Experimental Study of Failures of the Rigid Spinal Posterior Fixation System Under Compressive Load Conditions: A Cadaver Study

Background Many studies have been conducted on the biomechanics of the spine to elucidate the fixation properties of spinal fusion surgery and the causes of instrumentation failure. Among these studies, there are some studies on load sharing in the spine and measurement using strain gauges and pressure gauges, but there is a lack of research on axial compressive loads. Methods Axial compressive load tests were performed on human cadaveric injured lumbar vertebrae fixed with pedicle screws (PS). Both the strain generated in the PS rod and the intradiscal pressure were measured. Subsequently, the stress generated in the PS rod and the load sharing of the spine and instrumentation were calculated. Results Even when only compressive load is applied, bending stress of more than 10 times the compression stress was generated in the rod, and the stress tended to concentrate on one rod. Rod deformation becomes kyphotic, in contrast to the lordotic deformation behavior of the lumbar spine. The stress shielding rate was approximately 40%, less than half. Conclusions This study obtained basic data useful for constructing and verifying numerical simulations that are effective for predicting and elucidating the causes of dislodgement and failure of spinal implants.

Social and Ethical Implications of Digital Crisis Technologies: Case Study of Pandemic Simulation Models During the COVID-19 Pandemic

BACKGROUND

Responses to public health crises are increasingly technological in nature, as the prominence of COVID-19-related statistics and simulations amply demonstrates. However, the use of technologies is preconditional and has various implications. These implications can not only affect acceptance but also challenge the acceptability of these technologies with regard to the ethical and normative dimension.

OBJECTIVE

This study focuses on pandemic simulation models as algorithmic governance tools that played a central role in political decision-making during the COVID-19 pandemic. To assess the social implications of pandemic simulation models, the premises of data collection, sorting, and evaluation must be disclosed and reflected upon. Consequently, the social construction principles of digital health technologies must be revealed and examined for their effects with regard to social, ethical, and ultimately political issues.

METHODS

This case study starts with a systematization of different simulation approaches to create a typology of pandemic simulation models. On the basis of this, various properties, functions, and challenges of these simulation models are revealed and discussed in detail from a socioscientific point of view.

RESULTS

The typology of pandemic simulation methods reveals the diversity of model-driven handling of pandemic threats. However, it is reasonable to assume that the use of simulation models could increasingly shift toward agent-based or artificial intelligence models in the future, thus promoting the logic of algorithmic decision-making in response to public health crises. As algorithmic decision-making focuses more on predicting future dynamics than statistical practices of assessing pandemic events, this study discusses this development in detail, resulting in an operationalized overview of the key social and ethical issues related to pandemic crisis technologies.

CONCLUSIONS

This study identifies 3 major recommendations for the future of pandemic crisis technologies.

System dynamics simulation models on overweight and obesity in children and adolescents: A systematic review

It has increasingly been recognized that developing successful obesity prevention policies and interventions requires understanding of the complex mechanisms driving the obesity pandemic and that models could be useful tools for simulating policies. This paper reviews system dynamics simulation models of mechanisms driving childhood overweight and obesity and/or testing of preventive interventions. A systematic literature search was conducted in six databases from inception to January 2023 using terms related to overweight/obesity, children, and system dynamics. Study descriptives, mechanisms, and where to intervene (the leverage points), as well as quality assessments of the simulation models were extracted by two researchers into a predetermined template and narratively synthesized. Seventeen papers describing 15 models were included. Models describing the mechanisms ranged from only intrapersonal factors to models cutting across multiple levels of the ecological model, but mechanisms across levels were lacking. The majority of interventions tested in the simulation models were changes to existing model parameters with less emphasis on models that alter system structure. In conclusion, existing models included mechanisms driving youth obesity at multiple levels of the ecological model. This is useful for developing an integrated simulation model combining mechanisms at multiple levels and allowing for testing fundamental system changes.

What makes women receptive to breast self-examination, animation, or simulation? - a comparative study

Background

Breast self-examination (BSE) plays an important role in the early diagnosis of breast cancer in India owing to the stigma attached to cancer. The authors compared the efficacies of animation video versus simulation techniques in BSE.

Methods

Women with no previous history of conditions affecting the breasts were included in this prospective observational study and divided into an animation or simulation arm. The latter was further divided into three subgroups as per the simulation models used : the German (Delta Healthcare), British (Health Edco), and Indian (low-cost, validated) models used for teaching BSE. The hybrid animation video had a 9 min runtime with a lecture on BSE and a virtual character performing BSE. In both the arms, participants filled in a validated modified patient satisfaction questionnaire.

Results

A total of 500 women participated. The mean age of the participants in the animation video arm was 20.21±3.88 years and 19.34±2.27, 22.94±9.6, and 18.97±1.31(20.41±5.99) years in the Indian, German, and British simulation models arm, respectively. The age difference between the two arms was statistically significant (P<0.05). Both animation video and simulation models were found to be useful by the participants. The participants' response to animation video being a better organized tool for learning BSE was statistically significant (90.48±7.98 vs. 84.02±15.09 P≤0.001) when compared to simulation models. The younger women (≤20 years) found these tools significantly more useful than those aged >20 years.

Conclusions

All models had good efficiency and utility as learning tools for BSE. However, large studies in BSE set up with combination models are needed.

Economic evaluations of artificial intelligence-based healthcare interventions: a systematic literature review of best practices in their conduct and reporting

Objectives: Health economic evaluations (HEEs) help healthcare decision makers understand the value of new technologies. Artificial intelligence (AI) is increasingly being used in healthcare interventions. We sought to review the conduct and reporting of published HEEs for AI-based health interventions. Methods: We conducted a systematic literature review with a 15-month search window (April 2021 to June 2022) on 17th June 2022 to identify HEEs of AI health interventions and update a previous review. Records were identified from 3 databases (Medline, Embase, and Cochrane Central). Two reviewers screened papers against predefined study selection criteria. Data were extracted from included studies using prespecified data extraction tables. Included studies were quality assessed using the National Institute for Health and Care Excellence (NICE) checklist. Results were synthesized narratively. Results: A total of 21 studies were included. The most common type of AI intervention was automated image analysis (9/21, 43%) mainly used for screening or diagnosis in general medicine and oncology. Nearly all were cost-utility (10/21, 48%) or cost-effectiveness analyses (8/21, 38%) that took a healthcare system or payer perspective. Decision-analytic models were used in 16/21 (76%) studies, mostly Markov models and decision trees. Three (3/16, 19%) used a short-term decision tree followed by a longer-term Markov component. Thirteen studies (13/21, 62%) reported the AI intervention to be cost effective or dominant. Limitations tended to result from the input data, authorship conflicts of interest, and a lack of transparent reporting, especially regarding the AI nature of the intervention. Conclusion: Published HEEs of AI-based health interventions are rapidly increasing in number. Despite the potentially innovative nature of AI, most have used traditional methods like Markov models or decision trees. Most attempted to assess the impact on quality of life to present the cost per QALY gained. However, studies have not been comprehensively reported. Specific reporting standards for the economic evaluation of AI interventions would help improve transparency and promote their usefulness for decision making. This is fundamental for reimbursement decisions, which in turn will generate the necessary data to develop flexible models better suited to capturing the potentially dynamic nature of AI interventions.

