Sensitivity Analysis of a Physiological Model for 2,3,7,8-Tetrachlorodibenzo-p-dioxin (TCDD): Assessing the Impact of Specific Model Parameters on Sequestration in Liver and Fat in the Rat

The knowledge about the impact of structure-specific parameters on the biomechanical behavior of a computer model has an essential meaning for the realistic modeling and system improving. Especially the biomechanical parameters of the intervertebral discs, the ligamentous structures and the facet joints are seen in the literature as significant components of a spine model, which define the quality of the model. Therefore, it is important to understand how the variations of input parameters for these components affect the entire model and its individual structures. Sensitivity analysis can be used to gain the required knowledge about the correlation of the input and output variables in a complex spinal model. The present study analyses the influence of the biomechanical parameters of the intervertebral disc using different sensitivity analysis methods to optimize the spine model parameters. The analysis is performed with a multi-body simulation model of the cervical functional spinal unit C6-C7.

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Nonintrusive Global Sensitivity Analysis for Linear Systems With Process Noise

Journal of Computational and Nonlinear Dynamics ◽

10.1115/1.4041622 ◽

2019 ◽

Vol 14 (2) ◽

Cited By ~ 1

Author(s):

Souransu Nandi ◽

Tarunraj Singh

Keyword(s):

Sensitivity Analysis ◽

Linear Systems ◽

Global Sensitivity Analysis ◽

Benchmark Problems ◽

Model Parameters ◽

Parameter Uncertainties ◽

Sobol Indices ◽

Global Sensitivity ◽

Time Invariant ◽

The Impact

The focus of this paper is on the global sensitivity analysis (GSA) of linear systems with time-invariant model parameter uncertainties and driven by stochastic inputs. The Sobol' indices of the evolving mean and variance estimates of states are used to assess the impact of the time-invariant uncertain model parameters and the statistics of the stochastic input on the uncertainty of the output. Numerical results on two benchmark problems help illustrate that it is conceivable that parameters, which are not so significant in contributing to the uncertainty of the mean, can be extremely significant in contributing to the uncertainty of the variances. The paper uses a polynomial chaos (PC) approach to synthesize a surrogate probabilistic model of the stochastic system after using Lagrange interpolation polynomials (LIPs) as PC bases. The Sobol' indices are then directly evaluated from the PC coefficients. Although this concept is not new, a novel interpretation of stochastic collocation-based PC and intrusive PC is presented where they are shown to represent identical probabilistic models when the system under consideration is linear. This result now permits treating linear models as black boxes to develop intrusive PC surrogates.

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A physically-based parsimonious hydrological model for flash floods in Mediterranean catchments

Natural Hazards and Earth System Science ◽

10.5194/nhess-11-2567-2011 ◽

2011 ◽

Vol 11 (9) ◽

pp. 2567-2582 ◽

Cited By ~ 41

Author(s):

H. Roux ◽

D. Labat ◽

P.-A. Garambois ◽

M.-M. Maubourguet ◽

J. Chorda ◽

...

Keyword(s):

Sensitivity Analysis ◽

Soil Properties ◽

Overland Flow ◽

Hydrological Model ◽

Extreme Event ◽

Model Parameters ◽

Peak Time ◽

Efficiency Coefficient ◽

Soil Saturation ◽

The Impact

Abstract. A spatially distributed hydrological model, dedicated to flood simulation, is developed on the basis of physical process representation (infiltration, overland flow, channel routing). Estimation of model parameters requires data concerning topography, soil properties, vegetation and land use. Four parameters are calibrated for the entire catchment using one flood event. Model sensitivity to individual parameters is assessed using Monte-Carlo simulations. Results of this sensitivity analysis with a criterion based on the Nash efficiency coefficient and the error of peak time and runoff are used to calibrate the model. This procedure is tested on the Gardon d'Anduze catchment, located in the Mediterranean zone of southern France. A first validation is conducted using three flood events with different hydrometeorological characteristics. This sensitivity analysis along with validation tests illustrates the predictive capability of the model and points out the possible improvements on the model's structure and parameterization for flash flood forecasting, especially in ungauged basins. Concerning the model structure, results show that water transfer through the subsurface zone also contributes to the hydrograph response to an extreme event, especially during the recession period. Maps of soil saturation emphasize the impact of rainfall and soil properties variability on these dynamics. Adding a subsurface flow component in the simulation also greatly impacts the spatial distribution of soil saturation and shows the importance of the drainage network. Measures of such distributed variables would help discriminating between different possible model structures.

