Humanity has always faced extreme events that pose significant challenges due to their unpredictability and substantial impact. While, in the past, these events were relatively rare, recently, the frequency, intensity, and geographic spread of extreme events have increased dramatically, driven by factors like climate change and globalisation. The European heatwaves of 2022, 2023, and 2024, for instance, demonstrated how climate-driven extremes can severely impact modern societies, with tens of thousands of heat-related deaths. Likewise, the COVID-19 pandemic revealed the vulnerabilities in global preparedness for health crises, causing severe economic impacts and widespread social disruption. Both phenomena underscore the urgent need for enhanced resilience and preparedness in the face of increasingly frequent and intense extreme events....@en

Urban vulnerability is affected by changing patterns of hazards due to climate change, increasing inequalities, rapid urban growth and inadequate infrastructure. While we have a relatively good understanding of how urban vulnerability changes in space, we know relatively little about the temporal dynamics of urban vulnerability. This paper presents a framework to assess urban vulnerability over time and space to address this gap. We apply the framework to Amsterdam, Rotterdam, and The Hague, the Netherlands. Using high-resolution, anonymised ambulance calls and socio-economic, built environment, and proximity data, we identify three temporal patterns: ’Midday Peaks’, ’Early Birds’, and ’All-Day All-Night’. Each pattern represents a unique rhythm of risk arising from the interaction of people with diverse demographic and socio-economic backgrounds and the temporal flow of their daily activities within various urban environments. Our findings also highlight the polycentric nature of modern Dutch cities, where similar rhythms emerge in areas with varying population densities. Through these case studies, we demonstrate that our framework uncovers the spatio-temporal dynamics of urban vulnerability. These insights suggest that a more nuanced approach is necessary for assessing urban vulnerability and enhancing preparedness efforts.

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Involuntary displacement from conflict and other causes leads to clustering of refugees and internally displaced people, often in long-term settlements. Within refugee-hosting countries, refugee settlements are frequently located in isolated and remote areas, characterized by poor-quality land and harsh climatic conditions. Yet, the exposure of refugee settlements to climatic events is underresearched. In this article, we study the exposure of the 20 largest refugee settlements worldwide to extreme variations in climatic conditions. The analysis integrates exposure of camp locations compared to the national trends for both slow- and rapid-onset events and includes descriptive statistics, signal-to-noise analyses, and trend analyses. Our findings show that most refugee settlements included face relatively high exposure to slow-onset events, including high temperatures (for settlements in Kenya, Ethiopia, Rwanda, Sudan, and Uganda), low temperatures (in the case of Jordan and Pakistan), and low levels of rainfall (in Ethiopia, Rwanda, Kenya, and Uganda) compared to national averages. Our findings for rapid-onset events-heatwaves, coldwaves, and extreme rainfall-are less conclusive compared to country trends, although we find relatively high exposure to extreme rainfall in Cox's Bazar, Bangladesh. Our analyses confirm that refugee populations are exposed to extreme weather conditions postdisplacement, which, in combination with their sociopolitical exclusion, poses a threat to well-being and increased marginalization. Our findings call for an inclusive and integrated approach, including refugees and their host communities, in designing climate adaptation and sustainable development policies, in order to promote equitable sustainable development pathways in refugee-hosting countries.

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Purpose: The coronavirus disease (COVID-19) pandemic has emerged as an unprecedented health crisis worldwide and heavily disrupted the healthcare supply chain. This study focuses on analysing the different types of disruptions occurring in personal protective equipment (PPE) supply chains during the COVID-19 pandemic and on proposing mitigation strategies that are fit to the global scale and many interdependencies that are characteristic for this pandemic. The authors construct a conceptual system dynamics model (SD) based on the literature and adjusted with the use of empirical data (interviews) to capture the complexity of a global supply chain and identify leverage points (mitigation strategies). Design/methodology/approach: This research follows a mix-methods approach. First, the authors developed a conceptual framework based on four types of disruptions that usually occur during health emergencies (direct effect, policy, supply chain strategy, and behaviourally induced disruptions). Second, the authors collected and analysed data from interviews with experts in the PPE supply chain. Based on the interviews data, the authors developed a conceptual system dynamics (SD) model that allows to capture the complex and dynamic interplay between the elements of the global supply chain system, by highlighting key feedback loops, delays, and the way the mitigation strategies can impact on them. From this analysis, the authors developed four propositions for supply chain risk management (SCRM) in global health emergencies and four recommendations for the policy and decision makers. Findings: The SD model highlights that without a combination of mitigation measures, it is impossible to overcome all disruptions. As such, a co-ordinated effort across the different countries and sectors that experience the disruptions is needed. The SD model also shows that there are important feedback loops, by which initial disruptions create delays and shortages that propagate through the supply chain network. If the co-ordinated mitigation measures are not implemented early at the onset of the pandemic, these disruptions will be persistent, creating potential shortages of PPE and other critical equipment at the onset of a pandemic – when they are most urgently needed. Originality/value: This research enriches the understanding of the disruptions of PPE supply chains on the systems level and proposes mitigation strategies based on empirical data and the existing literature.@en

The paper presents a web-based system designed to visualize a simulation of ca. 500,000 residents of a city during the COVID-19 outbreak. Each agent's infection status and mobility are presented in the time scale. The main effort was to ensure simplicity from the user's perspective and smooth operation on various client devices. The full range of software engineering problems is discussed, from efficient data storage to optimization of visualization methods. @en

Public transit networks in cities are crucial in addressing the transforming mobility needs of citizens for work, services and leisure. The rapid changes in urban demographics pose several challenges for the efficient management of transit services. To forecast transit demand, planners often resort to sociological investigations, modelling or population data that are either difficult to obtain, inaccurate or outdated. How can we then estimate the variable demand for mobility? We propose a simple method to identify the spatiotemporal demand for public transit in a city. Using a Gaussian mixture model, we decompose empirical ridership data into a set of temporal demand profiles representative of ridership over any given day. A case of ≈4.6 million daily transit traces of the primary mode of underground services from the Greater London region reveals distinct commuting profiles. We find that a weighted mixture of these profiles can generate any station traffic remarkably well, uncovering spatially concentric clusters of mobility needs. Our results also suggest that heavily used stations that exhibit mixed-use commuting patterns are generally located in the cluster of the central business district and stations away from the centre of the city are largely single use residential areas. Overall, identifying mixed temporal and spatial use of stations diverging from macro mobility patterns in public transit indicates that our approach may be useful in a detailed understanding of integrated transit planning for heterogeneous needs of travellers.

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