Expanding Clinician Access to High-Quality, Low-Cost Biomechanical Research: A Technical Report on the Carolina Neurosurgery and Spine Biomechanics Laboratory

Spine biomechanical research helps us better understand the spine in physiologic and pathologic states and gives us a mechanism by which to evaluate surgical interventions, generate and evaluate models of spine pathologies, and develop novel, data-driven surgical strategies and devices. Access to a biomechanical testing laboratory is therefore potentially invaluable to those who specialize in treating spine pathologies. A number of barriers to access have precluded many clinicians from pursuing their biomechanical research interests, foremost among these is cost. The Carolina Neurosurgery and Spine Biomechanics Research Laboratory (CNSBL) was developed as a model of a low-cost, easy-to-access laboratory capable of generating high-quality data in tests of axial load, tension, torque, displacement, and pathological model testing. Our experience in developing this laboratory suggests that a large number of basic biomechanical research inquiries can be studied in a laboratory composed of less than $7500 USD of hardware. We hope that this model serves as a roadmap for any like-minded practitioners seeking broader access to biomechanical testing facilities.

Effectiveness of Colorectal Cancer (CRC) Screening on All-Cause and CRC-Specific Mortality Reduction: A Systematic Review and Meta-Analysis

(1) Background: The aim of this study was to pool and compare all-cause and colorectal cancer (CRC) specific mortality reduction of CRC screening in randomized control trials (RCTs) and simulation models, and to determine factors that influence screening effectiveness. (2) Methods: PubMed, Embase, Web of Science and Cochrane library were searched for eligible studies. Multi-use simulation models or RCTs that compared the mortality of CRC screening with no screening in general population were included. CRC-specific and all-cause mortality rate ratios and 95% confidence intervals were calculated by a bivariate random model. (3) Results: 10 RCTs and 47 model studies were retrieved. The pooled CRC-specific mortality rate ratios in RCTs were 0.88 (0.80, 0.96) and 0.76 (0.68, 0.84) for guaiac-based fecal occult blood tests (gFOBT) and single flexible sigmoidoscopy (FS) screening, respectively. For the model studies, the rate ratios were 0.45 (0.39, 0.51) for biennial fecal immunochemical tests (FIT), 0.31 (0.28, 0.34) for biennial gFOBT, 0.61 (0.53, 0.72) for single FS, 0.27 (0.21, 0.35) for 10-yearly colonoscopy, and 0.35 (0.29, 0.42) for 5-yearly FS. The CRC-specific mortality reduction of gFOBT increased with higher adherence in both studies (RCT: 0.78 (0.68, 0.89) vs. 0.92 (0.87, 0.98), model: 0.30 (0.28, 0.33) vs. 0.92 (0.51, 1.63)). Model studies showed a 0.62-1.1% all-cause mortality reduction with single FS screening. (4) Conclusions: Based on RCTs and model studies, biennial FIT/gFOBT, single and 5-yearly FS, and 10-yearly colonoscopy screening significantly reduces CRC-specific mortality. The model estimates are much higher than in RCTs, because the simulated biennial gFOBT assumes higher adherence. The effectiveness of screening increases at younger screening initiation ages and higher adherences.

Validation of cost-effective model for breast self-examination

The incidence of breast cancer is increasing in India; it predominantly affects women in their 30s and 40s. The disease burden is very high given the high incidence of triple-negative disease in a large portion of the population. Early detection can save lives and aid in breast conservation surgery. Breast self-examination (BSE) is a valid tool for early breast cancer detection. If performed with the help of a simulation model that resembles a given culture and tradition, it can result in good outcomes from screening programs. We designed and validated an Indian model for BSE and reported the feasibility of this model.

Materials and methods

We designed an Indian model for the BSE based on the cultural mindset of Indian women. The design was finalized, and the model was constructed. It was then compared with preexisting international models and validated by in-depth interviews with validation experts from various fields involved in breast cancer management. Minor design revisions were made, followed by testing and re-testing. Finally, it was ready for public use.

Results

The in-depth interview was conducted using a validated modified animation multimedia questionnaire. The majority of the validation experts had used stimulation models before, and all stated that it could help teach women about BSE, and it was comparable with other preexisting internationally validated models (91.33±4.98%).

Conclusion

Using a breast model, women can learn to detect breast cancer as early as possible, and this can lead to good outcomes. We designed the model using easily available, cheap, and safe materials to keep it as realistic and useful as possible. The Indian BSE model can be used by Indian women to learn to detect breast lumps early. It is easily reproducible and cost-effective.

Laplace's demon in biology: Models of evolutionary prediction

Our ability to predict natural phenomena can be limited by incomplete information. This issue is exemplified by "Laplace's demon," an imaginary creature proposed in the 18th century, who knew everything about everything, and thus could predict the full nature of the universe forward or backward in time. Quantum mechanics, among other things, has cast doubt on the possibility of Laplace's demon in the full sense, but the idea still serves as a useful metaphor for thinking about the extent to which prediction is limited by incomplete information on deterministic processes versus random factors. Here, we use simple analytical models and computer simulations to illustrate how data limits can be captured in a Bayesian framework, and how they influence our ability to predict evolution. We show how uncertainty in measurements of natural selection, or low predictability of external environmental factors affecting selection, can greatly reduce predictive power, often swamping the influence of intrinsic randomness caused by genetic drift. Thus, more accurate knowledge concerning the causes and action of natural selection is key to improving prediction. Fortunately, our analyses and simulations show quantitatively that reasonable improvements in data quantity and quality can meaningfully increase predictability.

Modeling Effects of Vertebrate Host Exclosures and Host-Targeted Acaricides on Lone Star Tick (Amblyomma americanum, L.) Infestations

We used a spatially explicit model to simulate the potential effects of exclosures and acaricides targeted at medium-sized mammalian hosts on the local distribution and abundance of lone star ticks (Amblyomma americanum) within forestlands of the southeastern United States. Both exclosures and acaricides were successful in markedly reducing the densities of all off-host tick life stages inside the treatment areas. Densities dropped to almost zero immediately inside the edges of the exclosures, with noticeably depressed densities extending outward 30 to 60 m from the exclosures, and the simulated exclosures maintained their effectiveness as their sizes were decreased from 4.5 to 2.25 to 0.8 ha. Densities exhibited a smooth gradient across the edges of the acaricide-treated areas, with depressed densities extending ≈100 m outward from the edges, but with perceptible densities extending ≈60 m inward from the edges; thus, the simulated acaricide areas lost their effectiveness as size was decreased to slightly less than one-half the diameter of the activity range of the targeted host. Our simulation results indicated that off-host nymph densities responded to reductions of medium-sized host densities. These results suggest that targeting acaricides at medium-sized hosts may be an effective, and currently under-utilized, method for tick suppression.

Impacto sanitario de la prohibición total de publicidades de tabaco en argentina

Health impact of the total ban on advertising of tobacco productsThe objective was to estimate the health impact of the total ban on advertising of tobacco products in terms of avoided cardiovascular events in those over 35 years of age in Argentina.The Cardiovascular Disease Policy Model (CVDPM) was used, which is a Markov simulation model used to represent and project mortality and morbidity due to cardiovascular disease (CVD) in the population aged 35 or over. It constitutes a demographic-epidemiological model, which represents the population between 35 and 95 years of age and uses a logistic regression model based on the Framingham equation to estimate the annual incidence of cardiovascular disease. We assumed that implementing a complete ban on the advertising of tobacco products would lead to a 9% reduction in tobacco consumption.The complete ban on advertising could prevent 15,164 deaths over a period of 10 years, of which 2610 would be the result of coronary heart disease and 747 due to stroke. These reductions would mean an annual decrease of 0.46% of total deaths, 0.60% of deaths from coronary heart disease and 0.33% in deaths from stroke. In addition, during the same period, it would avoid 6630 acute myocardial infarctions and 2851 strokes (reductions of 1.35% and 0.40%, respectively).We hope that these findings might contribute to the strengthening of sanitary tobacco control policies in Argentina based on the remarkable benefits of banning the advertising of tobacco products in full and in line with current global recommendations.

