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.

Solid waste generation prediction model framework using socioeconomic and demographic factors with real-time MSW collection data.

This article proposes a framework for developing predictive models of end-of-life product flows, highlighting the importance of conducting thorough analyses before developing waste management and end-of-life product flow strategies. The framework emphasizes the importance of recognizing the nature and quality of the available data and finding a balance between model development time and detail requirements. It is designed to adapt to source material heterogeneity and address varying data availability scenarios, such as the presence or absence of radio frequency identification chips. A case study for the city of Gatineau is presented, showcasing the framework's application through agent-based simulation models in a geographic information systems environment. The study focuses on creating models of municipal solid waste generation based on socioeconomic and demographic factors and collection data to accurately predict the quantity and quality of waste streams, enabling municipalities to assess the environmental impact of their waste management strategies.

The spectral underpinnings of pathogen spread on animal networks.

Predicting what factors promote or protect populations from infectious disease is a fundamental epidemiological challenge. Social networks, where nodes represent hosts and edges represent direct or indirect contacts between them, are important in quantifying these aspects of infectious disease dynamics. However, how network structure and epidemic parameters interact in empirical networks to promote or protect animal populations from infectious disease remains a challenge. Here we draw on advances in spectral graph theory and machine learning to build predictive models of pathogen spread on a large collection of empirical networks from across the animal kingdom. We show that the spectral features of an animal network are powerful predictors of pathogen spread for a variety of hosts and pathogens and can be a valuable proxy for the vulnerability of animal networks to pathogen spread. We validate our findings using interpretable machine learning techniques and provide a flexible web application for animal health practitioners to assess the vulnerability of a particular network to pathogen spread.

Testing hypotheses on the calcification in scleractinian corals using a spatio-temporal model that shows a high degree of robustness.

Calcification in photosynthetic scleractinian corals is a complicated process that involves many different biological, chemical, and physical sub-processes that happen within and around the coral tissue. Identifying and quantifying the role of separate processes in vivo or in vitro is difficult or not possible. A computational model can facilitate this research by simulating the sub-processes independently. This study presents a spatio-temporal model of the calcification physiology, which is based on processes that are considered essential for calcification: respiration, photosynthesis, Ca2＋-ATPase, carbonic anhydrase. The model is used to test different hypotheses considering ion transport across the calicoblastic cells and Light Enhanced Calcification (LEC). It is also used to quantify the effect of ocean acidification (OA) on the Extracellular Calcifying Medium (ECM) and ATP-consumption of Ca2＋-ATPase. It was able to reproduce the experimental data of three separate studies and finds that paracellular transport plays a minor role compared to transcellular transport. In the model, LEC results from increased Ca2＋-ATPase activity in combination with increased metabolism. Implementing OA increases the concentration of CO2 throughout the entire tissue, thereby increasing the availability of CO3- in the ECM. As a result, the model finds that calcification becomes more energy-demanding and the calcification rate increases.

Ecotoxicological risk assessment of 14 pesticides and corresponding metabolites to groundwater and soil organisms using China-PEARL model and RQ approach.

Global use of pesticides brings uncertain risks to human and nontarget species via environmental matrix. Currently, various models for exposure risk assessment are developed and widely used to forecast the impact of pesticides on environmental organisms. In this study, five commonly used insecticides, seven herbicides and three fungicides were chosen to analyze the subsequent risks in groundwater in simulated scenarios using China-PEARL (Pesticide Emission Assessment at Regional and Local Scales) model. In addition, their exposure risks to soil organisms were characterized based on risk quotient (RQ) approach. The results indicated that 23.3% of the total 528 predicted environmental concentrations (PECs) of pesticides and respective metabolites in groundwater from six Chinese simulated locations with ten crops were above 10 µg L-1. Furthermore, acceptable human risks of pesticides in groundwater were observed for all simulation scenarios (RQ < 1). Based on the derived PECs in soil short-term and long-term exposure simulation scenarios, all compounds were evaluated to be with acceptable risks to soil organisms, except that imidacloprid was estimated to be with unacceptable chronic risk (RQ = 27.5) to earthworms. Overall, the present findings provide an opportunity for a more-comprehensive understanding of exposure toxicity risks of pesticides leaching into groundwater and soil.

