Uncertainty In Model Inputs Research Articles

Enhancing Outcomes in Opioid Use Disorder Treatment: An Economic Evaluation of Improving Medication Adherence for Buprenorphine Through Blister-Packaging.

The opioid epidemic has severely impacted the US over the last 15 years. Buprenorphine is a partial opioid agonist indicated for the treatment of opioid use disorder (OUD) and is recognized as an effective treatment when taken as prescribed. However, adherence rates have been low in real-world settings. Blister-packaging has been shown to promote medication adherence across a variety of disease states, although it has never been studied in OUD. An economic analysis was conducted to assess the impact of increased adherence of blister-packaged buprenorphine on health care resource utilization (HCRU) and health care costs for 10,000 patients initiating therapy for OUD. The model analyzed a commercially insured population within the US over a one-year time horizon. Medication adherence was defined in the model as proportion of days covered (PDC) of at least 80%. Literature-based references were used to inform both the impact of blister-packaging on the number of patients who became adherent as well as the impact of medication adherence on HCRU and health care costs. Model input uncertainty was assessed in one-way sensitivity analyses. With the implementation of blister-packaging buprenorphine, adherence rates increased from 37.1% of patients in the pre-intervention period to 45.3%, resulting in an additional 818 patients becoming adherent post-intervention. The increase in adherence led to a reduction of medical costs of $12,138,757 (-$1,214 per-patient (PP)). Specifically, inpatient costs decreased by $7,127,073 (-$713 PP) while outpatient costs decreased by $5,013,319 (-$501 PP). Pharmacy costs increased by $3,432,705 ($343 PP). Despite the increase in pharmacy costs, total health care costs saw a reduction of $8,559,684 (-$856 PP). Blister-packaging buprenorphine for treatment of OUD has potential to improve medication adherence and health outcomes while reducing HCRU and health care costs. Future studies are necessary to assess the real-world application and impact of blister-packaging buprenorphine for OUD across various patient populations and health care settings.

Confidence-based design optimization using multivariate kernel density estimation under insufficient input data

The uncertainty quantification of the input statistical model in reliability-based design optimization (RBDO) has been widely investigated for accurate reliability analysis, and it could be estimated through its characteristics, cumulative experiences, and available data. However, uncertainty quantification of random variables in existing RBDO studies has exploited parametric distributions quantifying the uncertainty through the Bayes' theorem. In addition, a correlation between random variables is often underestimated due to a lack of knowledge and difficulty to describe the high-dimensional correlation. Hence, it has been a challenge to properly quantify input statistical model and its uncertainty. Therefore, a multivariate kernel density estimation (KDE) is employed to perform data-driven confidence-based design optimization (CBDO) for effective quantification of input model uncertainty. Any assumption on input distribution is not necessary since it is established only with the given input data. Moreover, the input model uncertainty due to insufficient data is quantified using bootstrapping and optimal adaptive bandwidth matrices through the Bayes’ theorem using cross-validation error. Consequently, the proposed CBDO with given input data is capable of finding a conservative optimum of RBDO accounting for both aleatory uncertainty of random variables and epistemic uncertainty induced by a limited number of input data through the multivariate KDE.

Distributed avoidance-accommodating flexible performance approach for adaptive formation tracking of underactuated surface vehicles against moving obstacles

A novel strategy for formation tracking of multiple uncertain underactuated surface vehicles is introduced in this study, to maintain the prescribed formation performance in the presence of moving obstacles with unknown velocities. Unlike the existing methods for prescribed formation performance control, our key contribution is the development of distributed avoidance-accommodating flexible performance functions (FPFs). These functions prevent deviation from the prescribed performance bounds while navigating around moving obstacles, considering input saturation and model uncertainties. We design local adaptive formation controllers by using distributed posture errors and avoidance-accommodating FPFs. Auxiliary signals that facilitate adjustment of the posture errors and performance functions during obstacle avoidance are derived using a recursive design that ensures Lyapunov stability. Additionally, command filters for virtual control laws are redesigned as saturation-compensating filters to solve the input saturation problem. Consequently, the proposed controller ensures the prescribed distributed formation performance, even in scenarios involving moving obstacles and input saturation, a capability lacking in conventional prescribed performance-based formation controllers, which can become unstable in such scenarios. The theoretical findings of this study are validated through comparative simulations.