Modelling and Simulation of Collaborative Surveillance for Unmanned Traffic Management

Unmanned traffic management (UTM) systems rely on collaborative position reporting to track unmanned aerial system (UAS) operations over wide unsurveilled (with counter-UAS systems) areas. Many different technologies, such as Remote-ID, ADS-B, FLARM, or MLAT might be used for this purpose, in addition to the direct exploitation of C2 telemetry, relayed though cellular networks. This paper provides an overview of the most used collaborative sensors and surveillance systems in this context, analyzing their main technical parameters and performance effects. In addition, this paper proposes an abstracted general statistical simulation model covering message encoding, network capacity and access, sensors coverage and distribution, message transmission and decoding. Making use of this abstracted model, this paper proposes a particularized set of simulation models for ADS-B, FLARM and Remote-Id; it is thus useful to test their potential integration in UTM systems. Finally, a comparative analysis, based on simulation, of these systems, is performed. It is shown that the most relevant effects are those related with quantification and the potential saturation of the communication channels leading to collisions and delays.

Review and Simulation of Counter-UAS Sensors for Unmanned Traffic Management

Noncollaborative surveillance of airborne UAS (Unmanned Aerial System) is a key enabler to the safe integration of UAS within a UTM (Unmanned Traffic Management) ecosystem. Thus, a wide variety of new sensors (known as Counter-UAS sensors) are being developed to provide real-time UAS tracking, ranging from radar, RF analysis and image-based detection to even sound-based sensors. This paper aims to discuss the current state-of-the art technology in this wide variety of sensors (both academically and commercially) and to propose a set of simulation models for them. Thus, the review is focused on identifying the key parameters and processes that allow modeling their performance and operation, which reflect the variety of measurement processes. The resulting simulation models are designed to help evaluate how sensors' performances affect UTM systems, and specifically the implications in their tracking and tactical services (i.e., tactical conflicts with uncontrolled drones). The simulation models cover probabilistic detection (i.e., false alarms and probability of detection) and measurement errors, considering equipment installation (i.e., monostatic vs. multistatic configurations, passive sensing, etc.). The models were integrated in a UTM simulation platform and simulation results are included in the paper for active radars, passive radars, and acoustic sensors.

Choosing a Metamodel of a Simulation Model for Uncertainty Quantification

BACKGROUND

Metamodeling may substantially reduce the computational expense of individual-level state transition simulation models (IL-STM) for calibration, uncertainty quantification, and health policy evaluation. However, because of the lack of guidance and readily available computer code, metamodels are still not widely used in health economics and public health. In this study, we provide guidance on how to choose a metamodel for uncertainty quantification.

METHODS

We built a simulation study to evaluate the prediction accuracy and computational expense of metamodels for uncertainty quantification using life-years gained (LYG) by treatment as the IL-STM outcome. We analyzed how metamodel accuracy changes with the characteristics of the simulation model using a linear model (LM), Gaussian process regression (GP), generalized additive models (GAMs), and artificial neural networks (ANNs). Finally, we tested these metamodels in a case study consisting of a probabilistic analysis of a lung cancer IL-STM.

RESULTS

In a scenario with low uncertainty in model parameters (i.e., small confidence interval), sufficient numbers of simulated life histories, and simulation model runs, commonly used metamodels (LM, ANNs, GAMs, and GP) have similar, good accuracy, with errors smaller than 1% for predicting LYG. With a higher level of uncertainty in model parameters, the prediction accuracy of GP and ANN is superior to LM. In the case study, we found that in the worst case, the best metamodel had an error of about 2.1%.

CONCLUSION

To obtain good prediction accuracy, in an efficient way, we recommend starting with LM, and if the resulting accuracy is insufficient, we recommend trying ANNs and eventually also GP regression.

Cost-Effectiveness of Anti-Epidermal Growth Factor Receptor Therapy Versus Bevacizumab in KRAS Wild-Type (WT), Pan-RAS WT, and Pan-RAS WT Left-Sided Metastatic Colorectal Cancer

Objectives

We aimed to compare the economic value of chemotherapy plus anti-epidermal growth factor receptor (anti-EGFR) monoclonal antibody (mAb) against chemotherapy with bevacizumab (Bev, an anti-vascular endothelial growth factor mAb) as first-line treatment in KRAS wild-type (WT), pan-RAS WT and pan-RAS WT left-sided metastatic colorectal cancer (mCRC) patients from the Hong Kong societal perspective.

Materials and Methods

We developed Markov models and 10-year horizon to estimate costs, quality-adjusted life years (QALYs), and incremental cost-effectiveness ratio (ICER) of chemotherapy plus anti-EGFR therapy against chemotherapy plus Bev in KRAS WT, pan-RAS WT, and pan-RAS WT left-sided mCRC. We considered two times of the local gross domestic product per capita (GDPpc) as the willingness-to-pay (WTP) threshold (2× GDPpc; US$97,832).

Results

Adding anti-EGFR mAb to chemotherapy provides additional 0.24 (95% confidence interval [CI] 0.19-0.29), 0.32 (95% CI 0.27-0.37), and 0.57 (95% CI 0.49-0.63) QALY compared to adding Bev in KRAS WT, pan-RAS WT, and left-sided pan-RAS WT mCRC populations respectively. The corresponding ICER is US$106,847 (95% CI 87,806-134,523), US$88,565 (95% CI 75,678-105,871), US$76,537 (95% CI 67,794-87,917) per QALY gained, respectively.

Conclusions

Anti-EGFR therapy is more cost-effective than Bev as a first-line targeted therapy in left-sided pan-RAS WT and pan-RAS WT, with ICER <US$100,000/QALY, compared to KRAS WT mCRC population.

Cost-effectiveness of risk-based breast cancer screening: A systematic review

To analyse published evidence on the economic evaluation of risk-based screening (RBS), a full systematic literature review was conducted. After a quality appraisal, we compared the cost-effectiveness of risk-based strategies (low-risk, medium-risk and high-risk) with no screening and age-based screening. Studies were also analysed for modelling, risk stratification methods, input parameters, data sources and harms and benefits. The 10 modelling papers analysed were based on screening performance of film-based mammography (FBM) (three); digital mammography (DM) and FBM (two); DM alone (three); DM, ultrasound (US) and magnetic resonance imaging (one) and DM and US (one). Seven studies did not include the cost of risk-stratification, and one did not consider the cost of diagnosis. Disutility was incorporated in only six studies (one for screening and five for diagnosis). None of the studies reported disutility of risk-stratification (being considered as high-risk). Risk-stratification methods varied from only breast density (BD) to the combination of familial risk, genetic susceptibility, lifestyle, previous biopsies, Jewish ancestry and reproductive history. Less or no screening in low-risk women and more frequent mammography screening in high-risk women was more cost-effective compared to no screening and age-based screening. High-risk women screened annually yielded a higher mortality rate reduction and more quality-adjusted life years at the expense of higher cost and false positives. RBS can be cost effective compared to the alternatives. However, heterogeneity among risk-stratification methods, input parameters, and weaknesses in the methodologies hinder the derivation of robust conclusions. Therefore, further studies are warranted to assess newer technologies and innovative risk-stratification methods.

Examining the diagnostic pathway for lung cancer patients in Wales using discrete event simulation

BACKGROUND

UK's National Health Service (NHS) has one of the poorest lung cancer survival rates in Europe. To improve patient outcomes, a single cancer pathway was introduced in the NHS. In this study, a Discrete Event Simulation was developed to understand bottlenecks during lung cancer treatment.