A Systematic Review of Simulation Models to Track and Address the Opioid Crisis.

The opioid overdose crisis is driven by an intersecting set of social, structural, and economic forces. Simulation models are a tool to help us understand and address thiscomplex, dynamic, and nonlinear social phenomenon. We conducted a systematic review of the literature on simulation models of opioid use and overdose up to September 2019. We extracted modeling types, target populations, interventions, and findings; created a database of model parameters used for model calibration; and evaluated study transparency and reproducibility. Of the 1,398 articles screened, we identified 88 eligible articles. The most frequent types of models were compartmental (36%), Markov (20%), system dynamics (16%), and agent-based models (16%). Intervention cost-effectiveness was evaluated in 40% of the studies, and 39% focused on services for people with opioid use disorder (OUD). In 61% of the eligible articles, authors discussed calibrating their models to empirical data, and in 31%, validation approaches used in the modeling process were discussed. From the 63 studies that provided model parameters, we extracted the data sources on opioid use, OUD, OUD treatment, cessation or relapse, emergency medical services, and death parameters. From this database, potential model inputs can be identified and models can be compared with prior work. Simulation models should be used to tackle key methodological challenges, including the potential for bias in the choice of parameter inputs, investment in model calibration and validation, and transparency in the assumptions and mechanics of simulation models to facilitate reproducibility.

Dynamic QTL-based ecophysiological models to predict phenotype from genotype and environment data.

BACKGROUND: Predicting the phenotype from the genotype is one of the major contemporary challenges in biology. This challenge is greater in plants because their development occurs mostly post-embryonically under diurnal and seasonal environmental fluctuations. Most current crop simulation models are physiology-based models capable of capturing environmental fluctuations but cannot adequately capture genotypic effects because they were not constructed within a genetics framework. RESULTS: We describe the construction of a mixed-effects dynamic model to predict time-to-flowering in the common bean (Phaseolus vulgaris L.). This prediction model applies the developmental approach used by traditional crop simulation models, uses direct observational data, and captures the Genotype, Environment, and Genotype-by-Environment effects to predict progress towards time-to-flowering in real time. Comparisons to a traditional crop simulation model and to a previously developed static model shows the advantages of the new dynamic model. CONCLUSIONS: The dynamic model can be applied to other species and to different plant processes. These types of models can, in modular form, gradually replace plant processes in existing crop models as has been implemented in BeanGro, a crop simulation model within the DSSAT Cropping Systems Model. Gene-based dynamic models can accelerate precision breeding of diverse crop species, particularly with the prospects of climate change. Finally, a gene-based simulation model can assist policy decision makers in matters pertaining to prediction of food supplies.

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.

Acid content and buffer-capacity: a charge-balance perspective.

Rational treatment and thorough diagnostic classification of acid-base disorders requires quantitative understanding of the mechanisms that generate and dissipate loads of acid and base. A natural precondition for this tallying is the ability to quantify the acid content in any specified fluid. Physical chemistry defines the pH-dependent charge on any buffer species, and also on strong ions on which, by definition, the charge is pH-invariant. Based, then, on the requirement of electroneutrality and conservation of mass, it was shown in 1914 that pH can be calculated and understood on the basis of the chemical composition of any fluid. Herein we first show that this specification for [H+] of the charge-balance model directly delivers the pH-dependent buffer-capacity as defined in the literature. Next, we show how the notion of acid transport as proposed in experimental physiology can be understood as a change in strong ion difference, ΔSID. Finally, based on Brønsted-Lowry theory we demonstrate that by defining the acid content as titratable acidity, this is equal to SIDref - SID, where SIDref is SID at pH 7.4. Thereby, any chemical situation is represented as a curve in a novel diagram with titratable acidity = SIDref - SID as a function of pH. For any specification of buffer chemistry, therefore, the change in acid content in the fluid is path invariant. Since constituents of SID and titratable acidity are additive, we thereby, based on first principles, have defined a new framework for modeling acid balance across a cell, a whole organ, or the whole-body.

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.

Two Models for Time-Domain Simulation of Hybrid Magnetic Bearing's Characteristics.

A comparison of two developed simulation models for a hybrid magnetic bearing (HMB) transient states is presented. This applies to analyses using the flux-circuit directly coupled magnetic equivalent circuit and field-circuit indirectly coupled finite element analysis. The required control system was implemented for both models. The results obtained from the simulations were compared with those obtained from measurement tests.