Fixed-time event-triggered adaptive control of manipulator system with input deadzone and model uncertainty

There exist universal model uncertainty and input deadzone in mechatronic motion plant due to unknown model parameters and actuator physical feature, which will degrade the motion performance and stability. In this study, a fixed-time event-triggered adaptive control is proposed in n-DOF manipulator system to address these problems. Firstly, the dynamic model of n-DOF manipulator with joint friction and input deadzone is constructed by Lagrange technique. Then a radial basis function neural network (RBFNN) is designed to approximate uncertain dynamics. Meanwhile, an event-triggered mechanism (ETM) is used to reduce the control update frequency and to save the communication resource. Furthermore, an adaptive estimation law is adopted to compensate unknown input deadzone parameters. Based on backstepping iteration technique, a fixed-time convergent controller is presented to guarantee the system state errors convergence to zero neighborhood in finite time irrelevant to arbitrary initial state. Finally, the effectiveness of the proposed control scheme is verified by sufficient comparative simulation and experimental results.

The Effect of Model Input Uncertainty on the Simulation of Typical Pollutant Transport in the Coastal Waters of China

Route planning to evade potential pollution holds critical importance for aquaculture vessels. This study establishes a fish-feed pollutant drift model based on the Lagrangian particle tracking algorithm and designs four sets of sensitivity experiments in the East China Sea. The research investigates the impact of model input uncertainties on the drift trajectory, centroid position, and sweeping area of the fish-feed pollutants. Numerical results indicate that the uncertainty in the background flow field significantly affects the uncertainty in the centroid position and sweeping area in the numerical simulations. Specifically, when a 35% random error is added to the background flow field, the centroid shift distance reaches its maximum, and the sweeping area also attains its largest value. The uncertainty in the background wind field affects the centroid position of particles but to a much lesser extent compared to the background flow field. When considering only the uncertainty of the background wind field, the sweeping area does not significantly differ from the control experiment as the uncertainty of the background wind field increases. The initial release position has little effect on the drift direction of the fish-feed pollutants but does affect the drift distance; it has minimal impact on the trajectory but significantly affects the final position of the pollutant centroid. By analyzing the model uncertainties, this study reveals the key factors influencing the drift of fish-feed pollutants. This information is crucial for aquaculture vessels in planning routes, considering environmental factors, and reducing potential pollution risks.

Remote sensing evapotranspiration in ensemble-based framework to enhance cascade routing and re-infiltration concept in integrated hydrological model applied to support decision making

Integrated hydrological models (IHMs) help characterize the complexity of surface–groundwater interactions. The cascade routing and re-infiltration (CRR) concept, recently applied to a MODFLOW 6 IHM, improved conceptualization and simulation of overland flow processes. The CRR controls the transfer of rejected infiltration and groundwater exfiltration from upslope areas to adjacent downslope areas where that water can be evaporated, re-infiltrated back to subsurface, or discharged to streams as direct runoff. The partitioning between these three components is controlled by uncertain parameters that must be estimated. Thus, by quantifying and reducing those uncertainties, next to uncertainties of the other model parameters (e.g. hydraulic and storage parameters), the reliability of the CRR is improved and the IHM is better suited for decision support modelling, the two key objectives of this work. To this end, the remotely sensed MODIS-ET product was incorporated into the calibration process for complementing traditional hydraulic head and streamflow observations. A total of approximately 150,000 observations guided the calibration of a 13-year MODFLOW 6 IHM simulation of the Sardon catchment (Spain) with daily stress periods. The model input uncertainty was represented by grid-cell-scale parameterization, yielding approximately 500,000 unknown input parameters to be conditioned. The calibration was carried out through an iterative ensemble smoother. Incorporating the MODIS-ET data improved the CRR implementation, and reduced uncertainties associated with other model parameters. Additionally, it significantly reduced the uncertainty associated with net recharge, a critical flux for water management that cannot be directly measured and rather is commonly estimated by IHM simulations.