METHODS

This study focused on the lung cancer diagnostic pathways at two Welsh hospitals. Discrete Event Simulation is a computer-based method that has been effectively used in demand and capacity planning. In this study, simulation models were developed for the current and proposed single cancer pathways. The validated models were used to provide Key Performance Indicators. Several "what-if" scenarios were considered for the current and proposed pathways.

RESULTS

Under the current diagnostic pathway, the mean time to treatment for a surgery patient was 68 days at the Royal Glamorgan Hospital and 79 days at Prince Charles Hospital. For chemotherapy patients, the mean time to treatment was 52 days at the Royal Glamorgan Hospital and 57 days at Prince Charles Hospital. For radiotherapy patients, the mean time to treatment was 44 days at Royal Glamorgan Hospital and 54 days at Prince Charles Hospital. Ensuring that the patient attends their first outpatient appointment within 7 days and streamlining the diagnostic tests would have the potential to remove approximately 20 days from the current lung cancer pathway resulting in a 20-25% increase of patients receiving treatment within 62 days. Ensuring that patients begin their treatment within 21 days of diagnosis sees almost all patients comply with the 62-day target.

CONCLUSIONS

Discrete Event Simulation coupled with a detailed statistical analysis provides a useful decision support tool which can be used to examine the current and proposed lung cancer pathways in terms of time spent on the pathway.

Novel evaluation technology for the demand characteristics of 3D food printing materials: a review

As a recently developed way of food manufacturing - 3D printing - is bringing about a revolution in the food industry. Rheological and mechanical properties of food material being printed are the determinants of their printability. Therefore, it is important to analyze the requirements of different 3D printing technologies on material properties and to evaluate the performance of the printed materials. In this review, the printing characteristics and classification of food materials are discussed. The four commonly used 3D printing techniques e.g. extrusion-based printing, selective sintering printing (SLS), binder jetting, and inkjet printing, are outlined along with suitable material characteristics required for each printing technique. Finally, recent technologies for evaluation of 3D printed products including low field nuclear magnetic resonance (LF-NMR), computer numerical simulation, applied reference material, morphological identification, and some novel instrumental analysis techniques are highlighted.

Economic Evaluation in Opioid Modeling: Systematic Review

OBJECTIVES

The rapid increase in opioid overdose and opioid use disorder (OUD) over the past 20 years is a complex problem associated with significant economic costs for healthcare systems and society. Simulation models have been developed to capture and identify ways to manage this complexity and to evaluate the potential costs of different strategies to reduce overdoses and OUD. A review of simulation-based economic evaluations is warranted to fully characterize this set of literature.

METHODS

A systematic review of simulation-based economic evaluation (SBEE) studies in opioid research was initiated by searches in PubMed, EMBASE, and EbscoHOST. Extraction of a predefined set of items and a quality assessment were performed for each study.

RESULTS

The screening process resulted in 23 SBEE studies ranging by year of publication from 1999 to 2019. Methodological quality of the cost analyses was moderately high. The most frequently evaluated strategies were methadone and buprenorphine maintenance treatments; the only harm reduction strategy explored was naloxone distribution. These strategies were consistently found to be cost-effective, especially naloxone distribution and methadone maintenance. Prevention strategies were limited to abuse-deterrent opioid formulations. Less than half (39%) of analyses adopted a societal perspective in their estimation of costs and effects from an opioid-related intervention. Prevention strategies and studies' accounting for patient and physician preference, changing costs, or result stratification were largely ignored in these SBEEs.

CONCLUSION

The review shows consistently favorable cost analysis findings for naloxone distribution strategies and opioid agonist treatments and identifies major gaps for future research.

After the lockdown: macroeconomic adjustment to the COVID-19 pandemic in sub-Saharan Africa

The COVID-19 pandemic is ripping around most of the world, but not in Africa; at least, not yet. At the same time, the policy response is remarkably uniform: most of sub-Saharan Africa went into lockdown from the second week in March. What happens next for the pandemic across Africa is uncertain, but the March lockdowns are unlikely to have contained the epidemic by themselves.

What is clear is that the combination of domestic lockdowns and the spill-over from the global recession means immediate and severe hardship. This paper looks beyond the public health aspects of the pandemic to examine the medium-term macroeconomic adjustment challenge confronting domestic policy-makers and international donors. We combine epidemiological and macroeconomic models to calibrate the scale of the combined shock to a representative low-income African economy and to show how alternative policy options for slowing transmission of COVID-19 impact on public revenue, and on GDP in the short run, and hence shape the path to recovery. Noting that the first lockdown, however costly, does not by itself eliminate the likelihood of a re-emergence of the epidemic, we then frame the agenda for key macroeconomic and public finance policies to sustain recovery, growth, and poverty reduction in sub-Saharan Africa.

The initial hit to consumption will be up to one-third. All the public policy options are grim. International donor finance of US$40–50 billion, together with domestic reform to accelerate recovery, would make a significant difference to the outlook for poverty.

A modern method of multiple working hypotheses to improve inference in ecology

Science provides a method to learn about the relationships between observed patterns and the processes that generate them. However, inference can be confounded when an observed pattern cannot be clearly and wholly attributed to a hypothesized process. Over-reliance on traditional single-hypothesis methods (i.e. null hypothesis significance testing) has resulted in replication crises in several disciplines, and ecology exhibits features common to these fields (e.g. low-power study designs, questionable research practices, etc.). Considering multiple working hypotheses in combination with pre-data collection modelling can be an effective means to mitigate many of these problems. We present a framework for explicitly modelling systems in which relevant processes are commonly omitted, overlooked or not considered and provide a formal workflow for a pre-data collection analysis of multiple candidate hypotheses. We advocate for and suggest ways that pre-data collection modelling can be combined with consideration of multiple working hypotheses to improve the efficiency and accuracy of research in ecology.

The Triangle Wave Versus the Cosine: How Classical Systems Can Optimally Approximate EPR-B Correlations

The famous singlet correlations of a composite quantum system consisting of two two-level components in the singlet state exhibit notable features of two kinds. One kind are striking certainty relations: perfect anti-correlation, and perfect correlation, under certain joint settings. The other kind are a number of symmetries, namely invariance under a common rotation of the settings, invariance under exchange of components, and invariance under exchange of both measurement outcomes. One might like to restrict attention to rotations in the plane since those are the ones most commonly investigated experimentally. One can then also further distinguish between the case of discrete rotations (e.g., only settings which are a whole number of degrees are allowed) and continuous rotations. We study the class of classical correlation functions, i.e., generated by classical physical systems, satisfying all these symmetries, in the continuous, planar, case. We call such correlation functions classical EPR-B correlations. It turns out that if the certainty relations and rotational symmetry holds at the level of the correlations, then rotational symmetry can be imposed “for free” on the underlying classical physical model by adding an extra randomisation level. The other binary symmetries are obtained “for free”. This leads to a simple heuristic description of all possible classical EPR-B correlations in terms of a “spinning bi-coloured disk” model. We deliberately use the word “heuristic” because technical mathematical problems remain wide open concerning the transition from finite or discrete to continuous. The main purpose of this paper is to bring this situation to the attention of the mathematical community. We do show that the widespread idea that “quantum correlations are more extreme than classical physics would allow” is at best highly inaccurate, through giving a concrete example of a classical correlation which satisfies all the symmetries and all the certainty relations and which exceeds the quantum correlations over a whole range of settings. It is found by a search procedure in which we randomly generate classical physical models and, for each generated model, evaluate its properties in a further Monte-Carlo simulation of the model itself.

