Birth-death-suppression Markov process and wildfires.

Birth and death Markov processes can model stochastic physical systems from percolation to disease spread and, in particular, wildfires. We introduce and analyze a birth-death-suppression Markov process as a model of controlled culling of an abstract, dynamic population. Using analytic techniques, we characterize the probabilities and timescales of outcomes like absorption at zero (extinguishment) and the probability of the cumulative population (burned area) reaching a given size. The latter requires control over the embedded Markov chain: this discrete process is solved using the Pollazcek orthogonal polynomials, a deformation of the Gegenbauer/ultraspherical polynomials. This allows analysis of processes with bounded cumulative population, corresponding to finite burnable substrate in the wildfire interpretation, with probabilities represented as spectral integrals. This technology is developed to lay the foundations for a dynamic decision support framework. We devise real-time risk metrics and suggest future directions for determining optimal suppression strategies, including multievent resource allocation problems and potential applications for reinforcement learning.

Healthcare resource use and costs of varicella and its complications: A systematic literature review.

Varicella is a highly contagious disease caused by the varicella zoster virus (VZV). While the disease is usually mild, severe complications can occur requiring costly hospitalization. A thorough understanding of the healthcare resource use (HCRU) and costs of varicella is needed to inform health-economic models of preventive strategies. A systematic literature review was carried out to retrieve relevant publications between 1999 and 2021, reporting HCRU and cost outcomes for varicella and its complications. Data were extracted and stratified according to pre-specified age groups and complication categories. Costs were re-based to a $US2020 footing using both purchasing power parity and the medical component of consumer price indexes. Data were summarized descriptively due to high heterogeneity in study design and outcome reporting. Forty-four publications fulfilled the inclusion and exclusion criteria of which 28 were conducted in Europe, 6 in Middle East and Asia, 5 in South America, 3 in North America, and 2 in multiple regions. Primary healthcare visits accounted for 30% to 85% of total direct costs. Hospitalization costs varied between $1,308 and $38,268 per episode depending on country, complication type, and length of stay, contributing between 2% and 60% to total direct costs. Indirect costs, mostly driven by workdays lost, accounted for approximately two-thirds of total costs due to varicella. The management of varicella and related complications can lead to substantial HCRU and costs for patients and the healthcare system. Additional research is needed to further characterize the varicella-associated economic burden and its broader impact from a societal standpoint.

Varicella, also known as chickenpox, is a highly contagious infectious disease which affects mostly children. Indeed, >90% of children will have had chickenpox by the age of 12 years. The symptoms are usually mild, but in some cases, serious complications can occur such as pneumonia, bacterial superinfection of the skin and encephalitis. A clear understanding of the complications of chickenpox for patients and the healthcare system would be helpful so that countries can assess the true health and economic burden of the disease.In this study, we have summarized existing published data from around the world. We have included studies that reported on the number of varicella cases, doctor visits, hospitalizations, and costs due to varicella and associated complications.These data showed that varicella causes high costs to the healthcare system. Even though less than 1% of varicella patients need to be hospitalized, costs remain high because varicella is so common. Furthermore, if the number of workdays lost are counted as well, then varicella-related costs are even higher.

Finding shortest and nearly shortest path nodes in large substantially incomplete networks by hyperbolic mapping.

Dynamic processes on networks, be it information transfer in the Internet, contagious spreading in a social network, or neural signaling, take place along shortest or nearly shortest paths. Computing shortest paths is a straightforward task when the network of interest is fully known, and there are a plethora of computational algorithms for this purpose. Unfortunately, our maps of most large networks are substantially incomplete due to either the highly dynamic nature of networks, or high cost of network measurements, or both, rendering traditional path finding methods inefficient. We find that shortest paths in large real networks, such as the network of protein-protein interactions and the Internet at the autonomous system level, are not random but are organized according to latent-geometric rules. If nodes of these networks are mapped to points in latent hyperbolic spaces, shortest paths in them align along geodesic curves connecting endpoint nodes. We find that this alignment is sufficiently strong to allow for the identification of shortest path nodes even in the case of substantially incomplete networks, where numbers of missing links exceed those of observable links. We demonstrate the utility of latent-geometric path finding in problems of cellular pathway reconstruction and communication security.

What is wrong with the Mission Dependency Index for US federal infrastructure decisions?

The Mission Dependency Index (MDI) is a risk metric used by US military services and federal agencies for guiding operations, management, and funding decisions for facilities. Despite its broad adoption for guiding the expenditure of billions in federal funds, several studies on MDI suggest it may have flaws that limit its efficacy. We present a detailed technical analysis of MDI to show how its flaws impact infrastructure decisions. We present the MDI used by the US Navy and develop a critique of current methods. We identify six problems with MDI that stem from its interpretation, use, and mathematical formulation, and we provide examples demonstrating how these flaws can bias decisions. We provide recommendations to overcome flaws for infrastructure risk decision making but ultimately recommend the US government develop a new metric less susceptible to bias.

