Sensitivity Analysis Of Model Research Articles

Fractional dynamics and sensitivity analysis of measles epidemic model through vaccination

Measles is a highly contagious disease that mainly affects children worldwide. Even though a reliable and effective vaccination is available, there were 140,000 measles deaths worldwide in 2018, and most of them were children under the age five years. In this paper, we comprehensively investigate a novel fractional SVEIR (Susceptible-Vaccinated-Exposed-Infected-Recovered) model of the measles epidemic powered by nonlinear fractional differential equations to understand the epidemic’s dynamical behaviour. We use a non-singular Atangana-Baleanu fractional derivative to analyze the proposed model, taking advantage of non-locality. The existence, uniqueness, positivity and boundedness of the solutions are shown via concepts of fixed point theory, and we also perform the Ulam-Hyers stability of the considered model. The parameter sensitivity is discussed in the context of the variance with each parameter using 3-D graphics based on the basic reproduction number. Moreover, with the Atangana-Toufik numerical scheme, numerical findings are depicted for different fractional-order values. The presented approach produce results that are efficiently consistent and in excellent agreement with the theoretical results.

Implementation of mechanistic modeling and global sensitivity analysis (GSA) for design, optimization, and scale-up of a roller compaction process

The pharmaceutical industry has been shifting towards the application of mechanistic modeling to improve process robustness, enable scale-up, and reduce time to market. Modeling approaches have been well-developed for processes such as roller compaction, a continuous dry granulation process. Several mechanistic models/approaches have been documented with limited application to high drug-loaded formulations. In this study, the Johanson model was employed to optimize roller compaction processing and guide its scale-up for a high drug loaded formulation. The model was calibrated using a pilot-scale Minipactor and was validated for a commercial-scale Macropactor. Global sensitivity analysis (GSA) was implemented to determine the impact of process parameter variations (roll force, gap, speed) on a quality attribute [solid fraction (SF)]. The throughput method, which estimates SF values of ribbons using granule production rate, was also studied. The model predicted SF values for all 14 Macropactor batches within ± 0.04 SF. The throughput method estimated SF with ± 0.06 SF for 7 out of 11 batches. GSA confirmed that roll force had the largest impact on SF. This case study represents a process modeling approach to build quality into the products/processes and expands the use of mechanistic modeling during drug product development.

A streamlined approach to uncertainty and sensitivity analysis for models with spatial outputs with an example from geodiversity assessment

The paper proposes a joint use of two global sensitivity analysis methods to evaluate the influence of model inputs on spatially explicit outputs. The analysis is conducted on spatial outputs of geodiversity assessment. First, the screening method is used to identify inputs with low importance and interactions, which can be discarded in later analysis. A simplified model is compared with a complete model using the mean and variance calculated on spatial outputs to evaluate outcome uncertainty. Finally, the variance decomposition method computes two sensitivity index maps per input. The results are mapped using bivariate classification, allowing for the simultaneous identification of regions with higher geodiversity scores and their corresponding variability. The research reveals that the streamlined approach preserves the overall patterns of outcome uncertainty and sensitivity observed in an original model while simultaneously reducing the number of outcome maps to interpret. Additionally, bivariate mapping proves efficient in concurrently depicting spatial sensitivity and interaction effects. The analytic efficiencies afforded by the presented approach and bivariate mapping enhance the operational knowledge of spatially explicit uncertainty and sensitivity analysis by simplifying the computational procedure and offering an alternative way of visualizing and interpreting input factors driving the sensitivity of model output.