Introduction to the Cancer Intervention and Surveillance Modeling Network (CISNET) Breast Cancer Models

The Cancer Intervention and Surveillance Modeling Network (CISNET) Breast Cancer Working Group is a consortium of National Cancer Institute-sponsored investigators who use statistical and simulation modeling to evaluate the impact of cancer control interventions on long-term population-level breast cancer outcomes such as incidence and mortality and to determine the impact of different breast cancer control strategies. The CISNET breast cancer models have been continuously funded since 2000. The models have gone through several updates since their inception to reflect advances in the understanding of the molecular basis of breast cancer, changes in the prevalence of common risk factors, and improvements in therapy and early detection technology. This article provides an overview and history of the CISNET breast cancer models, provides an overview of the major changes in the model inputs over time, and presents examples for how CISNET breast cancer models have been used for policy evaluation.

Accuracy of the Ventilator Automated Displayed Respiratory Mechanics in Passive and Active Breathing Conditions: A Bench Study

BACKGROUND

New-generation ventilators display dynamic measures of respiratory mechanics, such as compliance, resistance, and auto-PEEP. Knowledge of the respiratory mechanics is paramount to clinicians at the bedside. These calculations are obtained automatically by using the least squares fitting method of the equation of motion. The accuracy of these calculations in static and dynamic conditions have not been fully validated or examined in different clinical conditions or various ventilator modes.

METHODS

A bench study was performed by using a lung simulator to compare the ventilator automated calculations during passive and active conditions. Three clinical scenarios (normal, COPD, and ARDS) were simulated with known compliances and resistance set per respective condition: normal (compliance 50 mL/cm H₂O, resistance 10 cm H₂O/L/s), COPD (compliance 60 mL/cm H₂O, resistance 22 cm H₂O/L/s), and ARDS (compliance 30 mL/cm H₂O, and resistance 13 cm H₂O/L/s). Each scenario was subjected to 4 different muscle pressures (P_mus): 0, -5, -10, and -15 cm H₂O. All the experiments were done using adaptive support ventilation. The resulting automated dynamic calculations of compliance and resistance were then compared based on the clinical scenarios.

RESULTS

There was a small bias (average error) and level of agreement in the passive conditions in all the experiments; however, these errors and levels of agreement got progressively higher proportional to the increased P_mus. There was a strong positive correlation between P_mus and compliance measured as well as a strong negative correlation between P_mus and resistance measured.

CONCLUSIONS

Automated displayed calculations of respiratory mechanics were not dependable or accurate in active breathing conditions. The calculations were clinically more reliable in passive conditions. We propose different methods of calculating P_mus, which, if incorporated into the calculations, would improve the accuracy of respiratory mechanics made via the least squares fitting method in actively breathing conditions.

Monte Carlo Simulations Demonstrate Algorithmic Interventions Over Time Reduce Hospitalisation in Patients With Schizophrenia and Bipolar Disorder

Non-adherence with pharmacologic treatment is associated with increased rates of relapse and rehospitalisation among patients with schizophrenia and bipolar disorder. To improve treatment response, remission, and recovery, research efforts are still needed to elucidate how to effectively map patient’s response to medication treatment including both therapeutic and adverse effects, compliance, and satisfaction in the prodromal phase of illness (ie, the time period in between direct clinical consultation and relapse). The Actionable Intime Insights (AI²) application draws information from Australian Medicare administrative claims records in real time when compliance with treatment does not meet best practice guidelines for managing chronic severe mental illness. Subsequently, the AI² application alerts clinicians and patients when patients do not adhere to guidelines for treatment. The aim of this study was to evaluate the impact of the AI² application on the risk of hospitalisation among simulated patients with schizophrenia and bipolar disorder. Monte Carlo simulation methodology was used to estimate the impact of the AI² intervention on the probability of hospitalisation over a 2-year period. Results indicated that when the AI² algorithmic intervention had an efficacy level of (>0.6), over 80% of actioned alerts were contributing to reduced hospitalisation risk among the simulated patients. Such findings indicate the potential utility of the AI² application should replication studies validate its methodologic and ecological rigour in real-world settings.

Network Rewiring in Cancer: Applications to Melanoma Cell Lines and the Cancer Genome Atlas Patients

Genes do not work in isolation, but rather as part of networks that have many feedback and redundancy mechanisms. Studying the properties of genetic networks and how individual genes contribute to overall network functions can provide insight into genetically-mediated disease processes. Most analytical techniques assume a network topology based on normal state networks. However, gene perturbations often lead to the rewiring of relevant networks and impact relationships among other genes. We apply a suite of analysis methodologies to assess the degree of transcriptional network rewiring observed in different sets of melanoma cell lines using whole genome gene expression microarray profiles. We assess evidence for network rewiring in melanoma patient tumor samples using RNA-sequence data available from The Cancer Genome Atlas. We make a distinction between “unsupervised” and “supervised” network-based methods and contrast their use in identifying consistent differences in networks between subsets of cell lines and tumor samples. We find that different genes play more central roles within subsets of genes within a broader network and hence are likely to be better drug targets in a disease state. Ultimately, we argue that our results have important implications for understanding the molecular pathology of melanoma as well as the choice of treatments to combat that pathology.

Indirectly connected: simple social differences can explain the causes and apparent consequences of complex social network positions

Animal societies are often structurally complex. How individuals are positioned within the wider social network (i.e. their indirect social connections) has been shown to be repeatable, heritable and related to key life-history variables. Yet, there remains a general lack of understanding surrounding how complex network positions arise, whether they indicate active multifaceted social decisions by individuals, and how natural selection could act on this variation. We use simulations to assess how variation in simple social association rules between individuals can determine their positions within emerging social networks. Our results show that metrics of individuals' indirect connections can be more strongly related to underlying simple social differences than metrics of their dyadic connections. External influences causing network noise (typical of animal social networks) generally inflated these differences. The findings demonstrate that relationships between complex network positions and other behaviours or fitness components do not provide sufficient evidence for the presence, or importance, of complex social behaviours, even if direct network metrics provide less explanatory power than indirect ones. Interestingly however, a plausible and straightforward heritable basis for complex network positions can arise from simple social differences, which in turn creates potential for selection to act on indirect connections.

Validated Computational Model to Compute Re-apposition Pressures for Treating Type-B Aortic Dissections

The use of endovascular treatment in the thoracic aorta has revolutionized the clinical approach for treating Stanford type B aortic dissection. The endograft procedure is a minimally invasive alternative to traditional surgery for the management of complicated type-B patients. The endograft is first deployed to exclude the proximal entry tear to redirect blood flow toward the true lumen and then a stent graft is used to push the intimal flap against the false lumen (FL) wall such that the aorta is reconstituted by sealing the FL. Although endovascular treatment has reduced the mortality rate in patients compared to those undergoing surgical repair, more than 30% of patients who were initially successfully treated require a new endovascular or surgical intervention in the aortic segments distal to the endograft. One reason for failure of the repair is persistent FL perfusion from distal entry tears. This creates a patent FL channel which can be associated with FL growth. Thus, it is necessary to develop stents that can promote full re-apposition of the flap leading to complete closure of the FL. In the current study, we determine the radial pressures required to re-appose the mid and distal ends of a dissected porcine thoracic aorta using a balloon catheter under static inflation pressure. The same analysis is simulated using finite element analysis (FEA) models by incorporating the hyperelastic properties of porcine aortic tissues. It is shown that the FEA models capture the change in the radial pressures required to re-appose the intimal flap as a function of pressure. The predictions from the simulation models match closely the results from the bench experiments. The use of validated computational models can support development of better stents by calculating the proper radial pressures required for complete re-apposition of the intimal flap.

