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.@en

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.@en

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.@en