Health economic evaluation of 2-dose and 3-dose rotavirus vaccines in children below 5 years of age in Morocco

ABSTRACT Following the introduction of rotavirus vaccination into the Moroccan National Immunization Program, the prevalence of the disease has decreased by nearly 50%. However, evidence on the economic value of rotavirus vaccinations in Morocco is limited. This health economic analysis evaluated, from both country payer and societal perspectives, the costs and the cost-effectiveness of three rotavirus vaccines using a static, deterministic, population model in children aged < 5 years in Morocco. Included vaccines were HRV (2-dose schedule), HBRV (3-dose schedule) and BRV-PV 1-dose vial (3-dose schedule). One-way and probabilistic sensitivity analyses were conducted to assess the impact of uncertainty in model inputs. The model predicted that vaccination with HRV was estimated to result in fewer rotavirus gastroenteritis events (−194 homecare events, −57 medical visits, −8 hospitalizations) versus the 3-dose vaccines, translating into 7 discounted quality-adjusted life years gained over the model time horizon. HRV was associated with lower costs versus HBRV from both the country payer (−$1.8 M) and societal (−$4.1 M) perspectives, and versus BRV-PV 1-dose vial from the societal perspective (−$187,000), dominating those options in the cost-effectiveness analysis. However, costs of BRV-PV 1-dose vial were lower than HRV from the payer perspective, resulting in an ICER of approximately $328,376 per QALY, above the assumed cost effectiveness threshold of $3,500. Vaccination with a 2-dose schedule of HRV may be a cost-saving option and could lead to better health outcomes for children in Morocco versus 3-dose schedule rotavirus vaccines.

Therapeutic sequencing in inflammatory bowel disease: Determining the optimal position of vedolizumab for long-term Crohn's disease control using real-world evidence.

Several biologics are available for the treatment of moderate to severe Crohn's disease, but data to optimize their use are scarce. Vedolizumab (VDZ) is a gut-selective anti-lymphocyte trafficking monoclonal antibody that was approved in 2014 for the treatment of moderate to severe Crohn's disease. Based on real-world evidence, a model was developed to examine the effect of VDZ's position in the treatment sequence on clinical outcomes. The aim of this study was to develop a model using real-world data to investigate how the positioning of VDZ in a sequence of biologic therapies for CD affects clinical effectiveness outcomes of quality-adjusted life-years (QALYS), patient-reported disease activity, and surgery rates. A semi-Markov sequential model was developed to identify the optimal position of VDZ in a treatment sequence that included corticosteroids (CS), two biologics, and best supportive care (BSC). Using real-world data, three sequences were compared: VDZ as first (position), second, and last biologic (with anti-tumor necrosis factor alpha agents adalimumab (ADA) and infliximab (IFX) and the anti-interleukin-12 and -23 agent ustekinumab (UST) as alternative biologic treatments). Published real-world evidence informed model inputs. Vedolizumab sequences were compared and ranked based on QALYS, patient-reported outcomes from Crohn's disease activity index scores, or proportion of patients undergoing surgery by the 10-year time horizon for model simulation. Sensitivity analyses were used to evaluate the impact of model input uncertainty. Vedolizumab as the first biologic was the optimal position for this treatment according to all criteria, including yielding the highest QALYs (5.09) versus VDZ in second (4.97) and third (4.96) biologic sequence positions in sequences containing CS, anti-TNFα (aggregated data), UST, and BSC; 1780/2000 (89%) probabilistic simulations. In sequences containing ADA, VDZ, and UST biologics, ADA and VDZ in the first-line biologic position yielded QALYs of 5.09 versus 5.07, respectively. Adalimumab as the first biologic was best for clinical remission. This simulation model using real-world evidence indicates that positioning VDZ or ADA as the first biologic is likely to lead to improved long-term patient outcomes when compared to administering these treatments later or starting with IFX monotherapy.

