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During catastrophic events like natural disasters, pandemics, large-scale industrial accidents, or wars, hospitals must continue providing uninterrupted healthcare services despite significant challenges. However, they might also become victims of the disaster and face the necessity of evacuation. Existing hospital evacuation models, which primarily depend on essential data being available before evacuation, often fail to account for the dynamic nature of emergencies and oversimplify the complexities of real-world situations. This paper marks a paradigm shift towards a real-time, data-driven decision-support model for managing hospital evacuations during acute emergencies. The proposed model integrates data on factors such as the severity of the situation, resource status, patient needs, and road conditions. It employs a Bayesian ARIMA component to predict patient arrivals, specially tailored for limited sample sizes. A case study of a hypothetical flood emergency in the Hawkesbury-Nepean Rivers region in Western Sydney, Australia, demonstrates the advantages of a proposed framework equipped with predictive analytics compared to a purely optimization-based model. Numerical testing reveals that without a forward-looking component to predict patient transfer demand over future periods, there can be a misallocation of resources in the initial stages, leading to shortages of critical resources later in the emergency operation. The proposed dynamic decision support framework underlines the potential value of predictive analytics for anticipating future trends in disaster management and response. The findings offer potential advancements in understanding how data and technology can be harnessed to improve emergency responses, promoting more resilient healthcare systems.

In disaster response, the overwhelming amount of time-sensitive information and response options, combined with the dynamic nature of disasters, makes decision-making challenging for emergency service providers. Furthermore, it is often not economically feasible for countries to maintain a large number of full-time emergency responders. As such, many countries rely heavily on volunteer emergency responders during major disasters. This means that the success of disaster response often hinges on the efficient use of this volunteer workforce. We propose a framework for a Decision Support System (DSS) designed to optimize the use of volunteers by emergency services. This framework includes the data management layer, integrating necessary inputs and information; the analytical layer, which serves as the system's processing core; the user interface layer; and the decision-making layer. We argue that, while significant academic focus has been on the analytical layer, practical implementation requires the integration of all four components. Additionally, we emphasize the need for coordination with a broad spectrum of stakeholders involved in data provision, decision-making, and resource deployment for operationalizing this DSS. We also explore and analyze existing methodologies for developing the analytical layers, the requirements of these models, and the current methodological gaps. The proposed framework establishes a clear roadmap for adopting emergency response approaches that are human-centric, but at the same time, effectively utilize advancements in modeling, optimization, machine learning, and data integration.

This study proposes a novel approach to enhancing urban resilience and emergency management practices in the face of heatwave events, often exacerbated by the urban heat island effect. Recognizing the limitations of traditional government-led responses in shelter allocation—characterized by high costs, logistical challenges, and a lack of adaptability—this paper introduces an innovative framework that integrates community-based volunteer efforts with advanced simulation and stochastic modelling techniques. The primary objective is to optimize the allocation of resources and volunteers to improve the efficiency and cost-effectiveness of heatwave response mechanisms. Utilizing Data Envelopment Analysis (DEA), the research identifies and enhances resource and volunteer distribution strategies, paving the way for more effective emergency services. A case study conducted in Blacktown City, Western Sydney, serves to demonstrate the practical application and implications of these strategies, underscoring the necessity for a paradigm shift towards more adaptable, volunteer-focused, and resource-efficient approaches in mitigating the impact of climate-change-induced heat waves on vulnerable urban populations. The anticipated outcomes include measurable improvements in response times, cost reductions, and enhanced volunteer mobilization efficiency.

Abstract Nature-based Solutions are recognised for their potential to address the biodiversity and climate crises, and less extensively, other societal challenges. However, this nature-society relationship is becoming more important as available food and water resources, income, and human health, are increasingly impacted by environmental changes. Here, we utilise the seven major societal challenges addressed by Nature-based Solutions according to the International Union for Conservation of Nature, to identify the primary themes of the Nature-based Solutions research landscape from 1990-2021. We evaluate how these themes, with respect to the societal challenges, evolved over time, and where. Our findings highlight the under-representation of four societal challenges across the research landscape: economic and social development, human health, food security, and water security. We propose six research pathways to advance the evidence for Nature-based Solutions in these societal challenges, and present opportunities for future research programs to prioritise the needs of society, the environment, and the economy.