Sensitivity Analysis of Borehole Thermal Energy Storage (BTES): Examining Key Factors for System Optimization

Borehole thermal-energy storage (BTES) systems have garnered significant attention owing to their efficacy in storing thermal energy for heating and cooling applications. Accurate modeling is paramount for ensuring the precise design and operation of BTES systems. This study conducts a sensitivity analysis of BTES modeling by employing a comparative investigation of five distinct parameters on a wedge-shaped model, with implications extendable to a cylindrical configuration. The parameters examined included two design factors (well spacing and grout thermal conductivity), two operational variables (charging and discharging rates), and one geological attribute (soil thermal conductivity). Finite element simulations were carried out for the sensitivity analysis to evaluate the roundtrip efficiency, both on a per-cycle basis and cumulatively over three years of operation, serving as performance metrics. The results showed varying degrees of sensitivity across different models to changes in these parameters. In particular, the round-trip efficiency exhibited a greater sensitivity to changes in spacing and volumetric flow rate. Furthermore, this study underscores the importance of considering the impact of the soil and grout-material thermal conductivities on the BTES-system performance over time. An optimized scenario was formulated and juxtaposed against the base case, with a comparative assessment based on a 10-year simulation. The analysis revealed that, at the end of the 10-year period, the optimized BTES model achieved a cyclic efficiency of 83.4%. This sensitivity analysis provides valuable insights into the merits and constraints of diverse BTES modeling methodologies, aiding in the selection of appropriate modeling tools for BTES system design and operation.

Characterizing the impact of reservoir storage and discharge on nitrogen dynamics in an upstream wetland using a δ15N and δ18O dual-isotope approach

The identification of nitrate sources in reservoir water is important for watershed-scale surface pollution management. Significant fluctuations in river water levels arising from reservoir storage and discharge influence nitrate sources and transport processes. The Sanmenxia Reservoir, in the middle reaches of the Yellow River in China, undergoes significant water level changes (290–316 m), altering the composition of the nitrogen sources. This study employed a δ15N and δ18O dual-isotope method and MixSIAR modeling to quantify the contributions of nitrate sources. This reveals the impact of reservoir water impoundment and discharge on nitrogen dynamics in the upstream region of the wetland and the model sensitivity for each nitrate source. The results showed that the average concentrations of nitrate‑nitrogen (NO- 3-N) were elevated during the impoundment period compared to the discharge period. Nitrogen sources exhibited varying proportions in surface water, groundwater, and soil water during both the impoundment and discharge periods. The predominant sources include manure and sewage (MS), with a maximum proportion of 57.4 % in surface water. Soil nitrogen (SN) accounted for 25.8 % of groundwater nitrogen and 32.1 % of soil water nitrogen during the impoundment period, whereas, during the discharge period, soil nitrogen made up 41.4 % of surface water nitrogen, manure and sewage contributed 44.8 % of groundwater nitrogen, and manure and sewage dominated with 56.7 % of soil water nitrogen. Sensitivity analysis of the MixSIAR model revealed that the isotopic composition of the manure and sewage primary source most significantly influenced the apportionment results of the riverine nitrate source. Reservoir discharge facilitates the dissimilatory nitrate reduction to ammonium (DNRA). The migration of NO- 3 from surface water to soil water and groundwater occurred from the impoundment period to the discharge period.

Parameter estimation and global sensitivity analysis of a bacterial-plasmid model with impulsive drug treatment

In this paper, a mathematical model is developed to study bacterial resistance caused by plasmids and mutations in the presence of the impulsive drug treatment. Firstly, the stability of the bacterial-plasmid extinction periodic solution is proved by Floquet theory and the permanence of system is presented while some conditions are satisfied. Secondly, the nontrivial periodic solution bifurcated from the bacterial-plasmid extinction periodic solution is obtained in view of the four-dimensional impulsive control of bifurcation method. Good fitting results are given by the Mean Absolute Percentage Error method for four different cases of bacterial media. Further, the global sensitivity of R0 is analyzed by the Partial Rank Correlation Coefficient method, and the parameters that have important influence on the system are determined. Finally, numerical simulations are performed to validate the accuracy of the theoretical findings.