How well do Earth system models reproduce the observed aerosol response to rapid emission reductions? A COVID-19 case study

Abstract. The spring 2020 COVID-19 lockdowns led to a rapid reduction in aerosol and aerosol precursor emissions. These emission reductions provide a unique opportunity for model evaluation and to assess the potential efficacy of future emission control measures. We investigate changes in observed regional aerosol optical depth (AOD) during the COVID-19 lockdowns and use these observed anomalies to evaluate Earth system model simulations forced with COVID-19-like reductions in aerosols and greenhouse gases. Most anthropogenic source regions do not exhibit statistically significant changes in satellite retrievals of total or dust-subtracted AOD, despite the dramatic economic and lifestyle changes associated with the pandemic. Of the regions considered, only India exhibits an AOD anomaly that exceeds internal variability. Earth system models reproduce the observed responses reasonably well over India but initially appear to overestimate the magnitude of response in East China and when averaging over the Northern Hemisphere (0–70∘ N) as a whole. We conduct a series of sensitivity tests to systematically assess the contributions of internal variability, model input uncertainty, and observational sampling to the aerosol signal, and we demonstrate that the discrepancies between observed and simulated AOD can be partially resolved through the use of an updated emission inventory. The discrepancies can also be explained in part by characteristics of the observational datasets. Overall our results suggest that current Earth system models have potential to accurately capture the effects of future emission reductions.

Influence of material parameter variability on the predicted coronary artery biomechanical environment via uncertainty quantification.

Central to the clinical adoption of patient-specific modeling strategies is demonstrating that simulation results are reliable and safe. Indeed, simulation frameworks must be robust to uncertainty in model input(s), and levels of confidence should accompany results. In this study, we applied a coupled uncertainty quantification-finite element (FE) framework to understand the impact of uncertainty in vascular material properties on variability in predicted stresses. Univariate probability distributions were fit to material parameters derived from layer-specific mechanical behavior testing of human coronary tissue. Parameters were assumed to be probabilistically independent, allowing for efficient parameter ensemble sampling. In an idealized coronary artery geometry, a forward FE model for each parameter ensemble was created to predict tissue stresses under physiologic loading. An emulator was constructed within the UncertainSCI software using polynomial chaos techniques, and statistics and sensitivities were directly computed. Results demonstrated that material parameter uncertainty propagates to variability in predicted stresses across the vessel wall, with the largest dispersions in stress within the adventitial layer. Variability in stress was most sensitive to uncertainties in the anisotropic component of the strain energy function. Moreover, unary and binary interactions within the adventitial layer were the main contributors to stress variance, and the leading factor in stress variability was uncertainty in the stress-like material parameter that describes the contribution of the embedded fibers to the overall artery stiffness. Results from a patient-specific coronary model confirmed many of these findings. Collectively, these data highlight the impact of material property variation on uncertainty in predicted artery stresses and present a pipeline to explore and characterize forward model uncertainty in computational biomechanics.

Event-triggered error-constrained switching guidance-based marine surface vessel path-following control and collision avoidance