Integrating trait-based empirical and modeling research to improve ecological restoration

A global ecological restoration agenda has led to ambitious programs in environmental policy to mitigate declines in biodiversity and ecosystem services. Current restoration programs can incompletely return desired ecosystem service levels, while resilience of restored ecosystems to future threats is unknown. It is therefore essential to advance understanding and better utilize knowledge from ecological literature in restoration approaches. We identified an incomplete linkage between global change ecology, ecosystem function research, and restoration ecology. This gap impedes a full understanding of the interactive effects of changing environmental factors on the long-term provision of ecosystem functions and a quantification of trade-offs and synergies among multiple services. Approaches that account for the effects of multiple changing factors on the composition of plant traits and their direct and indirect impact on the provision of ecosystem functions and services can close this gap. However, studies on this multilayered relationship are currently missing. We therefore propose an integrated restoration agenda complementing trait-based empirical studies with simulation modeling. We introduce an ongoing case study to demonstrate how this framework could allow systematic assessment of the impacts of interacting environmental factors on long-term service provisioning. Our proposed agenda will benefit restoration programs by suggesting plant species compositions with specific traits that maximize the supply of multiple ecosystem services in the long term. Once the suggested compositions have been implemented in actual restoration projects, these assemblages should be monitored to assess whether they are resilient as well as to improve model parameterization. Additionally, the integration of empirical and simulation modeling research can improve global outcomes by raising the awareness of which restoration goals can be achieved, due to the quantification of trade-offs and synergies among ecosystem services under a wide range of environmental conditions.

Modeling Ductal Carcinoma In Situ (DCIS): An Overview of CISNET Model Approaches

BACKGROUND

Ductal carcinoma in situ (DCIS) can be a precursor to invasive breast cancer. Since the advent of screening mammography in the 1980's, the incidence of DCIS has increased dramatically. The value of screen detection and treatment of DCIS, however, is a matter of controversy, as it is unclear the extent to which detection and treatment of DCIS prevents invasive disease and reduces breast cancer mortality. The aim of this paper is to provide an overview of existing Cancer Intervention and Surveillance Modelling Network (CISNET) modeling approaches for the natural history of DCIS, and to compare these to other modeling approaches reported in the literature.

DESIGN

Five of the 6 CISNET models currently include DCIS. Most models assume that some, but not all, lesions progress to invasive cancer. The natural history of DCIS cannot be directly observed and the CISNET models differ in their assumptions and in the data sources used to estimate the DCIS model parameters.

RESULTS

These model differences translate into variation in outcomes, such as the amount of overdiagnosis of DCIS, with estimates ranging from 34% to 72% for biennial screening from ages 50 to 74 y. The other models described in the literature also report a large range in outcomes, with progression rates varying from 20% to 91%.

LIMITATIONS

DCIS grade was not yet included in the CISNET models.

CONCLUSION

In the future, DCIS data by grade from active surveillance trials, the development of predictive markers of progression probability, and evidence from other screening modalities, such as tomosynthesis, may be used to inform and improve the models' representation of DCIS, and might lead to convergence of the model estimates. Until then, the CISNET model results consistently show a considerable amount of overdiagnosis of DCIS, supporting the safety and value of observational trials for low-risk DCIS.

Common Model Inputs Used in CISNET Collaborative Breast Cancer Modeling

BACKGROUND

Since their inception in 2000, the Cancer Intervention and Surveillance Network (CISNET) breast cancer models have collaborated to use a nationally representative core of common input parameters to represent key components of breast cancer control in each model. Employment of common inputs permits greater ability to compare model output than when each model begins with different input parameters. The use of common inputs also enhances inferences about the results, and provides a range of reasonable results based on variations in model structure, assumptions, and methods of use of the input values. The common input data are updated for each analysis to ensure that they reflect the most current practice and knowledge about breast cancer. The common core of parameters includes population rates of births and deaths; age- and cohort-specific temporal rates of breast cancer incidence in the absence of screening and treatment; effects of risk factors on incidence trends; dissemination of plain film and digital mammography; screening test performance characteristics; stage or size distribution of screen-, interval-, and clinically- detected tumors by age; the joint distribution of ER/HER2 by age and stage; survival in the absence of screening and treatment by stage and molecular subtype; age-, stage-, and molecular subtype-specific therapy; dissemination and effectiveness of therapies over time; and competing non-breast cancer mortality.

METHOD AND RESULTS

In this paper, we summarize the methods and results for the common input values presently used in the CISNET breast cancer models, note assumptions made because of unobservable phenomena and/or unavailable data, and highlight plans for the development of future parameters.

CONCLUSION

These data are intended to enhance the transparency of the breast CISNET models.

Modelling and simulating decision processes of linked lives: An approach based on concurrent processes and stochastic race

Individuals' decision processes play a central role in understanding modern migration phenomena and other demographic processes. Their integration into agent-based computational demography depends largely on suitable support by a modelling language. We are developing the Modelling Language for Linked Lives (ML3) to describe the diverse decision processes of linked lives succinctly in continuous time. The context of individuals is modelled by networks the individual is part of, such as family ties and other social networks. Central concepts, such as behaviour conditional on agent attributes, age-dependent behaviour, and stochastic waiting times, are tightly integrated in the language. Thereby, alternative decisions are modelled by concurrent processes that compete by stochastic race. Using a migration model, we demonstrate how this allows for compact description of complex decisions, here based on the Theory of Planned Behaviour. We describe the challenges for the simulation algorithm posed by stochastic race between multiple concurrent complex decisions.

A Risk Assessment Framework for Seed Degeneration: Informing an Integrated Seed Health Strategy for Vegetatively Propagated Crops

Pathogen buildup in vegetative planting material, termed seed degeneration, is a major problem in many low-income countries. When smallholder farmers use seed produced on-farm or acquired outside certified programs, it is often infected. We introduce a risk assessment framework for seed degeneration, evaluating the relative performance of individual and combined components of an integrated seed health strategy. The frequency distribution of management performance outcomes was evaluated for models incorporating biological and environmental heterogeneity, with the following results. (1) On-farm seed selection can perform as well as certified seed, if the rate of success in selecting healthy plants for seed production is high; (2) when choosing among within-season management strategies, external inoculum can determine the relative usefulness of 'incidence-altering management' (affecting the proportion of diseased plants/seeds) and 'rate-altering management' (affecting the rate of disease transmission in the field); (3) under severe disease scenarios, where it is difficult to implement management components at high levels of effectiveness, combining management components can be synergistic and keep seed degeneration below a threshold; (4) combining management components can also close the yield gap between average and worst-case scenarios. We also illustrate the potential for expert elicitation to provide parameter estimates when empirical data are unavailable. [Formula: see text] Copyright © 2017 The Author(s). This is an open access article distributed under the CC BY-NC-ND 4.0 International license .

Testing and application of simple semi-analytical models for soil temperature estimation and prediction in environmental assessments

The ability of various semi-analytical models to predict soil temperature profiles in an experimental plot during a 16-year monitoring study for soil depths up to 120 cm is evaluated. The models are developed from an analytical model by replacing the steady-state soil temperature with easily obtained hourly and daily average temperature values. Such values include the hourly air temperature, the daily average air temperature, the hourly soil temperature of selected soil depths from three daily observations, the daily average of the soil temperature profile and the hourly soil temperature for the bottom depth. The performance evaluation results show that, in principle, all models exhibit high correlation (R² values in the range 0.85-0.97), indicating a very good agreement between measured and predicted values. In addition, error statistics reveal that the best performance in terms of Mean Absolute Error (MAE) and Root Mean Square Error (RMSE) is the model based on the daily average of the soil temperature profile with MAE values in the range of 0-0.4°C and RMSE values in the range of 0.1-1.5°C.