Sensitivity analysis of similarity learning models for well-intervals based on logging data

The problem of the interpretability of neural network predictions is crucial for industry, especially in areas where the cost of a mistake is incredibly high, to which oil&gas belongs. In this sphere, similarity models play an important role. One of the pre-required input data for constructing a geological model of hydrocarbon field are interwell correlation results through well logging data analysis. Recent publications address the problem of similarity assessment between distinct wells, which is the manual, time-consuming, and expert-based process. To enhance the interpretability of the developed deep learning models, we propose a visualization tool consisting of two methods. Our first method is an adaptation of a saliency map, which has already shown its visualization and interpretability quality. However, as the possibility of dealing with black-box models exists and is high enough, we propose another method based on the masking of the original well-interval. The case study from the Norway basin and our tool’s visualization quality evaluation demonstrate the effectiveness of the developed tool.

Simultaneous sensitivity analysis of mixed-integer location-allocation models using machine learning tools: cancer hospitals’ network design

Simultaneous sensitivity analysis of mixed-integer location-allocation models using machine learning tools: cancer hospitals’ network design

Temporal meta-optimiser based sensitivity analysis (TMSA) for agent-based models and applications in children's services.

With current and predicted economic pressures within English Children's Services in the UK, there is a growing discourse around the development of methods of analysis using existing data to make more effective interventions and policy decisions. Agent-Based modelling shows promise in aiding in this, with limitations that require novel methods to overcome. This can include challenges in managing model complexity, transparency, and validation; which may deter analysts from implementing such Agent-Based simulations. Children's Services specifically can gain from the expansion of modelling techniques available to them. Sensitivity analysis is a common step when analysing models that currently has methods with limitations regarding Agent-Based Models. This paper outlines an improved method of conducting Sensitivity Analysis to enable better utilisation of Agent-Based models (ABMs) within Children's Services. By using machine learning based regression in conjunction with the Nomadic Peoples Optimiser (NPO) a method of conducting sensitivity analysis tailored for ABMs is achieved. This paper demonstrates the effectiveness of the approach by drawing comparisons with common existing methods of sensitivity analysis, followed by a demonstration of an improved ABM design in the target use case.

Soft computing to predict the porosity and permeability of pervious concrete based on mix design and ultrasonic pulse velocity

ABSTRACT The present study explores the potential of machine learning to predict the porosity and permeability of pervious concrete constructed on mix parameters (compaction energy, aggregate-to-cement ratio and aggregate size) and ultrasonic velocity. The prediction models use non-destructive measurements and mixed design variables, which can help the construction sector apply the models without any theoretical expertise. The study uses 225 data samples from an experimental study. This study used six machine learning algorithms, namely, linear regression, artificial neural networks, boosted decision tree regression, random forest regression, K-nearest neighbour and support vector regression, to determine the best predictive model. The results show that the ANN model is the best technique for predicting the porosity of pervious concrete (R2 = 0.9502 for training datasets and R2 = 0.8958 for testing datasets) and boosted decision tress for permeability of pervious concrete (R2 = 0.9323 for training datasets and R2 = 0.7574 for testing datasets). The sensitivity analysis of the random forest regression model reveals that ultrasonic pulse velocity is the most influential parameter for the prediction of porosity and permeability of pervious concrete. The proposed models provide a more accurate method for estimating the porosity and permeability of pervious concrete.

Vitamin D metabolism in critically ill patients with acute kidney injury: a prospective observational study