Abstract Introduction Health economics is a thriving sub-discipline of economics. Applied health economics research is considered essential in the health care sector and is used extensively by public policy makers. For scholars, it is important to understand the history and status of health economics—when it emerged, the rate of research output, trending topics, and its temporal evolution—to ensure clarity and direction when formulating research questions. Methods Nearly 13,000 articles were analysed, which were found in the collective publications of the ten most specialised health economic journals. We explored this literature using patterns of term co-occurrence and document co-citation. Results The research output in this field is growing exponentially. Five main research divisions were identified: (i) macroeconomic evaluation, (ii) microeconomic evaluation, (iii) measurement and valuation of outcomes, (iv) monitoring mechanisms (evaluation), and (v) guidance and appraisal. Document co-citation analysis revealed eighteen major research streams and identified variation in the magnitude of activities in each of the streams. A recent emergence of research activities in health economics was seen in the Medicaid Expansion stream. Established research streams that continue to show high levels of activity include Child Health, Health-related Quality of Life (HRQoL) and Cost-effectiveness. Conversely, Patient Preference, Health Care Expenditure and Economic Evaluation are now past their peak of activity in specialised health economic journals. Analysis also identified several streams that emerged in the past but are no longer active. Conclusions Health economics is a growing field, yet there is minimal evidence of creation of new research trends. Over the past 10 years, the average rate of annual increase in internationally collaborated publications is almost double that of domestic collaborations (8.4% vs 4.9%), but most of the top scholarly collaborations remain between six countries only.

Abstract As sea-level rise (SLR) accelerates due to climate change, its multidisciplinary field of science has similarly expanded, from 41 articles published in 1990 to 1475 articles published in 2021, and nearly 15,000 articles published in the Web of Science over this 32-year period. Here, big-data bibliometric techniques are adopted to systematically analyse this large literature set. Four main research clusters (themes) emerge: (I) geological dimensions and sea-level indicators, (II) impacts, risks, and adaptation, (III) physical components of sea-level change, and (IV) coastal ecosystems and habitats, with 16 associated sub-themes. This analysis provides insights into the evolution of research agendas, the challenges and opportunities for future assessments (e.g. next IPCC reports), and growing focus on adaptation. For example, the relative importance of sub-themes evolves consistently with a relative decline in pure science analysis towards solution-focused topics associated with SLR risks such as high-end rises, declining ecosystem services, flood hazards, and coastal erosion/squeeze.

This paper presents a conceptual hybrid model that embodies a synergistic approach by integrating principles from operations research (OR) with steps from the design science research methodology (DSRM). The model is applied to develop an evacuation support system for a group of hospitals located in Western Sydney, Australia, exposed to the risk of floods induced by climate change. By combining these two approaches, the conceptual hybrid model effectively overcomes the limitations associated with traditional DSRM and provides a comprehensive research framework for addressing OR problems. The research findings hold significant implications for academia, professionals, and policy makers engaged in the field of disaster risk management. Moreover, this study offers valuable insights into enhancing the effectiveness and efficiency of OR-based solutions in practical scenarios, thus contributing to the advancement of knowledge in this area.

Abstract When implemented in practice, digital technologies have shown improvements in morbidity and mortality outcomes in patients with cardiovascular disease (CVD). For scholars, research into digital technologies in cardiovascular care has been relatively recent, thus it is important to understand the history of digital health technology in cardiovascular research—its emergence, rate of growth, hot topics, and its temporal evolution. The aim of this study was to analyse more than 16,000 articles in this domain based on their scientometric indicators. Web of Science (WoS) Core Collection was accessed and searched at several levels, including titles, abstracts, keywords, authors, sources and individual articles. Analysis examined the temporal shifts in research and scholarly focus based on keywords, networks of collaboration, topical divisions in relation to digital technologies, and influential publications. Findings showed this research area is growing exponentially. Co-citation analysis revealed twenty prominent research streams and identified variation in the magnitude of activities in each stream. A recent emergence of research activities in digital technology in cardiovascular rehabilitation (CR), out-of-hospital cardiac arrest (OHCA), and arrythmia research was also demonstrated. Conversely, wearable technologies, activity tracking and electronic medical records research are now past their peak of reported research activity. With increasing amounts of novel technologies becoming available and more patients taking part in remote health care monitoring, further evaluation and research into digital technologies, including their long-term effectiveness, is needed. Furthermore, emerging technologies, which are evaluated and/or validated should be considered for implementation into clinical practice as treatment and prevention modalities for CVD.