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Sensitivity analysis of an integrated numerical flow model output to key parameters used in common qualitative vulnerability assessment methods

10.5194/egusphere-egu2020-10512 ◽

2020 ◽

Author(s):

Joanna Doummar ◽

Assaad H. Kassem

Keyword(s):

Sensitivity Analysis ◽

Vulnerability Assessment ◽

Groundwater Vulnerability ◽

Assessment Methods ◽

Qualitative Assessment ◽

Model Parameters ◽

Karst Aquifers ◽

Model Output ◽

Local Sensitivity ◽

The Impact

Qualitative vulnerability assessment methods applied in karst aquifers rely on key factors in the hydrological compartments usually assigned different weights according to their estimated impact on groundwater vulnerability. Based on an integrated numerical groundwater model on a snow-governed karst catchment area (Assal Spring- Lebanon), the aim of this work is to quantify the importance of the most influential parameters on recharge and spring discharge and outline potential parameters that are not accounted for in standard methods, when in fact they do play a role in the intrinsic vulnerability of a system. The assessment of the model sensitivity and the ranking of parameters are conducted using an automatic calibration tool for local sensitivity analysis in addition to a variance-based local sensitivity assessment of model output time series (recharge and discharge) &#160;for two consecutive years (2016-2017) to various model parameters. The impact of each parameter was normalized to estimate standardized weights for each of the process based key-controlling parameters. Parameters to which model was sensitive were factors related to soil, 2) fast infiltration (bypass function) typical of karst aquifers, 3) climatic parameters (melting temperature and degree day coefficient) and 4) aquifer hydraulic properties that play a major role in groundwater vulnerability inducing a temporal effect and varied recession. Other less important parameters play different roles according to different assigned weights proportional to their ranking. Additionally, the effect of slope/geomorphology (e.g., dolines) was further investigated. &#160;In general, this study shows that the weighting coefficients assigned to key vulnerability factors in the qualitative assessment methods can be reevaluated based on this process-based approach.&#160;&#160;&#160;

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Uncertainty and sensitivity analysis of GIS based continuous hydrological modelling

10.32920/ryerson.14643702.v1 ◽

2021 ◽

Author(s):

Harry R. Manson

Keyword(s):

Sensitivity Analysis ◽

Curve Number ◽

Monte Carlo Analysis ◽

Model Parameters ◽

Rainfall Runoff ◽

Lumped Model ◽

Daily Average ◽

Rainfall Runoff Model ◽

The Impact ◽

Runoff Model

The impact of uncertainty in spatial and a-spatial lumped model parameters for a continuous rainfall-runoff model is evaluated with respect to model prediction. The model uses a modified SCS-Curve Number approach that is loosely coupled with a geographic information system (GIS). The rainfall-runoff model uses daily average inputs and is calibrated using a daily average streamflow record for the study site. A Monte Carlo analysis is used to identify total model uncertainty while sensitivity analysis is applied using both a one-at-a-time (OAT) approach as well as through application of the extended Fourier Amplitude Sensitivity Technique (FAST). Conclusions suggest that the model is highly followed by model inputs and finally the Curve Number. While the model does not indicate a high degree of sensitivity to the Curve Number at present conditions, uncertainties in Curve Number estimation can potentially be the cause of high predictive errors when future development scenarios are evaluated.

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The Importance of Prior Sensitivity Analysis in Bayesian Statistics: Demonstrations Using an Interactive Shiny App

Frontiers in Psychology ◽

10.3389/fpsyg.2020.608045 ◽

2020 ◽

Vol 11 ◽

Author(s):

Sarah Depaoli ◽

Sonja D. Winter ◽

Marieke Visser

Keyword(s):

