, and others. We examined how collapse might have propagated through the societies’ connections by modeling the Eastern Mediterranean Late Bronze Age trade and socio-political networks. Our model shows that the Late Bronze Age societies made a robust network, where any single node's collapse was insufficient to catalyze the regional collapse that historically transpired. However, modeled scenarios indicate that some paired node disruptions could cause cascading failure within the network. Subsequently, a holistic understanding of the region's network incentive structures and feedback loops can help societies anticipate compounding risk conditions that might lead to widespread collapse and allow them to take appropriate actions to mitigate or adapt societal dependencies. Such network analyses may be able to provide insight as to how we can prevent a collapse of socio-political, economic and trade networks similar to what occurred at the end of the Late Bronze Age. Though such data-intensive analytics were unavailable to these Bronze Age regions, modern society may be able to leverage historical lessons in order to foster improved robustness and resilience to compounding threats. Our work shows that civilization collapses are preventable; we are not necessarily destined to collapse.

Emergency services play a crucial role in safeguarding human life and property within society. In this paper, we propose a network-based methodology for calculating transportation access between emergency services and the broader community. Using New York City as a case study, this study identifies ‘emergency service deserts’ based on the National Fire Protection Association (NFPA) guidelines, where accessibility to Fire, Emergency Medical Services, Police, and Hospitals are compromised. The results show that while 95% of NYC residents are well-served by emergency services, the residents of Staten Island are disproportionately underserved. By quantifying the relationship between first responder travel time, Emergency Services Sector (ESS) site density, and population density, we discovered a negative power law relationship between travel time and ESS site density. This relationship can be used directly by policymakers to determine which parts of a community would benefit the most from providing new ESS locations. Furthermore, this methodology can be used to quantify the resilience of emergency service infrastructure by observing changes in accessibility in communities facing threats.

Dynamic processes on networks, be it information transfer in the Internet, contagious spreading in a social network, or neural signaling, take place along shortest or nearly shortest paths. Computing shortest paths is a straightforward task when the network of interest is fully known, and there are a plethora of computational algorithms for this purpose. Unfortunately, our maps of most large networks are substantially incomplete due to either the highly dynamic nature of networks, or high cost of network measurements, or both, rendering traditional path finding methods inefficient. We find that shortest paths in large real networks, such as the network of protein-protein interactions and the Internet at the autonomous system level, are not random but are organized according to latent-geometric rules. If nodes of these networks are mapped to points in latent hyperbolic spaces, shortest paths in them align along geodesic curves connecting endpoint nodes. We find that this alignment is sufficiently strong to allow for the identification of shortest path nodes even in the case of substantially incomplete networks, where numbers of missing links exceed those of observable links. We demonstrate the utility of latent-geometric path finding in problems of cellular pathway reconstruction and communication security.

Researchers from various scientific disciplines have attempted to forecast the spread of coronavirus disease 2019 (COVID-19). The proposed epidemic prediction methods range from basic curve fitting methods and traffic interaction models to machine-learning approaches. If we combine all these approaches, we obtain the Network Inference-based Prediction Algorithm (NIPA). In this paper, we analyse a diverse set of COVID-19 forecast algorithms, including several modifications of NIPA. Among the algorithms that we evaluated, the original NIPA performed best at forecasting the spread of COVID-19 in Hubei, China and in the Netherlands. In particular, we show that network-based forecasting is superior to any other forecasting algorithm.

Despite many studies on the transmission mechanism of the Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), it remains still challenging to efficiently reduce mortality. In this work, we apply a two-population Susceptible-Infected-Removed (SIR) model to investigate the COVID-19 spreading when contacts between elderly and non-elderly individuals are reduced due to the high mortality risk of elderly people. We discover that the reduction of connections between two populations can delay the death curve but cannot reduce the final mortality. We propose a merged SIR model, which advises elderly individuals to interact less with their non-elderly connections at the initial stage but interact more with their non-elderly relationships later, to reduce mortality. Finally, immunizing elderly hub individuals can also significantly decrease mortality.

Background: The COVID-19 pandemic has a significant impact on economy. Decisions regarding the reopening of businesses should account for infection risks. Objective: This paper describes a novel model for COVID-19 infection risks and policy evaluations. Methods: The model combines the best principles of the agent-based, microexposure, and probabilistic modeling approaches. It takes into account specifics of a workplace, mask efficiency, and daily routines of employees, but does not require specific inter-agent rules for simulations. Likewise, it does not require knowledge of microscopic disease related parameters. Instead, the risk of infection is aggregated into the probability of infection, which depends on the duration and distance of every contact. The probability of infection at the end of a workday is found using rigorous probabilistic rules. Unlike previous models, this approach requires only a few reference data points for calibration, which are more easily collected via empirical studies. Results: The application of the model is demonstrated for a typical office environment and for a real-world case. Conclusion: The proposed model allows for effective risk assessment and policy evaluation when there are large uncertainties about the disease, making it particularly suitable for COVID-19 risk assessments.

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.

Disruptions to transportation networks are inevitable. When road networks are not resilient, or in other words, do not recover rapidly from disruptions, unpredictable events can cause significant delays that may be disproportionately greater than the extent of the disruption. Enhancing transportation system resilience can help mitigate the consequences of disruptions; however, required investments are difficult to justify given the low probability of such events. This paper calculates economic implications of unmitigated random disruptions in urban road systems. We modeled delays in transportation networks and demonstrated how resilience can be integrated into macroeconomic modeling via the transportation planning model, REMI TranSight. The model was applied to 10 cities in the United States to forecast the impact of disruptions on gross domestic product (GDP). Different disruption scenarios were modeled and the magnitude of disruption was used to calculate additional delays in transportation networks, which were then integrated into the TranSight model. The results were compared to a baseline case, where economic impact was assumed to be proportional to the magnitude of disruptions. Results show that losses in GDP were far more pronounced in the case scenario as compared to the baseline. The losses tended to be higher in wealthier and more economically productive cities. The economic output tends to rebound one to two years after a disruptive event. We conclude that different topology in transportation networks in different cities requires explicit consideration and quantification of resilience to support investment decisions designed to improve transportation networks in cities.

Many cities are adopting increasingly advanced Intelligent Transportation Systems (ITS). These systems combine connectivity, coordination, adaptivity, and automated response for transportation policy optimization, thus increasing “smartness” and efficiency. However, the control and sensing systems of implemented ITS can open new vulnerabilities, especially to cyber-attacks. Currently vulnerability is managed within the framework of traditional risk assessment that assesses potential failures of the system in response to specified threats. Emerging technologies by their nature have threats that are not fully known, therefore, resilience, defined as the system's ability to recover and adapt to both known and unknown threats, is an emerging area that holds promise for assessing threats to ITS. To illustrate the applicability of resilience to ITS, we conducted a study of network efficiency and resilience in response to random and targeted disruptions of ITS systems in 10 urban areas. Disruptions were generated to affect either intersections or roadways controlled by ITS under different threat scenarios. Modeled attacks, under worst case scenarios, disrupted 20% of intersections causing on average 14.6% more additional delays than the same severity attacks on roadways. Additionally, locking traffic signal states was found to cause more disruption than fully disabling signals. Thus, as cities adopt ITS and other smart systems resulting in potentially unknown vulnerabilities, it is important to consider resilience of transportation infrastructure affected by potential cyber-attacks.