In light of the proliferation of scientometric/bibliometric software and its accessibility to a wide array of researchers, we have witnessed a substantial increase in literature analyses founded in these methods across diverse scientific domains. With these methods gaining popularity, the influx of similar papers seeking publication is expected to continue in the forthcoming years. However, persistent questions remain regarding the benchmarks for a well-justified, informative, and publication-worthy scientometric review paper. This concern resonates not only with authors but also holds significance for editors and reviewers tasked with evaluating the quality, rigour, and relevance of such contributions. In this context, this paper furnishes a concise guide elucidating the fundamental criteria underpinning a publication-worthy scientometric literature analysis. Encompassing the entire spectrum of steps integral to a scientometric review, the guide addresses the rationale and benefits of undertaking such an analysis. This includes all relevant aspects such as scoping, data procurement and search methodologies, visualisation, result delineation and interpretation. Furthermore, this guide addresses a range of prevalent misconceptions and common errors with respect to this genre of scholarly publications. It concludes by proposing a quality checklist that can be used as a standard benchmark for assessing studies that employ methodologies of scientometric literature analysis.

The recent outbreaks of diseases have emphasized the importance of having robust decision-making models in place to aid in the evacuation of patients, particularly from overwhelmed hospitals to alternative facilities. The sudden surge of patients during an outbreak can quickly deplete a hospital's resources, leading to compromised healthcare delivery and an increase in mortality rates. Evacuating patients to other hospitals can alleviate the burden on a single facility, but numerous factors need to be considered when making evacuation decisions, including patient acuity, transportation logistics, and bed availability at other facilities. Despite the critical role of patient evacuation in disease outbreaks, there has been a lack of systematic and comprehensive research on developing decision-making models that identify the critical factors. This paper aims to bridge this gap by providing the key factors that should be considered when creating decision models for patient evacuation in response to contagious disease outbreaks. The findings reveal that decision-making processes are influenced by a multitude of factors, including patient numbers and classifications, staff scheduling and assignments, resource availability for transportation, road traffic conditions, capacities and numbers of evacuation and receiving hospitals, as well as the behaviour of disease outbreaks. These findings provide practical insights for relevant authorities to optimize emergency strategies during disease outbreaks and enhance the overall resilience of the healthcare system against external disruptions.

Pandemics and sudden disease outbreaks place considerable stress on hospital resources. Their increasing numbers in recent years has necessitated investment in disaster risk management strategies, particularly in the healthcare sector. The sudden surge of patients, particularly in requesting ambulance services, overwhelms hospital systems and compromises health service delivery. Failure of health planners to respond immediately to a sudden disease outbreak can result in insufficient distribution of healthcare services and can thereby exacerbate the death toll dramatically. The current research aims to develop an optimisation-based integrated decision model to assist healthcare decision-makers with immediate and effective planning for ambulances to move critical patients from their residences to hospitals, considering the available capacities of each hospital. Several lemmas for the problem are proposed, and based on these; several local search methods are developed to improve the performance of the proposed optimisation method. To confirm the efficacy of the proposed approach, a comprehensive comparison is conducted. In conclusion, sensitivity analyses are performed to discuss some practical insights. The proposed models can be adopted to develop decision tools that enable hospital system managers to optimize their resources to changing healthcare needs in disease outbreaks.