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.

Utilizing In-Hospital Fabrication to Decrease Simulation Costs.

BACKGROUND: Two restrictive factors for surgical training through simulation, are the cost of and accessibility to materials and consoles for simulation models. Commercial surgical simulation models continue to maintain high prices with a wide range of fidelity levels. We believe that by utilizing in-house fabrication, these barriers can be decreased while maintaining and even improving the functionality of surgical simulation models as well as increase their individualization and customization. METHODS: By using a combination of digital and manual fabrication techniques such as 3D printing and basic mold making methods, we were able to create models equivalent to current commercial products by utilizing the first of its kind MakerHEALTH space and collaborating with our surgical simulation staff. We then compared our research and development, start-up, materials, operational, and labor costs to buying comparable commercial models with the simulation usage rates of our institution. RESULTS: We were able to decrease the costs of a 6 model simulation sample set (appendectomy, cholecystectomy, common bile duct exploration, ventral hernia, chest tube insertion, and suture pads) at our institution from $99,646.60 to $13,817.21 for a medical student laborer, $14,500.56 for a surgical resident laborer, $15,321.08 for a simulation staff laborer, and $18,984.48 for an attending physician laborer. CONCLUSION: We describe successful approaches for the creation of cost-effective and modular simulation models with the aim of decreasing the barriers to entry and improving surgical training and skills. These techniques make it financially feasible for learners to train during larger faculty-led workshops and on an individual basis, allowing for access to simulation at any time or place.

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.

A review of simulation models for the long-term management of type 2 diabetes in low-and-middle income countries.

INTRODUCTION: The burden of type 2 diabetes is steadily increasing in low-and-middle-income countries, thereby posing a major threat from both a treatment, and funding standpoint. Although simulation modelling is generally relied upon for evaluating long-term costs and consequences associated with diabetes interventions, no recent article has reviewed the characteristics and capabilities of available models used in low-and-middle-income countries. We review the use of computer simulation modelling for the management of type 2 diabetes in low-and-middle-income countries. METHODS: A search for studies reporting computer simulation models of the natural history of individuals with type 2 diabetes and/or decision models to evaluate the impact of treatment strategies on these populations was conducted in PubMed. Data were extracted following Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines and assessed using modelling checklists. Publications before the year 2000, from high-income countries, studies involving animals and analyses that did not use mathematical simulations were excluded. The full text of eligible articles was sourced and information about the intervention and population being modelled, type of modelling approach and the model structure was extracted. RESULTS: Of the 79 articles suitable for full text review, 44 studies met the inclusion criteria. All were cost-effectiveness/utility studies with the majority being from the East Asia and Pacific region (n = 29). Of the included studies, 34 (77.3%) evaluated the cost-effectiveness of pharmacological interventions and approximately 75% of all included studies used HbA1c as one of the treatment effects of the intervention. 32 (73%) of the publications were microsimulation models, and 29 (66%) were state-transition models. Most of the studies utilised annual cycles (n = 29, 71%), and accounted for costs and outcomes over 20 years or more (n = 38, 86.4%). CONCLUSIONS: While the use of simulation modelling in the management of type 2 diabetes has been steadily increasing in low-and-middle-income countries, there is an urgent need to invest in evaluating therapeutic and policy interventions related to type 2 diabetes in low-and-middle-income countries through simulation modelling, especially with local research data. Moreover, it is important to improve transparency and credibility in the reporting of input data underlying model-based economic analyses, and studies.

On-farm assessment of eucalypt yield gaps - a case study for the producing areas of the state of Minas Gerais, Brazil.

