This paper introduces a computation-free method for evaluating and prioritizing simulation-based stress tests for resilience assessment of transport systems. It enables infrastructure managers to efficiently screen and rank stress tests, optimizing the selection process to maximize insights into system resilience while minimizing computational demands. Stress tests have been proven to be a practical tool for understanding and mitigating the impact of disruptive events, yet conducting all possible tests using simulations, particularly for complex systems including plausible scenarios to account for climate change and other stressors, is computationally impractical, thus discouraging their use in practice. To address this, the paper suggests a methodology to estimate the impact of stress tests on risks at no computation cost and rank them accordingly to be selected for more detailed assessment. It uses the results of an initial risk assessment and, through a novel implementation of importance sampling and Bootstrapping resampling, selects subsets of the initial results to mimic specific stress test conditions, estimating their impact on risks. The methodology was validated through application to a Swiss road network facing flooding, demonstrating its practical effectiveness in identifying stress tests with significant potential impact on risks, hence having higher priority for more detailed assessment. In the presented case study, the proposed method enabled instant screening of 80 stress test scenarios, saving approximately 56 weeks of computation.

The 2030 Sustainable Development Goals (SDGs) offer a blueprint for global peace and prosperity, while conserving natural ecosystems and resources for the planet. However, factors such as climate-induced weather extremes and other High-Impact Low-Probability (HILP) events on their own can devastate lives and livelihoods. When a pandemic affects us, as COVID-19 has, any concurrent hazards interacting with it highlight additional challenges to disaster and emergency management worldwide. Such amplified effects contribute to greater societal and environmental risks, with cross-cutting impacts and exposing inequities. Hence, understanding how a pandemic affects the management of concurrent hazards and HILP is vital in disaster risk reduction practice. This study reviews the contemporary literature and utilizes data from the Emergency Events Database (EM-DAT) to unpack how multiple extreme events have interacted with the coronavirus pandemic and affected the progress in achieving the SDGs. This study is especially urgent, given the multidimensional societal impacts of the COVID-19 pandemic amidst climate change. Results indicate that mainstreaming risk management into development planning can mitigate the adverse effects of disasters. Successes in addressing compound risks have helped us understand the value of new technologies, such as the use of drones and robots to limit human exposure. Enhancing data collection efforts to enable inclusive sentinel systems can improve surveillance and effective response to future risk challenges. Stay-at-home policies put in place during the pandemic for virus containment have highlighted the need to holistically consider the built environment and socio-economic exigencies when addressing the pandemic’s physical and mental health impacts, and could also aid in the context of increasing climate-induced extreme events. As we have seen, such policies, services, and technologies, along with good nutrition, can significantly help safeguard health and well-being in pandemic times, especially when simultaneously faced with ubiquitous climate-induced extreme events. In the final decade of SDG actions, these measures may help in efforts to “Leave No One Behind”, enhance human–environment relations, and propel society to embrace sustainable policies and lifestyles that facilitate building back better in a post-pandemic world. Concerted actions that directly target the compounding effects of different interacting hazards should be a critical priority of the Sendai Framework by 2030.