For marine surface vessel with input saturation, model uncertainties, and unknown disturbances, this article proposes a path-following control and collision avoidance method based on error-constrained event-triggered switching line-of-sight guidance. This method can autonomously switch between path-following and collision avoidance modes, constrain the position and heading errors to satisfy safe navigation in confined waters, and reduce the computational frequency of the controller and the mechanical wear of the actuator. In the path-following mode, we construct an asymmetric modified barrier Lyapunov function to constrain the position errors and use the relative threshold event-triggered mechanism to reduce the update frequency of the guidance heading angle. In the collision avoidance mode, static or dynamic obstacles are avoided through the response of collision avoidance radius. In the heading and velocity control design, asymmetric modified barrier Lyapunov function and event-triggered mechanism are also applied, so that the marine surface vessel can track the guided heading angle and meet the heading error-constrained while reducing the update frequency of the controller. The adaptive auxiliary systems are used to compensate for the input saturation, and radial basis function neural networks and adaptive laws are used to approximate the model uncertainties and composite disturbances. According to the Lyapunov stability theory, all signals are semi-globally uniformly ultimately bounded and the Zeno phenomenon is avoided. Finally, the comparative experiment shows the superiority of the designed control strategy.

BUDGET IMPACT OF MIRIKIZUMAB-MRKZ IN THE TREATMENT OF ADULT PATIENTS WITH MODERATELY-TO-SEVERELY ACTIVE ULCERATIVE COLITIS IN THE UNITED STATES

Abstract BACKGROUND Mirikizumab (miri), an IL-23p19 antagonist, is undergoing US FDA review for treatment of moderately-to-severely active ulcerative colitis (UC) in adults. However, the budget impact of its addition to payer formularies is unknown. METHODS A de novo budget impact model was developed to estimate the cost of introducing miri to the formulary of a hypothetical one-million-member health insurance plan. Epidemiological inputs drew primarily from published sources, while market research informed the current and future market share of advanced therapies. The model accounted for drug acquisition, drug administration, treatment monitoring, and adverse event management, derived from US prescribing information, trial results, and published sources. Discontinuation and dose escalation rates were based on the literature, applying assumptions where data gaps existed. In the base case, the model assumed that half of miri’s market share would be accrued from ustekinumab (an IL-12/IL-23 antagonist). The base-case analysis adopted a US commercial payer perspective over a 3-year period. Deterministic sensitivity analyses (DSA) assessed the implications of uncertainty in model inputs, while scenario analyses estimated budget impact under alternative assumptions about the origin of miri’s market share, and about the patient population under consideration. RESULTS In the base-case, 445, 480, and 515 patients were predicted eligible for advanced therapies for UC in Year1, Year2, and Year3, respectively with total UC drug management expenditures of $137.4 million over three years. Introducing miri to formulary for moderately-to-severely active UC yielded cumulative incremental costs of $1.3 million (relative impact=0.93%), equivalent to $0.04 per member per month (PMPM) or $0.43 per member per year (PMPY). Among patients who had already failed an advanced therapy, miri yielded lower cumulative incremental costs of $0.8 million (relative impact=1.1%), equivalent to $0.02 PMPM/$0.28 PMPY, consistent with the more narrowly defined eligible patient population (219, 252, and 284 patients in Year1, Year2, and Year3, respectively). In the scenario in which miri’s market share derived only from ustekinumab, the model predicted total cost savings of $2.6 million over three years (relative impact=-1.9%), as well as PMPM savings of $0.07 and PMPY savings of $0.85, due to higher estimated average drug costs associated with ustekinumab. DSA results suggested that base-case results were robust to uncertainty in the value of key model parameters. CONCLUSION Introducing miri to formulary for moderately-to-severely active UC may offer patients and clinicians an additional therapeutic option to manage this challenging and debilitating condition at minimal added cost to payers. The addition of miri to formulary could be relatively budget neutral or cost saving in some contexts.