Ophthalmoscopy simulation: advances in training and practice for medical students and young ophthalmologists

OBJECTIVE

To describe and appraise the latest simulation models for direct and indirect ophthalmoscopy as a learning tool in the medical field.

METHODS

The present review was conducted using four national and international databases - PubMed, Scielo, Medline and Cochrane. Initial set of articles was screened based on title and abstracts, followed by full text analysis. It comprises of articles that were published in the past fifteen years (2002-2017).

RESULTS

Eighty-three articles concerning simulation models for medical education were found in national and international databases, with only a few describing important aspects of ophthalmoscopy training and current application of simulation in medical education. After secondary analysis, 38 articles were included.

CONCLUSION

Different ophthalmoscopy simulation models have been described, but only very few studies appraise the effectiveness of each individual model. Comparison studies are still required to determine best approaches for medical education and skill enhancement through simulation models, applied to both medical students as well as young ophthalmologists in training.

The Influence of Mexican Hat Recurrent Connectivity on Noise Correlations and Stimulus Encoding

Noise correlations are a common feature of neural responses and have been observed in many cortical areas across different species. These correlations can influence information processing by enhancing or diminishing the quality of the neural code, but the origin of these correlations is still a matter of controversy. In this computational study we explore the hypothesis that noise correlations are the result of local recurrent excitatory and inhibitory connections. We simulated two-dimensional networks of adaptive spiking neurons with local connection patterns following Gaussian kernels. Noise correlations decay with distance between neurons but are only observed if the range of excitatory connections is smaller than the range of inhibitory connections (“Mexican hat” connectivity) and if the connection strengths are sufficiently strong. These correlations arise from a moving blob-like structure of evoked activity, which is absent if inhibitory interactions have a smaller range (“inverse Mexican hat” connectivity). Spatially structured external inputs fixate these blobs to certain locations and thus effectively reduce noise correlations. We further investigated the influence of these network configurations on stimulus encoding. On the one hand, the observed correlations diminish information about a stimulus encoded by a network. On the other hand, correlated activity allows for more precise encoding of stimulus information if the decoder has only access to a limited amount of neurons.

Resource Estimations in Contingency Planning for Foot-and-Mouth Disease

Preparedness planning for a veterinary crisis is important to be fast and effective in the eradication of disease. For countries with a large export of animals and animal products, each extra day in an epidemic will cost millions of Euros due to the closure of export markets. This is important for the Danish husbandry industry, especially the swine industry, which had an export of €4.4 billion in 2012. The purposes of this project were to (1) develop an iterative tool with the aim of estimating the resources needed during an outbreak of foot-and-mouth disease (FMD) in Denmark, (2) identify areas, which can delay the control of the disease. The tool developed should easily be updated, when knowledge is gained from other veterinary crises or during an outbreak of FMD. The stochastic simulation model DTU-DADS was used to simulate spread of FMD in Denmark. For each task occurring during an epidemic of FMD, the time and personnel needed per herd was estimated by a working group with expertise in contingency and crisis management. By combining this information, an iterative model was created to calculate the needed personnel on a daily basis during the epidemic. The needed personnel was predicted to peak within the first week with a requirement of approximately 123 (65–175) veterinarians, 33 (23–64) technicians, and 36 (26–49) administrative staff on day 2, while the personnel needed in the Danish Emergency Management Agency (responsible for the hygiene barrier and initial cleaning and disinfection of the farm) was predicted to be 174 (58–464), mostly recruits. The time needed for surveillance visits was predicted to be the most influential factor in the calculations. Based on results from a stochastic simulation model, it was possible to create an iterative model to estimate the requirements for personnel during an FMD outbreak in Denmark. The model can easily be adjusted, when new information on resources appears from management of other crisis or from new model runs.

Long time horizon for adaptive management to reveal predation effects in a salmon fishery

Predator-prey interactions shape ecosystem structure and function, potentially limiting the productivity of valuable species. Simultaneously, stochastic environmental forcing affects species productivity, often through unknown mechanisms. The interacting effects of trophic and environmental conditions complicate management of exploited ecosystems and have motivated calls for more holistic management via ecosystem-based approaches, yet the limitations to these approaches are not widely appreciated. The Chignik salmon fishery in Alaska is managed to achieve maximum sustainable yield for sockeye salmon, though research suggests that predation by less economically valuable, and thus not targeted, coho salmon during juvenile rearing limits the productivity of sockeye salmon. We examined the relationship between historical sockeye salmon recruitment and coho salmon abundance observed in the Chignik system and could not detect a clear effect of coho salmon abundance on sockeye salmon productivity, given existing data. Using simulation models, we examined the probability of detecting a known predation effect on sockeye salmon recruitment in the presence of observation error in coho salmon abundance and stochasticity in sockeye salmon recruitment. Increased recruitment stochasticity reduced the ability to detect predator effects in recruitment, an effect further strengthened when low frequency environmental variation was added to the system. Further, increased observation error biased estimates of predator effects towards zero. Thus, in systems with high observation error on predator abundances, estimates of predation effects will be substantially weaker than true effects. We examined the effects of stochasticity on the ability of an adaptive management program to learn about ecosystem structure and detect an effect of management actions intended to release a prey species from its predators. Simulation models revealed that even under scenarios of large predation effects on sockeye salmon, stochastic recruitment masked detection of an effect of increased coho salmon harvest for nearly a decade. These results highlight the challenges inherent in ecosystem-based management of predator-prey systems due to mismatched timescales of ecosystem dynamics and the willingness of stakeholders to risk losses in order to test uncertain hypotheses. It is critical for stakeholders considering EBFM (ecosystem-based fisheries management) and adaptive management strategies to be aware of the potential timelines of perceiving ecosystem change.

Early Decision Indicators for Foot-and-Mouth Disease Outbreaks in Non-Endemic Countries

Disease managers face many challenges when deciding on the most effective control strategy to manage an outbreak of foot-and-mouth disease (FMD). Decisions have to be made under conditions of uncertainty and where the situation is continually evolving. In addition, resources for control are often limited. A modeling study was carried out to identify characteristics measurable during the early phase of a FMD outbreak that might be useful as predictors of the total number of infected places, outbreak duration, and the total area under control (AUC). The study involved two modeling platforms in two countries (Australia and New Zealand) and encompassed a large number of incursion scenarios. Linear regression, classification and regression tree, and boosted regression tree analyses were used to quantify the predictive value of a set of parameters on three outcome variables of interest: the total number of infected places, outbreak duration, and the total AUC. The number of infected premises (IPs), number of pending culls, AUC, estimated dissemination ratio, and cattle density around the index herd at days 7, 14, and 21 following first detection were associated with each of the outcome variables. Regression models for the size of the AUC had the highest predictive value (R² = 0.51-0.9) followed by the number of IPs (R² = 0.3-0.75) and outbreak duration (R² = 0.28-0.57). Predictability improved at later time points in the outbreak. Predictive regression models using various cut-points at day 14 to define small and large outbreaks had positive predictive values of 0.85-0.98 and negative predictive values of 0.52-0.91, with 79-97% of outbreaks correctly classified. On the strict assumption that each of the simulation models used in this study provide a realistic indication of the spread of FMD in animal populations. Our conclusion is that relatively simple metrics available early in a control program can be used to indicate the likely magnitude of an FMD outbreak under Australian and New Zealand conditions.

Predicting the Burden of Revision Knee Arthroplasty: Simulation of a 20-Year Horizon

OBJECTIVES

To estimate future utilization scenarios for knee arthroplasty (KA) revision in the Spanish National Health System in the short- and long-term and their impact on primary KA utilization.