Sensitivity Analysis ◽

Simulation Study ◽

Bayesian Methods ◽

Multiple Regression Model ◽

Specific Model ◽

Estimation Methods ◽

Prior Distributions ◽

Shiny App ◽

Novice Users ◽

The Impact

The current paper highlights a new, interactive Shiny App that can be used to aid in understanding and teaching the important task of conducting a prior sensitivity analysis when implementing Bayesian estimation methods. In this paper, we discuss the importance of examining prior distributions through a sensitivity analysis. We argue that conducting a prior sensitivity analysis is equally important when so-called diffuse priors are implemented as it is with subjective priors. As a proof of concept, we conducted a small simulation study, which illustrates the impact of priors on final model estimates. The findings from the simulation study highlight the importance of conducting a sensitivity analysis of priors. This concept is further extended through an interactive Shiny App that we developed. The Shiny App allows users to explore the impact of various forms of priors using empirical data. We introduce this Shiny App and thoroughly detail an example using a simple multiple regression model that users at all levels can understand. In this paper, we highlight how to determine the different settings for a prior sensitivity analysis, how to visually and statistically compare results obtained in the sensitivity analysis, and how to display findings and write up disparate results obtained across the sensitivity analysis. The goal is that novice users can follow the process outlined here and work within the interactive Shiny App to gain a deeper understanding of the role of prior distributions and the importance of a sensitivity analysis when implementing Bayesian methods. The intended audience is broad (e.g., undergraduate or graduate students, faculty, and other researchers) and can include those with limited exposure to Bayesian methods or the specific model presented here.

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A physiological model for predicting carboxyhemoglobin formation from exposure to carbon monoxide in rats

Journal of Applied Physiology ◽

10.1152/jappl.1999.86.6.1977 ◽

1999 ◽

Vol 86 (6) ◽

pp. 1977-1983 ◽

Cited By ~ 13

Author(s):

Edgar C. Kimmel ◽

Robert L. Carpenter ◽

James E. Reboulet ◽

Kenneth R. Still

Keyword(s):

General Model ◽

Minute Ventilation ◽

Blood Gas Analysis ◽

Physiological Model ◽

Specific Model ◽

Gas Analysis ◽

Model Parameters ◽

Total Blood ◽

Continuous Simulation ◽

Model Predictions

A time-dependent simulation model, based on the Coburn-Forster-Kane equation, was written in Advanced Continuous Simulation Language to predict carboxyhemoglobin (HbCO) formation and dissociation in F-344 rats during and after exposure to 500 parts/million CO for 1 h. Blood-gas analysis and CO-oximetry were performed on samples collected during exposure and off-gassing of CO. Volume displacement plethysmography was used to measure minute ventilation (V˙e) during exposure. CO diffusing capacity in the lung (Dl CO) was also measured. Other model parameters measured in the animals included blood pH, total blood volume, and Hb concentration. Comparisons between model predictions using values forV˙e, Dl CO, and the Haldane coefficient cited in the literature and predictions using measured V˙e, Dl CO, and calculated Haldane coefficient for individual animals were made. General model predictions using values for model parameters derived from the literature agreed with published HbCO values by a factor of 0.987 but failed to simulate experimental data. On average, the general model overpredicted measured HbCO level by nearly 9%. A specific model using the means of measured variables predicted HbCO concentration within a factor of 0.993. When experimentally observed parameter fluctuations were included, the specific model predictions reflected experimental effects on HbCO formation.

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Modelling gastrointestinal parasitism infection in a sheep flock over two reproductive seasons:in silicoexploration and sensitivity analysis

Parasitology ◽

10.1017/s0031182016000871 ◽

2016 ◽

Vol 143 (12) ◽

pp. 1509-1531 ◽

Cited By ~ 2

Author(s):

M. SACCAREAU ◽

C. R. MORENO ◽

I. KYRIAZAKIS ◽

R. FAIVRE ◽

S. C. BISHOP

Keyword(s):