BackgroundVitamin D deficiency in critically ill patients is associated with poor outcomes, and vitamin D supplementation is recommended for patients with chronic kidney disease. Whether acute kidney injury (AKI) is associated with altered Vitamin D metabolism is unknown. We aimed to compare the longitudinal profiles of serum 25(OH)D and 1,25(OH)2D concentrations in critically ill patients with and without moderate to severe AKI and explore the impact of renal recovery and parathyroid hormone (PTH).MethodsIn this prospective, observational study in two centres in the UK, critically ill patients with and without AKI underwent serial measurement of serum 25(OH)D and 1,25(OH)2D and plasma PTH concentrations for 5 days. Linear mixed model analysis and sensitivity analyses were performed.ResultsSerial data of 137 patients were analysed. Seventy-one patients had AKI stage II/III of whom 23 recovered kidney function during the 5-day study period; 66 patients did not have AKI at enrolment of whom 14 developed new AKI. On day of enrolment, patients’ serum 25(OH)D concentrations were low (median 18 nmol/L) but there was no significant difference between patients with and without AKI. Median serum 1,25(OH)2D levels were significantly lower in patients with AKI II/III (41 pmol/L [IQR 26, 58]) compared to similarly unwell patients without AKI (54 pmol/L [IQR 33, 69]) during the 5-day period. Recovery of kidney function in patients with AKI was associated with a rise in 1,25(OH)2D concentrations. Plasma PTH results were impacted by serum calcium and magnesium levels but not associated with 1,25(OH)2D levels.ConclusionsCritically ill patients with moderate-to-severe AKI have significantly lower serum 1,25(OH)2D concentrations than similarly sick patients without AKI but there was no difference in serum 25(OH)D concentrations. Recovery of AKI was associated with a rise in serum 1,25(OH)2D concentrations. More research is needed to investigate the health benefits and safety of supplementation with active vitamin D in critically ill patients with moderate-to-severe AKI.Trial registration Clinicaltrials.gov (NCT02869919), registered on 16 May 2016.

Cost-Effectiveness of Axicabtagene Ciloleucel for Adult Patients With Relapsed or Refractory Follicular Lymphoma in the United States

ObjectivesThe results of a recent single-arm trial (ZUMA-5) of axicabtagene ciloleucel (axi-cel) for relapsed/refractory (r/r) follicular lymphoma (FL) demonstrated high rates of durable response and tolerable toxicity among treated patients. To quantify the value of axi-cel compared with standard of care (SOC) to manage r/r FL patients who have had at least 2 prior lines of systemic therapy (3L+), a cost-effectiveness model was developed from a US third-party payer perspective. MethodsA 3-state partitioned-survival cost-effectiveness model was developed with a lifetime horizon. Patient-level analyses of the 36-month ZUMA-5 (axi-cel) and SCHOLAR-5 (SOC) studies were used to extrapolate progression-free and overall survivals. After 5 years of survival, an estimated 40% of the modeled population was assumed to experience long-term remission based on literature. Results include the incremental cost-effectiveness ratio (ICER) measured as incremental cost per quality-adjusted life year (QALY) gained. One-way sensitivity analysis, probabilistic sensitivity analysis, and scenario analyses were performed. All outcomes were discounted 3% per year. ResultsAxi-cel led to an increase of 4.28 life-years, 3.64 QALYs, and a total cost increase of $321 192 relative to SOC, resulting in an ICER of $88 300 per QALY. Across all parameters varied in the one-way sensitivity analysis, the ICER varied between $133 030 and $67 277. In the probabilistic sensitivity analysis, axi-cel had a 99% probability of being cost-effective across 5000 iterations using a $150 000 willingness-to-pay threshold. ConclusionsGiven the robustness of the model results and sensitivity analyses, axi-cel is expected to be a cost-effective treatment in 3L+ r/r FL.

Cost-Effectiveness of First-Line Atezolizumab versus Chemotherapy in Non-Small-Cell Lung Cancer Patients Ineligible for Platinum-Containing Regimens.

The IPSOS study provided evidence supporting the efficacy and tolerability of first-line atezolizumab compared to single-agent chemotherapy for non-small-cell lung cancer (NSCLC) patients ineligible for treatment with a platinum-containing regimen. This study aimed to assess the cost-effectiveness of atezolizumab specifically in this population, considering the perspective of the Chinese healthcare system. In this analysis, a three-state Markov model was utilized. The survival data were derived from the IPSOS clinical trial. Direct medical costs and utility values were collected from national authoritative database and published literature. The primary outcomes were costs, quality-adjusted life-years (QALYs) and incremental cost-effectiveness ratio (ICER). To ensure the robustness of our model, both one-way and probabilistic sensitivity analyses were conducted. Atezolizumab monotherapy led to an increase in costs of $4139.23 compared to single-agent chemotherapy. Additionally, it resulted in a gain of 0.14 QALYs, leading to an ICER of $29,365.79 per QALY, which was below the willingness-to-pay threshold of $36,066 per QALY used in the model. One-way sensitivity analyses revealed cost of atezolizumab and utility of progressive disease (PD) as major influencing factors for ICER. Furthermore, probabilistic sensitivity analyses confirmed our base-case results. From the perspective of the Chinese healthcare system, atezolizumab emerges as a cost-effective choice for the first-line treatment of NSCLC patients ineligible for platinum-based chemotherapy.