Productivity losses in patients with newly diagnosed multiple myeloma following stem cell transplantation and the impact of maintenance therapy.

OBJECTIVE: This study examined productivity losses in European patients with newly diagnosed multiple myeloma (NDMM) undergoing autologous stem cell transplantation (ASCT), to better understand and model the impact of NDMM and lenalidomide maintenance therapy on productivity from a patient and societal perspective. METHODS: A cross-sectional online patient survey was conducted across the UK, Germany, France, Spain and Italy. A partitioned survival model was used to estimate productivity loss and the impact of maintenance therapy, using human capital (HC) and friction cost approaches. RESULTS: Of the 115 eligible survey respondents, 76.5% were economically active at the time of diagnosis and highlighted return to work as an important factor affecting their quality of life; only 39.1% of respondents were economically active post-ASCT. HC analyses estimated average total productivity losses per ASCT patient at EUR 290,601 over a 20-year period. Modelling the impact of maintenance therapy alone for these patients reduced average productivity losses by just over 10%. CONCLUSION: Patients with NDMM aspire to engage in productive lives post-ASCT, but most are unable to do so. Access to treatments extending remission and supporting engagement in a productive life can have a positive impact both for patients and wider society.

Rethinking Resilience Analytics.

The concept of "resilience analytics" has recently been proposed as a means to leverage the promise of big data to improve the resilience of interdependent critical infrastructure systems and the communities supported by them. Given recent advances in machine learning and other data-driven analytic techniques, as well as the prevalence of high-profile natural and man-made disasters, the temptation to pursue resilience analytics without question is almost overwhelming. Indeed, we find big data analytics capable to support resilience to rare, situational surprises captured in analytic models. Nonetheless, this article examines the efficacy of resilience analytics by answering a single motivating question: Can big data analytics help cyber-physical-social (CPS) systems adapt to surprise? This article explains the limitations of resilience analytics when critical infrastructure systems are challenged by fundamental surprises never conceived during model development. In these cases, adoption of resilience analytics may prove either useless for decision support or harmful by increasing dangers during unprecedented events. We demonstrate that these dangers are not limited to a single CPS context by highlighting the limits of analytic models during hurricanes, dam failures, blackouts, and stock market crashes. We conclude that resilience analytics alone are not able to adapt to the very events that motivate their use and may, ironically, make CPS systems more vulnerable. We present avenues for future research to address this deficiency, with emphasis on improvisation to adapt CPS systems to fundamental surprise.

Stability of a giant connected component in a complex network.

We analyze the stability of the network's giant connected component under impact of adverse events, which we model through the link percolation. Specifically, we quantify the extent to which the largest connected component of a network consists of the same nodes, regardless of the specific set of deactivated links. Our results are intuitive in the case of single-layered systems: the presence of large degree nodes in a single-layered network ensures both its robustness and stability. In contrast, we find that interdependent networks that are robust to adverse events have unstable connected components. Our results bring novel insights to the design of resilient network topologies and the reinforcement of existing networked systems.

Diabetes-related amputations create considerable public health burden in the UK.

AIMS: The main aim of this study was to assess the cost of diabetic amputation (both direct and indirect) to the National Health Service from the point of amputation onwards. METHODS: This systematic review involved searches of published literature between January 2007 and March 2017 mainly using the bibliographic databases, the Cochrane Library, EMBASE via Ovid®, MEDLINE via Ovid®, as well as grey literature, both in print and in electronic formats published through non-commercial publications, which reported the cost of amputation due to diabetic foot ulcers. RESULTS: The studies included in this review varied considerably in estimating the cost including cost elements and how those costs were categorised. The cost estimates for inpatient care associated with amputation involving admissions or procedures on amputation stumps in people with diabetes was £43.8 million. The annual expenditure for post-amputation care involving prosthetic care, physiotherapy, transport and wheelchair use was £20.8 million. CONCLUSIONS: There is a considerable public health and economic burden caused by diabetes-related amputations in England. More focussed research is needed with improved methods of estimating costs that would account for direct and indirect costs associated with diabetic amputation.

Geographic Hotspots of Critical National Infrastructure.