Predictive State Observer-Based Aircraft Distributed Formation Tracking Considering Input Delay and Saturations

This paper investigates a fully distributed time-varying formation tracking problem for a group of fixed-wing aircraft. The fixed-wing aircraft formation control system consists of an outer-loop trajectory control subsystem and an inner-loop attitude control subsystem. For fixed-wing aircraft, it is crucial to consider the time delay of the engine response, the model uncertainties, the tracking capability of the attitude commands in the inner loop, and other agility performances of the aircraft. To address the problems related to the input time delay and model uncertainties, a predictive extended state observer-based fully distributed time-varying formation tracking control (PESO-TVFTC) protocol is proposed. To satisfy the constraints set by the attitude tracking quickness and the trajectory tracking smoothness, the low gain feedback technique is introduced in the protocol to keep the control inputs for the outer loop within the desired saturation constraints. Through theoretical analysis, it is proved that the multiple aircraft systems can achieve time-varying formation tracking consensus under specific initial conditions and feasibility conditions, and it is shown that the upper bounds of the PESO gains are restricted by the time delay. Numerical simulations are used to demonstrate the effectiveness of and the improvements in the proposed method.

Accounting for Machine Learning Prediction Errors in Design

Abstract Machine learning is gaining prominence in mechanical design, offering cost-effective surrogate models to replace computationally expensive models. Nevertheless, concerns persist regarding the accuracy of these models, especially when applied to safety-critical products. To address this challenge, this study investigates methods to account for model prediction errors by incorporating epistemic uncertainty within surrogate models while managing aleatory uncertainty in input variables. The paper clarifies key aspects of modeling coupled epistemic and aleatory uncertainty when using surrogate models derived from noise-free training data. Specifically, the study concentrates on quantifying the impacts of coupled uncertainty in mechanical design through the development of numerical methods based on the concept of the most probable point. This method is particularly relevant for mechanical component design, where failure prevention holds paramount importance, and the probability of failure is low. It is applicable to design problems characterized by probability distributions governing aleatory and epistemic uncertainties in model inputs and predictions. The proposed method is demonstrated using shaft and beam designs as two illustrative examples. The results demonstrate the method's effectiveness in quantifying and mitigating the influence of coupled uncertainty in the design process.

Barrier Lyapunov function-based dual event-triggered prescribed performance path following control for marine surface vessel under input saturation

Concentrated on the marine surface vessel (MSV) with input saturation, model uncertainties and unknown disturbances, this paper proposes the error-constrained line-of-sight guidance-based prescribed performance dual event-triggered path following control (ECLOS-PPDETPFC) strategy which applies the dual relative threshold event-triggered mechanism (ETM) to both guidance and controller of the MSV path following. We apply the prescribed performance function (PPF) as the bounds of asymmetric modified barrier Lyapunov function (AMBLF) to construct the prescribed performance asymmetric modified barrier Lyapunov function (PPAMBLF). In guidance, event-triggered error-constrained line-of-sight (ETECLOS) is constructed by relative threshold ETM and AMBLF to reduce the calculation pressure and keep position errors within the constraint range; In controller, prescribed performance event-triggered path following control (PPETPFC) is based on the relative threshold ETM and PPAMBLF, which makes the heading error meet the prescribed performance requirements, while reducing the loss of actuator. At the same time, the adaptive auxiliary systems are used to compensate the influence of input saturation, radial basis function neural networks (RBFNNs) and adaptive laws are used to approximate the model uncertainties and composite disturbances. According to the Lyapunov stability theory, all signals are semi-globally uniformly ultimately bounded (SGUUB) and the Zeno phenomenon is avoided. Finally, the comparative experiment shows the effectiveness and superiority of the designed control strategy.

Performance Evaluation of Nord2000, RTN-96 and CNOSSOS-EU against Noise Measurements in Central Jutland, Denmark