Spatial modeling sensitivity analysis: Copula selection for wind speed dependence

As the adoption of wind energy as a key renewable energy source accelerates, precise power flow analysis becomes crucial for accurate power delivery forecasting. This paper addresses the inherent uncertainties in wind speed data at different wind farm locations by conducting a sensitivity analysis to assess wind farm pairs. The analysis accommodates various data sizes, namely, short, medium, and large, and diverse spatial relationships between wind farms. By leveraging National Renewable Energy Laboratory wind speed data from nine distinct wind farms, the dependence structure between wind farm pairs is modeled using copulas. This modeling takes both the wind speed knowledge level and the various spatial interplays among the wind farm pairs into consideration. The findings indicate an inverse proportionality between the strength of dependence and the distance separating the wind farm pairs.

Ranking of asset maintenance process KPIs using Fermatean fuzzy Delphi and Fermatean fuzzy ELECTRE method

The paper aims to present a hybrid model for measuring the performance of business processes in complex organizations based on the subjective decision-making of expert teams. The subject of the research is finding ways to measure, analyze and improve the key performance indicators (KPIs) process. Obtaining the values of KPIs, which reflect the real state of the process, creates a basis for their ranking, i.e. insight into KPIs that are extremely important for the process as well as KPIs that are of lesser importance, but as such are not excluded from consideration because they are necessary for the beginning, realization and completion of the process. The model was compiled through five phases and was tested through a case study in a real business organization, which deals with the maintenance of complex combat systems. The obtained results helped the management to take certain measures in order to improve the performance of the maintenance process. In the model, it is proposed to form two expert teams, which make assessments based on experience and express them in linguistic terms according to a predefined scale. Modeling of linguistic expressions is realized using intuitive fuzzy sets of a higher order, more precisely Fermatean fuzzy sets (FFS). Selecting KPIs, decomposing the process into sub-processes and assessing the relative importance of sub-processes is carried out by one team of experts, while another team carries out the assessment of KPIs at the level of each sub-process. Determining the relative importance of sub-processes is realized using the Delphi method extended to FFS while reaching a consensus. The measurement of process performance, i.e. the value of KPIs, is realized using Multi-Criteria Group Decision-Making (MCGDM), such as the ELECTRE method extended with FFS. The sensitivity analysis of the developed model is realized by uncertainty modeling with q-rung orthopair fuzzy sets.

Kernel-based Sensitivity Analysis for (excursion) sets

In this paper, we aim to perform sensitivity analysis of set-valued models and, in particular, to quantify the impact of uncertain inputs on feasible sets, which are key elements in solving a robust optimization problem under constraints. While most sensitivity analysis methods deal with scalar outputs, this paper introduces a novel approach to perform sensitivity analysis with set-valued outputs. Our innovative methodology is designed for excursion sets, but is versatile enough to be applied to set-valued simulators, including those found in viability fields, or when working with maps like pollutant concentration maps or flood zone maps. We propose to use the Hilbert-Schmidt Independence Criterion (HSIC) with a kernel designed for set-valued outputs. After setting a probabilistic framework for random sets, a first contribution is the proof that this kernel is characteristic, an essential property in a kernel-based sensitivity analysis context. To measure the contribution of each input, we then propose to use HSIC-ANOVA indices. With these indices, we can identify which inputs should be neglected (screening) and we can rank the others according to their influence (ranking). The estimation of these indices is also adapted to the set-valued outputs. Finally, we test the proposed method on three test cases of excursion sets.

