Aims The influence of the COVID-19 pandemic on attendance to out-of-hospital cardiac arrest (OHCA) has only been described in city or regional settings. The impact of COVID-19 across an entire country with a high infection rate is yet to be explored. Methods The study uses data from 8629 cases recorded in two time-series (2017/2018 and 2020) of the Spanish national registry. Data from a non-COVID-19 period and the COVID-19 period (February 1st–April 30th 2020) were compared. During the COVID-19 period, data a further analysis comparing non-pandemic and pandemic weeks (defined according to the WHO declaration on March 11th, 2020) was conducted. The chi-squared analysis examined differences in OHCA attendance and other patient and resuscitation characteristics. Multivariate logistic regression examined survival likelihood to hospital admission and discharge. The multilevel analysis examined the differential effects of regional COVID-19 incidence on these same outcomes. Results During the COVID-19 period, the incidence of resuscitation attempts declined and survival to hospital admission (OR = 1.72; 95%CI = 1.46–2.04; p < 0.001) and discharge (OR = 1.38; 95%CI = 1.07–1.78; p = 0.013) fell compared to the non-COVID period. This pattern was also observed when comparing non-pandemic weeks and pandemic weeks. COVID-19 incidence impinged significantly upon outcomes regardless of regional variation, with low, medium, and high incidence regions equally affected. Conclusions The pandemic, irrespective of its incidence, seems to have particularly impeded the pre-hospital phase of OHCA care. Present findings call for the need to adapt out-of-hospital care for periods of serious infection risk. Study registration number ISRCTN10437835.

Emergency evacuation of crowds is a fascinating phenomenon that has attracted researchers from various fields. Better understanding of this class of crowd behavior opens up for improving evacuation policies and smarter design of buildings, increasing safety. Recently, a new class of disruptive technology has appeared: Humancentered sensing which allows crowd behavior to be monitored in real-time, and provides the basis for realtime crowd control. The question then becomes: to what degree can previous crowd models incorporate this development, and what areas need further research? In this paper, we provide a survey that describes some widely used crowd models and discuss their advantages and shortages from the angle of human-centered sensing. Our review reveals important research opportunities that may contribute to an improved and more robust emergency management.

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Managing the uncertainties that arise in disasters-such as ship fire-can be extremely challenging. Previous work has typically focused either on modeling crowd behavior or hazard dynamics, targeting fully known environments. However, when a disaster strikes, uncertainty about the nature, extent and further development of the hazard is the rule rather than the exception. Additionally, crowd and hazard dynamics are both intertwined and uncertain, making evacuation planning extremely difficult. To address this challenge, we propose a novel spatio-temporal probabilistic model that integrates crowd with hazard dynamics, using a ship fire as a proof-of-concept scenario. The model is realized as a dynamic Bayesian network (DBN), supporting distinct kinds of crowd evacuation behavior-both descriptive and normative (optimal). Descriptive modeling is based on studies of physical fire models, crowd psychology models, and corresponding flow models, while we identify optimal behavior using Ant-Based Colony Optimization (ACO). Simulation results demonstrate that the DNB model allows us to track and forecast the movement of people until they escape, as the hazard develops from time step to time step. Furthermore, the ACO provides safe paths, dynamically responding to current threats.

Maturity models enhance the performance of companies by prescribing a trajectory through stages of increasing capability. However, a recent review of maturity models concludes that current maturity models hardly meet the design principles required for prescriptive use. To address this deficiency, we conducted semistructured interviews and a Group Model Building study with industrial companies in Spain in which we studied the progression toward a Leading Green Company as the highest maturity stage of environmental management. The findings from the study were tested using surveys with enterprises in Spain, Italy, and the United Kingdom, semistructured interviews in the United Kingdom and case studies in Spain. Using these data sources, we develop a causal model that captures an idealized environmental management maturity dynamic progression though stages. By mapping maturity stages to feedback loops connected to actions to improve those maturity levels, system dynamics can help companies articulate policies for transitioning toward higher maturity stages.