This article aims to assess the performance of Nord2000, RTN-96, and CNOSSOS-EU, the Nordic and European noise prediction standards, in predicting daily LAeq24h and Lden levels (dBA), by comparing them with measurements gathered over 76 days from the E45 motorway in Helsted, Central Jutland, Denmark. In addition, the article investigates the potential viability of utilizing Confidence-Weighting Average (CWA) for data fusion to enhance noise estimation accuracy. The results showed highly positive Spearman’s correlations (RS), reflecting strong agreements between observed and predicted data, Nord2000 = 0.85–0.98, CNOSSOS-EU = 0.79–0.92 and RTN-96 = 0.86–0.91. Model differences, RMSE = 0.4–3.3 dBA (Nord2000), 1.4 = 2.8 dBA (CNOSSOS) and 1.3–4.2 dBA (RTN-96), were mainly due to underlying model parametrization and uncertainties in model inputs. Overall, Nord2000 outperformed CNOSSOS and RTN-96 in reproducing observed noise levels. Moreover, CNOSSOS agreed well with the measured data and exhibited a high potential for noise mapping and health assessments. Likewise, the CWA is found to be a promising, forward-looking data fusion approach to improve noise estimates’ accuracy. More research is required to further evaluate the models in greater detail over a larger geographical area and across varied temporal scales (e.g., hourly, yearly).

Global sensitivity analysis of APSIM-wheat yield predictions to model parameters and inputs

The performance of cropping system models is often limited by uncertainties in model parameters and inputs. This study aims to explore how model yield prediction responds to these uncertainties under various environmental and management conditions. The Sobol’ method was used to investigate the sensitivity of the Agricultural Production Systems SIMulator (APSIM)-Wheat yield prediction to the following factors: air temperature (maximum and minimum), precipitation, initial soil nitrogen content, nitrogen fertilization amount, and soil hydraulic parameters. Eighteen scenarios were defined to consider a combination of three weather conditions (wet, normal, and dry growing season, based on the historical climatology of the Wimmera district of Victoria, Australia), three soil types (sandy, loamy, and clayey soils), and two nitrogen fertilization applications (50 and 100 kg N/ha). Eight thousand APSIM simulations were used to calculate the first order and total sensitivity indices for each scenario. The effects of weather, soil water characteristics and nitrogen availability were estimated by measuring their impacts on sensitivity indices. Our results show that yield was more sensitive either to variables that control water availability (precipitation and soil type) or variables that control nitrogen availability, depending on which was more limiting to wheat growth under the scenario. For example, in case of low nitrogen fertilization scenario, the initial nitrogen content sensitivity ranked first. Variation of this factor contributed 64% to 79% to the variance in simulated yield for clayey soils, nearly four times higher than the second-ranked factor (nitrogen fertilization amount). Soil hydraulic parameters and precipitation were most important when the crop growth was more constrained by water availability than by nitrogen availability. In the case of sandy soils in dry years with high nitrogen fertilization level, soil parameters and precipitation accounted for 83% of the yield variability. Minimum temperature consistently ranked as the least important factor under all scenarios. This work will help researchers better understand the model inputs’ impacts on the simulated yield variability under various soil, management, and weather conditions. The results also support agronomists and practitioners in the optimal use of the APSIM-Wheat model as a management tool.

Economic Evaluation of Rotavirus Vaccination in Children Aged Under Five Years in South Africa.

Evidence on the economic value of rotavirus vaccines in middle-income countries is limited. We aimed to model the implementation of three vaccines (human rotavirus, live, attenuated, oral vaccine [HRV, 2 doses]; rotavirus vaccine, live, oral, pentavalent [HBRV, 3 doses] and rotavirus vaccine, live attenuated oral, freeze-dried [BRV-PV, 3 doses] presented in 1-dose and 2-dose vials) into the South African National Immunisation Programme. Cost and cost-effectiveness analyses were conducted to compare three rotavirus vaccines using a static, deterministic, population model in children aged <5 years in South Africa from country payer and societal perspectives. Deterministic and probabilistic sensitivity analyses were conducted to assess the impact of uncertainty in model inputs. The human rotavirus, live, attenuated, oral vaccine (HRV)was associated with cost savings versus HBRV from both perspectives, and versus BRV-PV 1-dose vial from the societal perspective. In the cost-effectiveness analysis, HRV was estimated to avoid 1,107 home care rotavirus gastroenteritis (RVGE) events, 247 medical visits, 35 hospitalisations, and 4 RVGE-related deaths versus HBRV and BRV-PV. This translated to 73 quality-adjusted life years gained. HRV was associated with lower costs versus HBRV from both payer (-$3.9M) and societal (-$11.5M) perspectives and versus BRV-PV 1-dose vial from the societal perspective (-$3.8M), dominating those options. HRVwas associated with higher costs versus BRV-PV 1-dose vial from the payer perspective and versus BRV-PV 2‑dose vial from both payer and societal perspectives (ICERs: $51,834, $121,171, and $16,717, respectively), exceeding the assumed cost-effectiveness threshold of 0.5 GDP per capita. Vaccination with a 2-dose schedule of HRV may lead to better health outcomes for children in South Africa compared with the 3-dose schedule rotavirus vaccines.