Failure of critical national infrastructures can result in major disruptions to society and the economy. Understanding the criticality of individual assets and the geographic areas in which they are located is essential for targeting investments to reduce risks and enhance system resilience. Within this study we provide new insights into the criticality of real-life critical infrastructure networks by integrating high-resolution data on infrastructure location, connectivity, interdependence, and usage. We propose a metric of infrastructure criticality in terms of the number of users who may be directly or indirectly disrupted by the failure of physically interdependent infrastructures. Kernel density estimation is used to integrate spatially discrete criticality values associated with individual infrastructure assets, producing a continuous surface from which statistically significant infrastructure criticality hotspots are identified. We develop a comprehensive and unique national-scale demonstration for England and Wales that utilizes previously unavailable data from the energy, transport, water, waste, and digital communications sectors. The testing of 200,000 failure scenarios identifies that hotspots are typically located around the periphery of urban areas where there are large facilities upon which many users depend or where several critical infrastructures are concentrated in one location.

Operational models of infrastructure resilience.

We propose a definition of infrastructure resilience that is tied to the operation (or function) of an infrastructure as a system of interacting components and that can be objectively evaluated using quantitative models. Specifically, for any particular system, we use quantitative models of system operation to represent the decisions of an infrastructure operator who guides the behavior of the system as a whole, even in the presence of disruptions. Modeling infrastructure operation in this way makes it possible to systematically evaluate the consequences associated with the loss of infrastructure components, and leads to a precise notion of "operational resilience" that facilitates model verification, validation, and reproducible results. Using a simple example of a notional infrastructure, we demonstrate how to use these models for (1) assessing the operational resilience of an infrastructure system, (2) identifying critical vulnerabilities that threaten its continued function, and (3) advising policymakers on investments to improve resilience.

Optimal vaccination in a stochastic epidemic model of two non-interacting populations.

Developing robust, quantitative methods to optimize resource allocations in response to epidemics has the potential to save lives and minimize health care costs. In this paper, we develop and apply a computationally efficient algorithm that enables us to calculate the complete probability distribution for the final epidemic size in a stochastic Susceptible-Infected-Recovered (SIR) model. Based on these results, we determine the optimal allocations of a limited quantity of vaccine between two non-interacting populations. We compare the stochastic solution to results obtained for the traditional, deterministic SIR model. For intermediate quantities of vaccine, the deterministic model is a poor estimate of the optimal strategy for the more realistic, stochastic case.

Measuring and modeling behavioral decision dynamics in collective evacuation.

Identifying and quantifying factors influencing human decision making remains an outstanding challenge, impacting the performance and predictability of social and technological systems. In many cases, system failures are traced to human factors including congestion, overload, miscommunication, and delays. Here we report results of a behavioral network science experiment, targeting decision making in a natural disaster. In a controlled laboratory setting, our results quantify several key factors influencing individual evacuation decision making in a controlled laboratory setting. The experiment includes tensions between broadcast and peer-to-peer information, and contrasts the effects of temporal urgency associated with the imminence of the disaster and the effects of limited shelter capacity for evacuees. Based on empirical measurements of the cumulative rate of evacuations as a function of the instantaneous disaster likelihood, we develop a quantitative model for decision making that captures remarkably well the main features of observed collective behavior across many different scenarios. Moreover, this model captures the sensitivity of individual- and population-level decision behaviors to external pressures, and systematic deviations from the model provide meaningful estimates of variability in the collective response. Identification of robust methods for quantifying human decisions in the face of risk has implications for policy in disasters and other threat scenarios, specifically the development and testing of robust strategies for training and control of evacuations that account for human behavior and network topologies.

Collective decision dynamics in the presence of external drivers.

We develop a sequence of models describing information transmission and decision dynamics for a network of individual agents subject to multiple sources of influence. Our general framework is set in the context of an impending natural disaster, where individuals, represented by nodes on the network, must decide whether or not to evacuate. Sources of influence include a one-to-many externally driven global broadcast as well as pairwise interactions, across links in the network, in which agents transmit either continuous opinions or binary actions. We consider both uniform and variable threshold rules on the individual opinion as baseline models for decision making. Our results indicate that (1) social networks lead to clustering and cohesive action among individuals, (2) binary information introduces high temporal variability and stagnation, and (3) information transmission over the network can either facilitate or hinder action adoption, depending on the influence of the global broadcast relative to the social network. Our framework highlights the essential role of local interactions between agents in predicting collective behavior of the population as a whole.

Dynamic resource allocation in disaster response: tradeoffs in wildfire suppression.

Challenges associated with the allocation of limited resources to mitigate the impact of natural disasters inspire fundamentally new theoretical questions for dynamic decision making in coupled human and natural systems. Wildfires are one of several types of disaster phenomena, including oil spills and disease epidemics, where (1) the disaster evolves on the same timescale as the response effort, and (2) delays in response can lead to increased disaster severity and thus greater demand for resources. We introduce a minimal stochastic process to represent wildfire progression that nonetheless accurately captures the heavy tailed statistical distribution of fire sizes observed in nature. We then couple this model for fire spread to a series of response models that isolate fundamental tradeoffs both in the strength and timing of response and also in division of limited resources across multiple competing suppression efforts. Using this framework, we compute optimal strategies for decision making scenarios that arise in fire response policy.