The concept of yield gaps provides a basis for identifying the main sources of production losses, caused by water or management deficiencies, which may help foresters and forest companies to better plan and make decisions in their areas. Thus, the aim of this study was to identify the magnitude and the major causes of yield gaps of eucalypts, being this the most planted forest genus in Brazil, in different producing regions of the state of Minas Gerais that has the largest planted area. To these ends, potential (Yp) and attainable (Ya) yields were simulated using the agroecological zone model (AEZ-FAO) adapted and calibrated for Brazilian eucalypt clones. Actual yield (Yr) data were obtained from 22 sites located in the state of Minas Gerais from 2009 to 2016, considering an average forest rotation of 6.7 years and plantings occurring between 2002 and 2010. From this, the total yield gap (YGtot), yield gap by water deficit (YGwd), and yield gap by sub-optimal management (YGman) were determined. The YGwd ranged from 37 to 69 m3 ha-1 year-1 across the 22 sites assessed, with an average value of 55 m3 ha-1 year-1. On the other hand, the YGman ranged from zero (optimal management) to 31 m3 ha-1 year-1. The eucalypt yield gap in commercial areas of Minas Gerais state was mainly caused by water deficit, which represented 77% of the total yield gap. On the other hand, the deficiencies in forest management accounted for 23% of the total yield gap.

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.

Modeling growth, development and yield of cassava: A review.

Cassava is an important crop in the developing world. The goal of this study was to review published cassava models (18) for their capability to simulate storage root biomass and to categorize them into static and dynamic models. The majority (14) are dynamic and capture within season growth dynamics. Most (13) of the dynamic models consider environmental factors such as temperature, solar radiation, soil water and nutrient restrictions. More than half (10) have been calibrated for a distinct genotype. Only one of the four static models includes environmental variables. While the static regression models are useful to estimate final yield, their application is limited to the locations or varieties used for their development unless recalibrated for distinct conditions. Dynamic models simulate growth process and provide estimates of yield over time with, in most cases, no fixed maturity date. The dynamic models that simulate the detailed development of nodal units tend to be less accurate in determining final yield compared to the simpler dynamic and statistic models. However, they can be more safely applied to novel environmental conditions that can be explored in silico. Deficiencies in the current models are highlighted including suggestions on how they can be improved. None of the current dynamic cassava models adequately simulates the starch content of fresh cassava roots with almost all models based on dry biomass simulations. Further studies are necessary to develop a new module for existing cassava models to simulate cassava quality.

Design and Validation of a Simulator for Feline Cephalic Vein Cannulation-A Pilot Study.

In recent years there has been an increased use of alternative methods for teaching veterinary clinical skills, since ethical considerations preclude the use of live animals for demonstration or practice of many procedures. Skills training on cats (i.e., feline venipuncture) is a particularly challenging area. This study aimed to develop a simulator for cephalic venipuncture in cats and to validate this simulator using questionnaires answered by undergraduate students and experienced veterinarians. The simulator was developed to provide an experience that was close to reality, including an artificial blood system that flows through the catheter when venipuncture is correctly performed, while at the same time using simple methodology and accessible materials so that it could be reproduced in other universities. The experienced vets agreed (44.4%) or strongly agreed (55.6%) that the simulator was good for venipuncture training, and the most useful feature was the experience of catheter manipulation and fixation on the cat's limb. All the students agreed that the practical class with the simulator was important for learning this skill. Both groups (students and experienced veterinarians) unanimously agreed that it is important to train using a simulator before trying the procedure on a live cat. This simulator offers undergraduate students an alternative way to learn and practice venipuncture in cats helping to reduce the use of live animals in practical classes.

Revealing photon transmission in an ultraviolet reactor: Advanced approaches for measuring fluence rate distribution in water for model validation.

Fluence rate (FR) distribution (optical field) is of great significance in the optimal design of ultraviolet (UV) reactors for disinfection or oxidation processes in water treatment. Since the 1970s, various simulation models have been developed, which can be combined with computational fluidic dynamic software to calculate the fluence delivered in a UV reactor. These models strive for experimental validation and further improvement, which is a major challenge for UV technology in water treatment. Herein, a review of the simulation models of the FR distribution in a UV reactor and the applications of the current main experimental measurement approaches including conventional flat-type UV detector, spherical actinometer, and micro-fluorescent silica detector (MFSD), is presented. Moreover, FR distributions in a UV reactor are compared between various simulation models and MFSD measurements. In addition, the main influential factors on the FR distribution, including inner-wall reflection, refraction and shadowing effects of adjacent lamps, and turbidity effect are discussed, which is helpful for improving the accuracy of the simulation models and avoiding dark regions in the reactor design. This paper provides an overview on the simulation models and measurement approaches for the FR distribution, which is helpful for the model selection in fluence calculations and gives high confidence on the optimal design of UV reactors in regard to present methods.