METHODS

A discrete-event simulation model was built to represent KA utilization for 20 years (2012-2031) in the Spanish National Health System. Data on KA utilization from 1997 to 2011 were obtained from the minimum data set. Three scenarios of future utilization of primary KA (1, fixed number since 2011; 2, fixed age- and sex-adjusted rates since 2011; and 3, projection using a linear regression model) were combined with two prosthesis survival functions (W [worse survival], from a study including primary KA from 1995 to 2000; and B [better survival], from the Catalan Registry of Arthroplasty, including primary KA from 2005 to 2013). The simulation results were analyzed in the short-term (2015) and the long-term (2030).

RESULTS

Variations in the number of revisions depended on both the primary utilization rate and the survival function applied, ranging from increases of 8.3% to 31.6% in the short- term and from 38.3% to 176.9% in the long-term, corresponding to scenarios 1-B and 3-W, respectively. The prediction of increases in overall surgeries ranged from 0.1% to 22.3% in the short-term and from 3.7% to 98.2% in the long-term.

CONCLUSIONS

Projections of the burden of KA provide a quantitative basis for future policy decisions on the concentration of high-complexity procedures, the number of orthopedic surgeons required to perform these procedures, and the resources needed.

A Dynamic Network Model to Explain the Development of Excellent Human Performance

Across different domains, from sports to science, some individuals accomplish excellent levels of performance. For over 150 years, researchers have debated the roles of specific nature and nurture components to develop excellence. In this article, we argue that the key to excellence does not reside in specific underlying components, but rather in the ongoing interactions among the components. We propose that excellence emerges out of dynamic networks consisting of idiosyncratic mixtures of interacting components such as genetic endowment, motivation, practice, and coaching. Using computer simulations we demonstrate that the dynamic network model accurately predicts typical properties of excellence reported in the literature, such as the idiosyncratic developmental trajectories leading to excellence and the highly skewed distributions of productivity present in virtually any achievement domain. Based on this novel theoretical perspective on excellent human performance, this article concludes by suggesting policy implications and directions for future research.

The benefits from complying with the framework convention on tobacco control: a SimSmoke analysis of 15 European nations

INTRODUCTION

This article compares the predicted impact of tobacco tax increases alone and as part of a comprehensive tobacco control strategy on smoking prevalence and smoking-attributable deaths (SADs) across 15 European countries.

METHODS

Country-specific population, smoking prevalence and policy data with modified parameter values have been applied to the previously validated SimSmoke model for 10 high-income and 5 middle-income European nations. The impact of past and potential future policies is modelled.

RESULTS

Models generally validated well across the 15 countries, and showed the impact of past policies. Without stronger future policies, 44 million lives would be lost due to smoking across the 15 study countries between 2011 and 2040, but effective policies could avert 7.7 million of those premature deaths.

CONCLUSIONS

Results suggest that past policies have been effective in reducing smoking rates, but there is also a strong potential for future policies consistent with the Framework Convention on Tobacco Control. When specific taxes are increased to 70% of retail price, strong smoke-free air laws, youth access laws and marketing restrictions are enforced, stronger health warnings are implemented, and cessation treatment and media campaigns are supported, smoking prevalence and SADs will fall substantially in European countries.

Symbiotic specificity, association patterns, and function determine community responses to global changes: defining critical research areas for coral-Symbiodinium symbioses

Climate change-driven stressors threaten the persistence of coral reefs worldwide. Symbiotic relationships between scleractinian corals and photosynthetic endosymbionts (genus Symbiodinium) are the foundation of reef ecosystems, and these associations are differentially impacted by stress. Here, we couple empirical data from the coral reefs of Moorea, French Polynesia, and a network theoretic modeling approach to evaluate how patterns in coral-Symbiodinium associations influence community stability under climate change. To introduce the effect of climate perturbations, we simulate local 'extinctions' that represent either the loss of coral species or the ability to engage in symbiotic interactions. Community stability is measured by determining the duration and number of species that persist through the simulated extinctions. Our results suggest that four factors greatly increase coral-Symbiodinium community stability in response to global changes: (i) the survival of generalist hosts and symbionts maximizes potential symbiotic unions; (ii) elevated symbiont diversity provides redundant or complementary symbiotic functions; (iii) compatible symbiotic assemblages create the potential for local recolonization; and (iv) the persistence of certain traits associate with symbiotic diversity and redundancy. Symbiodinium may facilitate coral persistence through novel environmental regimes, but this capacity is mediated by symbiotic specificity, association patterns, and the functional performance of the symbionts. Our model-based approach identifies general trends and testable hypotheses in coral-Symbiodinium community responses. Future studies should consider similar methods when community size and/or environmental complexity preclude experimental approaches.

Susceptibility to emotional contagion for negative emotions improves detection of smile authenticity

A smile is a context-dependent emotional expression. A smiling face can signal the experience of enjoyable emotions, but people can also smile to convince another person that enjoyment is occurring when it is not. For this reason, the ability to discriminate between felt and faked enjoyment expressions is a crucial social skill. Despite its importance, adults show remarkable individual variation in this ability. Revealing the factors responsible for these huge individual differences is a key challenge in this domain. Here we investigated, on a large sample of participants, whether individual differences in smile authenticity recognition are accounted for by differences in the predisposition to experience other people's emotions, i.e., by susceptibility to emotional contagion. Results showed that susceptibility to emotional contagion for negative emotions increased smile authenticity detection, while susceptibility to emotional contagion for positive emotions worsened detection performance, because it leaded to categorize most of the faked smiles as sincere. These findings suggest that susceptibility to emotional contagion plays a key role in complex emotion recognition, and point out the importance of analyzing the tendency to experience other people's positive and negative emotions as separate abilities.

Simulation models of obesity: a review of the literature and implications for research and policy

Simulation models (SMs) combine information from a variety of sources to provide a useful tool for examining how the effects of obesity unfold over time and impact population health. SMs can aid in the understanding of the complex interaction of the drivers of diet and activity and their relation to health outcomes. As emphasized in a recently released report of the Institute or Medicine, SMs can be especially useful for considering the potential impact of an array of policies that will be required to tackle the obesity problem. The purpose of this paper is to present an overview of existing SMs for obesity. First, a background section introduces the different types of models, explains how models are constructed, shows the utility of SMs and discusses their strengths and weaknesses. Using these typologies, we then briefly review extant obesity SMs. We categorize these models according to their focus: health and economic outcomes, trends in obesity as a function of past trends, physiologically based behavioural models, environmental contributors to obesity and policy interventions. Finally, we suggest directions for future research.

Crop responses to climatic variation

The yield and quality of food crops is central to the well being of humans and is directly affected by climate and weather. Initial studies of climate change on crops focussed on effects of increased carbon dioxide (CO2) level and/or global mean temperature and/or rainfall and nutrition on crop production. However, crops can respond nonlinearly to changes in their growing conditions, exhibit threshold responses and are subject to combinations of stress factors that affect their growth, development and yield. Thus, climate variability and changes in the frequency of extreme events are important for yield, its stability and quality. In this context, threshold temperatures for crop processes are found not to differ greatly for different crops and are important to define for the major food crops, to assist climate modellers predict the occurrence of crop critical temperatures and their temporal resolution. This paper demonstrates the impacts of climate variability for crop production in a number of crops. Increasing temperature and precipitation variability increases the risks to yield, as shown via computer simulation and experimental studies. The issue of food quality has not been given sufficient importance when assessing the impact of climate change for food and this is addressed. Using simulation models of wheat, the concentration of grain protein is shown to respond to changes in the mean and variability of temperature and precipitation events. The paper concludes with discussion of adaptation possibilities for crops in response to drought and argues that characters that enable better exploration of the soil and slower leaf canopy expansion could lead to crop higher transpiration efficiency.