The glue ear 'epidemic': a historical perspective.

This paper explores the historical context of the dramatic rise in surgery for glue ear in the mid-20th century, and questions the published assertion that this represented a manufactured 'epidemic'. In examining historical sources, the reader's theoretical viewpoint greatly influences their conclusions: the sustained rise in treatment for glue ear may be seen as the advance of science in a golden age or the resistance of insular professionals to reason in the light of new scientific study methods. Current views on the practice of medicine, consumerism, science and standardisation, rationing and the nature of 'truth' all affect the way that we see this period. Technological advances clearly allowed better diagnosis and more effective treatment, but these did not appear to drive an 'epidemic', rather they were developed to meet the pre-existing challenges of otological practice. The proposition that an 'epidemic' was created does not appear to have any solid grounding. Society's perception of what constitutes disease and what needs treatment may have evolved, but the prevalence of other important diseases changed dramatically over this time period, and a real change in the epidemiology of glue ear cannot be dismissed. In defining the case for and against surgical treatment, a solely positivist, quantitative worldview cannot give us a complete picture of benefit and risk to individuals, families and society at large.

Evaluating the effectiveness of the Voxel-Man TempoSurg virtual reality simulator in facilitating learning mastoid surgery.

INTRODUCTION: The Chief Medical Officer's 2008 annual report highlighted the importance of simulation in medical training. Simulator development has focused on increasing authenticity and fidelity. Development has not necessarily been guided by evidence for educational improvement. On reviewing 34 years of literature, Issenberg et al identified ten features of high-fidelity medical simulators that facilitate learning. This study compares cadaveric temporal bone (CTB) simulation with the Voxel-Man TempoSurg (VT) virtual reality simulator in addressing these features. SUBJECTS AND METHODS: A questionnaire was designed comparing the VT with CTB. Fourteen trainees and six consultants completed the questionnaire after using the simulator. RESULTS: The VT is better at allowing repetitive practice, ease of control of difficulty, and capturing clinical and pathological variation. The VT is as good as CTB in curriculum integration, allowing multiple learning strategies, providing a controlled environment, individualising learning and defining benchmarks. It appears worse with regards to face validity and feedback. CONCLUSIONS: Virtual reality simulation and CTB have features that allow effective learning. Some of these are common to both, in some CTB is better and in others virtual reality is better. Virtual reality could be a significant mode of learning supplementary to CTB and experience in the operating theatre.

Developing expertise in surgery.

The concept of expertise is widely embraced but poorly defined in surgery. Dictionary definitions differentiate between authority and experience, while a third view sees expertise as a mind-set rather than a status. Both absolute and relative models of expertise have been developed, and each allows a richer understanding of the application of these concepts to emerge. Trainees must develop both independent and interdependent expertise, and an appreciation of the essentially constructivist and uncertain nature of medical knowledge. Approach may be more important than innate talent; the concepts of 'flow', sustained 'deliberate practice' and 'adaptive expertise' are examples of expert approaches to learning. Non-analytical reasoning plays a key role in decision making at expert levels of practice. A technically gifted surgeon may be seen as a safety hazard rather than an expert if inter-dependent expertise has not been developed. Key roles of a surgical educator are to facilitate the development of an expert approach to education and to enable entry into and movement towards the centre of an expert community of practice.

Difficulty in establishing a diagnosis in an uncommon presentation of a minor salivary gland tumour.

We report the case of a man presenting with a large, airway obstructing, minor salivary gland tumour arising from the right tonsillar base. Clinically, the tumour behaved in a malignant way. A firm diagnosis of malignancy could not be made histologically. The differential diagnosis included polymorphous low-grade adenocarcinoma, basal cell adenoma and adenoid cystic carcinoma. However, on balance, based on the clinical presentation, a diagnosis of malignancy was favoured and appropriate treatment was considered. Because of the patient's co-morbidities, further surgery was not an option and it was agreed that the patient should undergo radiotherapy instead. While he was waiting for radiotherapy, the tumour went into remission.

Diversity of graphs with highly variable connectivity.

A popular approach for describing the structure of many complex networks focuses on graph theoretic properties that characterize their large-scale connectivity. While it is generally recognized that such descriptions based on aggregate statistics do not uniquely characterize a particular graph and also that many such statistical features are interdependent, the relationship between competing descriptions is not entirely understood. This paper lends perspective on this problem by showing how the degree sequence and other constraints (e.g., connectedness, no self-loops or parallel edges) on a particular graph play a primary role in dictating many features, including its correlation structure. Building on recent work, we show how a simple structural metric characterizes key differences between graphs having the same degree sequence. More broadly, we show how the (often implicit) choice of a background set against which to measure graph features has serious implications for the interpretation and comparability of graph theoretic descriptions.