Bibliographic properties of more than 75 million scholarly articles, are examined and trends in overall research productivity are analysed as a function of research field (over the period of 1970-2020) and author gender (over the period of 2006-2020). Potential disruptive effects of the Covid-19 pandemic are also investigated. Over the last decade (2010-2020), the annual number of publications have invariably increased every year with the largest relative increase in a single year happening in 2019 (more than 6% relative growth). But this momentum was interrupted in 2020. Trends show that Environmental Sciences and Engineering Environmental have been the fastest growing research fields. The disruption in patterns of scholarly publication due to the Covid-19 pandemic was unevenly distributed across fields, with Computer Science, Engineering and Social Science enduring the most notable declines. The overall trends of male and female productivity indicate that, in terms of absolute number of publications, the gender gap does not seem to be closing in any country. The trends in absolute gap between male and female authors is either parallel (e.g., Canada, Australia, England, USA) or widening (e.g., majority of countries, particularly Middle Eastern countries). In terms of the ratio of female to male productivity, however, the gap is narrowing almost invariably, though at markedly different rates across countries. While some countries are nearing a ratio of .7 and are well on track for a 0.9 female to male productivity ratio, our estimates show that certain countries (particularly across the Middle East) will not reach such targets within the next 100 years. Without interventional policies, a significant gap will continue to exist in such countries. The decrease or increase in research productivity during the first year of the pandemic, in contrast to trends established before 2020, was generally parallel for male and female authors. There has been no substantial gender difference in the disruption due to the pandemic. However, opposite trends were found in a few cases. It was observed that, in some countries (e.g., The Netherlands, The United States and Germany), male productivity has been more negatively affected by the pandemic. Overall, female research productivity seems to have been more resilient to the disruptive effect of Covid-19 pandemic, although the momentum of female researchers has been negatively affected in a comparable manner to that of males.

This study aims to make an objective comparative analysis between the relative significance of three crucial modelling aspects involved in the probabilistic analysis of transport networks. The first question to address is the extent to which the size of generated path sets can affect the prediction of the static flow in the path-based traffic assignment paradigm. The importance of this question arises from the fact that the need to generate a large quantity of paths may be perceived by analysts as a preventative reason as to the application of path-based stochastic traffic assignment (STA) models for large-scale networks. A simulated path generation algorithm, which allows the number of generated paths to be under modeller's control, is applied. Findings show that the size of the generated path sets does not substantially affect the flow prediction outcome in this case study. Further investigations with respect to the relative importance of STA model estimation (or equivalently, parameter calibration) and model specification (or equivalently, error term formulation) are also conducted. A paired combinatorial logit (PCL) assignment model with an origin–destination-specific-parameter, along with a heuristic method of model estimation (calibration), is proposed. The proposed model cannot only accommodate the correlation between path utilities, but also accounts for the fact that travelling between different origin–destination (O–D) pairs can correspond to different levels of stochasticity and choice randomness. Results suggest that the estimation of the stochastic user equilibrium (SUE) models can affect the outcome of the flow prediction far more meaningfully than the complexity of the choice model (i.e., model specification).

Background. Evacuation behaviour of human crowds is often characterised by the notion of ‘irrational behaviour’. While the term has been frequently used in the literature, clear definitions and methods for measuring rationality do not exist. Objective. Here, we suggest that rationality, in this context, can alternatively and more effectively be formulated as a question of ‘optimal behaviour’. Decision optimality can potentially be measured and quantified. The main challenges, however, include (i) distinctly identifying the level at which we measure optimality, and (ii) identifying proper reference points at each level. Methods. We differentiate between optimality at the individual (i.e., micro) and the system (i.e., macro/aggregate) levels and illustrate how certain reference points can be established at each level. We suggest that, at the micro level, optimality of individual decisions can be quantified by comparing the outcome of each individual’s decision to those of their ‘nearly equal peers’. At the macro level, optimality can be measured by simulating the system using parametric numerical models and measuring the system performance while altering the behavioural parameters compared to their empirical estimates. Results. Having applied these methods, we observed that variation in micro level decision optimality rises rapidly as the space becomes more heavily crowded. As crowdedness increases in the environment, the difference between ‘good’ and ‘bad’ decisions becomes more distinct; and suboptimal decisions become more frequent. In other words, optimality at individual level seems to be moderated by the level of crowdedness. At the macro level, numerical simulations showed that, for certain exit attributes (like exit congestion), extreme marginal valuations (or preferences) were optimal, whereas for certain other attributes (like exit visibility), intermediate levels of valuation were closer to the optimal. In most cases, the natural observed (or estimated) tendency of evacuees (at the aggregate level) was not quite at the optimum level, meaning that the system could improve by modifying individuals’ marginal valuations of exit attributes. Applications and Recommendations. These results highlight the importance of guiding evacuation decisions particularly in heavily crowded spaces. They also theoretically illustrate the potential benefit of influencing/modifying people’s evacuation strategies, so they make decisions that are collectively more efficient. A crucial step to this end, however, is to identify what optimum strategy is and under what circumstances people are likelier to make suboptimal decisions.