Sensitivity analysis of a 2D flood inundation model. A case study of Tous Dam

Sensitivity analysis of a 2D flood inundation model. A case study of Tous Dam

Participatory assessment of pollution and health risk in artisanal and small-scale gold mining communities in Colombia

The predominantly Afro-descendant communities of Yolombó and La Toma depend on the region's rich natural resources through livelihoods such as farming, fishing, and artisanal and small-scale gold mining (ASGM). Previous studies of the region have identified elevated levels of mercury in several surface and groundwater sites. Furthermore, recent regional studies have identified human health risks associated with mercury exposure through hazard quotient (HQ) modeling and global sensitivity and uncertainty analysis (GSUA). However, there has yet to be a comprehensive analysis of water pollution involving different metals and metalloids potentially present in ASGM-influenced areas to estimate the associated environmental and human health impacts. In this study, we employed a participatory methodology to identify sources of exposure in Colombian ASGM communities through surveys, consumption diaries, and interviews. We then screened for relevant metals and metalloids through groundwater analysis using inductively coupled plasma optical emission spectroscopy (ICP-OES) and cold vapor atomic absorption spectrometry (CV-AAS). Furthermore, we calculated water pollution indices, including heavy metal pollution index (HPI), degree of contamination (Cd), heavy metal toxicity load (HMTL) and heavy metal evaluation index (HEI). Finally, we quantified human health risks related to groundwater consumption through the HQ and hazard index (HI). Although the groundwater assessed in this study generally meets United States and Colombian regulatory standards, heightened Fe, Mn, and Al levels were observed. The pollution indices generally agreed that three of the sites tested showed medium to high pollution levels. The HI showed that the combined effect of all metals tested did not substantiate risk for most participants. However, the HI of one participant, a child, exceeded the 1.00 threshold. Furthermore, the presence of Pb, despite meeting regulatory limits, necessitates further scrutiny due to its substantial impact on pollution and human health hazard indices.

Spreadsheet Modeling and Wrangling with Python

A staple of many spreadsheet-based management science courses is the use of Excel for activities such as model building, sensitivity analysis, goal seeking, and Monte-Carlo simulation. What might those things look like if carried out using Python? We describe a teaching module in which Python is used to do typical Excel-based modeling and data-wrangling tasks. In addition, students are exposed to basic software engineering principles, including project folder structures, version control, object-oriented programming, and other more advanced Python skills, creating deployable packages and documentation. The module is supported with Jupyter notebooks, Python scripts, course web pages that include numerous screencasts, and a few GitHub repositories. All of the supporting materials are permissively licensed and freely accessible. Supplemental Material: The supplemental files are available at https://doi.org/10.1287/ited.2023.0047 .

Sensitivity of bias correction step on generating hydrological scenarios

Abstract Significant shifts in hydro-climatic regimes are expected in many parts of the world during the 21st century, affecting the water cycle. Vulnerability, impact, and adaptation studies often use tailored modeling chains to assess the expected effects of climate change, but the robustness of these chains is rarely investigated. This highlights the need for more rigorous evaluation of modeling chains to ensure that they are reliable for informed decision-making processes. To address this gap, we propose a framework for evaluating the sensitivity of hydrological scenario production to the bias correction step. We apply the framework to the Senegal River Basin, using three bias correction methods (linear scale, empirical quantile mapping, and nested bias correction) and three procedures (climate-correction, hydrological-correction, and climate-hydrological-correction). Our results show that the choice of modeling chain has a significant impact on future hydro-climatic trajectories. In particular, the combination of climate-and-hydrological-correction procedures may be optimal when both climate biases and hydrological model errors are significant. Moreover, using multiple bias correction methods can strengthen the ensemble of future hydro-climatic conditions. These findings have implications for vulnerability-impact-adaptation studies and underscore the importance of rigorous modeling chain design and sensitivity analysis.