Uncertainty quantification of the effect of cardiac position variability in the inverse problem of electrocardiographic imaging

Objective. Electrocardiographic imaging (ECGI) is a functional imaging modality that consists of two related problems, the forward problem of reconstructing body surface electrical signals given cardiac bioelectric activity, and the inverse problem of reconstructing cardiac bioelectric activity given measured body surface signals. ECGI relies on a model for how the heart generates bioelectric signals which is subject to variability in inputs. The study of how uncertainty in model inputs affects the model output is known as uncertainty quantification (UQ). This study establishes develops, and characterizes the application of UQ to ECGI. Approach. We establish two formulations for applying UQ to ECGI: a polynomial chaos expansion (PCE) based parametric UQ formulation (PCE-UQ formulation), and a novel UQ-aware inverse formulation which leverages our previously established ‘joint-inverse’ formulation (UQ joint-inverse formulation). We apply these to evaluate the effect of uncertainty in the heart position on the ECGI solutions across a range of ECGI datasets. Main results. We demonstrated the ability of our UQ-ECGI formulations to characterize the effect of parameter uncertainty on the ECGI inverse problem. We found that while the PCE-UQ inverse solution provided more complex outputs such as sensitivities and standard deviation, the UQ joint-inverse solution provided a more interpretable output in the form of a single ECGI solution. We find that between these two methods we are able to assess a wide range of effects that heart position variability has on the ECGI solution. Significance. This study, for the first time, characterizes in detail the application of UQ to the ECGI inverse problem. We demonstrated how UQ can provide insight into the behavior of ECGI using variability in cardiac position as a test case. This study lays the groundwork for future development of UQ-ECGI studies, as well as future development of ECGI formulations which are robust to input parameter variability.

Enhancing the predictability of flood forecasts by combining Numerical Weather Prediction ensembles with multiple hydrological models

Ensemble Prediction Systems (EPS) from a single Numerical Weather Prediction (NWP) model often miss out on a few extreme events. Similarly, a single hydrological model cannot capture all types of flows, such as flood peaks, occurring within a watershed due to the inaccurate representation of the real world processes in their mathematical structure. To focus on these aspects, this study proposes five ‘NWP+HM’ combinations (HM refers to Hydrologic Model) to investigate which combination performs better in providing quality flood forecasts. The EPS obtained from THORPEX Interactive Grand Global Ensemble (TIGGE) archive are forced into calibrated hydrologic models to generate ensemble flood forecasts (EFFs). The methodology also includes a machine-learning post-processing technique, Quantile Regression Forests (QRF), for temporal correction of bias and under-dispersion in the raw EPS data. The study finds that both the EPS used show similar performance in generating flood forecasts. However, the study demonstrates that the hydrologic model uncertainty has a more significant impact on capturing flood peaks compared to input model uncertainty. Finally, the simulated flood forecasts are verified for their quality using both deterministic and probabilistic metrics. For the above five combinations, an increase in leadtime up to 10 days is observed when the raw data are post-processed using QRF technique. The study identifies a computationally efficient ‘NWP+HM’ combination for use in the hydrological forecasting system, which improves the predictability of flood forecasts several days ahead.