Background. The three terms “panic”, “irrationality”, and “herding” are ubiquitous in the crowd dynamics literature and have a strong influence on both modelling and management practices. The terms are also commonly shared between the scientific and nonscientific domains. The pervasiveness of the use of these terms is to the point where their underlying assumptions have often been treated as common knowledge by both experts and lay persons. Yet, at the same time, the literature on crowd dynamics presents ample debate, contradiction, and inconsistency on these topics. Method. This review is the first to systematically revisit these three terms in a unified study to highlight the scope of this debate. We extracted from peer-reviewed journal articles direct quotes that offer a definition, conceptualisation, or supporting/contradicting evidence on these terms and/or their underlying theories. To further examine the suitability of the term herding, a secondary and more detailed analysis is also conducted on studies that have specifically investigated this phenomenon in empirical settings. Results. The review shows that (i) there is no consensus on the definition for the terms panic and irrationality and that (ii) the literature is highly divided along discipline lines on how accurate these theories/terminologies are for describing human escape behaviour. The review reveals a complete division and disconnection between studies published by social scientists and those from the physical science domain and also between studies whose main focus is on numerical simulation versus those with empirical focus. (iii) Despite the ambiguity of the definitions and the missing consensus in the literature, these terms are still increasingly and persistently mentioned in crowd evacuation studies. (iv) Different to panic and irrationality, there is relative consistency in definitions of the term herding, with the term usually being associated with ‘(blind) imitation’. However, based on the findings of empirical studies, we argue why, despite the relative consistency in meaning, (v) the term herding itself lacks adequate nuance and accuracy for describing the role of ‘social influence’ in escape behaviour. Our conclusions also emphasise the importance of distinguishing between the social influence on various aspects of evacuation behaviour and avoiding generalisation across various behavioural layers. Conclusions. We argue that the use of these three terms in the scientific literature does not contribute constructively to extending the knowledge or to improving the modelling capabilities in the field of crowd dynamics. This is largely due to the ambiguity of these terms, the overly simplistic nature of their assumptions, or the fact that the theories they represent are not readily verifiable. Recommendations. We suggest that it would be beneficial for advancing this research field that the phenomena related to these three terms are clearly defined by more tangible and quantifiable terms and be formulated as verifiable hypotheses, so they can be operationalized for empirical testing.

How humans resolve non-trivial tradeoffs in their navigational choices between the social interactions (e.g., the presence and movements of others) and the physical factors (e.g., spatial distances, route visibility) when escaping from threats in crowded confined spaces? The answer to this question has major implications for the planning of evacuations and the safety of mass gatherings as well as the design of built environments. Due to the challenges of collecting behavioral data from naturally-occurring evacuation settings, laboratory-based virtual-evacuation experiments have been practiced in a number of studies. This class of experiments faces the traditional question of contextual bias and generalizability: How reliably can we infer humans' behavior from decisions made in hypothetical settings? Here, we address these questions by making a novel link between two different forms of empirical observations. We conduct hypothetical emergency exit-choice experiments framed as simple pictures, and then mimic those hypothetical scenarios in more realistic fashions through staging mock evacuation trials with actual crowds. Econometric choice models are estimated based on the observations made in both experimental contexts. The models are contrasted with each other from a number of perspectives including their predictions as well as the sign, magnitude, statistical significance, person-to-person variations (reflecting individuals' perception/preference differences) and the scale (reflecting context-dependent decision randomness) of their inferred parameters. Results reveal a surprising degree of resemblance between the models derived from the two contexts. Most strikingly, they produce fairly similar prediction probabilities whose differences average less than 10%. There is also unexpected consensus between the inferences derived from both experimental sources on many aspects of people's behavior notably in terms of the perception of social interactions. Results show that we could have elicited peoples' escape strategies with fair precision without observing them in action (i.e., simply by using only hypothetical-choice data as an inexpensive, practical and non-invasive experimental technique in this context). As a broader application, this offers promising evidence as to the potential applicability of the hypothetical-decision experiments to other decision contexts (at least for non-financial decisions) when field or real-world data is prohibitively unavailable. As a practical application, the behavioral insights inferred from our observations (reflected in the estimated parameters) can improve how accurately we predict the movement patterns of human crowds in emergency scenarios arisen in complex spaces. Fully-generic-in-parameters, our proposed models can even be directly introduced to a broad range of crowd simulation software to replicate navigation decision making of evacuees.