Sensitivity Analysis ◽

Control Strategies ◽

Real Data ◽

Published Data ◽

Model Parameters ◽

Sheep Flock ◽

Source Of Infection ◽

Teladorsagia Circumcincta ◽

Host Nutrition ◽

The Impact

SUMMARYIn reproducing ewes, a periparturient breakdown of immunity is often observed to result in increased fecal egg excretion, making them the main source of infection for their immunologically naive lambs. In this study, we expanded a simulation model previously developed for growing lambs to explore the impact of the genotype (performance and resistance traits) and host nutrition on the performance and parasitism of both growing lambs and reproducing ewes naturally infected withTeladorsagia circumcincta. Our model accounted for nutrient-demanding phases, such as gestation and lactation, and included a supplementary module to manage the age structure of the ewe flock. The model was validated by comparison with published data. Because model parameters were unknown or poorly estimated, detailed sensitivity analysis of the model was performed for the sheep mortality and the level of infection, following a preliminary screening step. The parameters with the greatest effect on parasite-related outputs were those driving animal growth and milk yield. Our model enables different parasite-control strategies (host nutrition, breeding for resistance and anthelmintic treatments) to be assessed on the long term in a sheep flock. To optimizein silicoexploration, the parameters highlighted by the sensitivity analysis should be refined with real data.

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Sensitivity of Direct Runoff to Curve Number Using the SCS-CN Method

Civil Engineering Journal ◽

10.28991/cej-2019-03091445 ◽

2019 ◽

Vol 5 (12) ◽

pp. 2738-2746

Author(s):

Abdul Ghani Soomro ◽

Muhammad Munir Babar ◽

Anila Hameem Memon ◽

Arjumand Zehra Zaidi ◽

Arshad Ashraf ◽

...

Keyword(s):

Sensitivity Analysis ◽

Water Availability ◽

Curve Number ◽

Resource Planning ◽

Model Parameters ◽

Rainfall Runoff ◽

Direct Runoff ◽

Wide Range ◽

The Impact ◽

Scs Cn

This study explores the impact of runoff curve number (CN) on the hydrological model outputs for the Morai watershed, Sindh-Pakistan, using the Soil Conservation Service Curve Number (SCS-CN) method. The SCS-CN method is an empirical technique used to estimate rainfall-runoff volume from precipitation in small watersheds, and CN is an empirically derived parameter used to calculate direct runoff from a rainfall event. CN depends on soil type, its condition, and the land use and land cover (LULC) of an area. Precise knowledge of these factors was not available for the study area, and therefore, a range of values was selected to analyze the sensitivity of the model to the changing CN values. Sensitivity analysis involves a methodological manipulation of model parameters to understand their impacts on model outputs. A range of CN values from 40-90 was selected to determine their effects on model results at the sub-catchment level during the historic flood year of 2010. The model simulated 362 cumecs of peak discharge for CN=90; however, for CN=40, the discharge reduced substantially to 78 cumecs (a 78.46% reduction). Event-based comparison of water volumes for different groups of CN values—90-75, 80-75, 75-70, and 90-40 —showed reductions in water availability of 8.88%, 3.39%, 3.82%, and 41.81%, respectively. Although it is known that the higher the CN, the greater the discharge from direct runoff and the less initial losses, the sensitivity analysis quantifies that impact and determines the amount of associated discharges with changing CN values. The results of the case study suggest that CN is one of the most influential parameters in the simulation of direct runoff. Knowledge of accurate runoff is important in both wet (flood management) and dry periods (water availability). A wide range in the resulting water discharges highlights the importance of precise CN selection. Sensitivity analysis is an essential facet of establishing hydrological models in limited data watersheds. The range of CNs demonstrates an enormous quantitative consequence on direct runoff, the exactness of which is necessary for effective water resource planning and management. The method itself is not novel, but the way it is proposed here can justify investments in determining the accurate CN before initiating mega projects involving rainfall-runoff simulations. Even a small error in CN value may lead to serious consequences. In the current study, the sensitivity analysis challenges the strength of the results of a model in the presence of ambiguity regarding CN value.

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Modeling Tuberculosis Among Healthcare Workers

Moroccan Journal of Pure and Applied Analysis ◽

10.2478/mjpaa-2019-0014 ◽

2019 ◽

Vol 5 (2) ◽

pp. 186-196

Author(s):

T.S. Faniran ◽

A.O. Falade ◽

T.O. Alakija

Keyword(s):

Sensitivity Analysis ◽

Equilibrium Point ◽

Healthcare Workers ◽

Compliance Rate ◽

Transmission Dynamics ◽

Infectious Period ◽

Model Parameters ◽

Asymptotically Stable ◽

Susceptible Population ◽

The Impact

AbstractA mathematical model for transmission dynamics of tuberculosis among healthcare workers is formulated. Tuberculosis is an airborne disease caused by Mycobacterium tuberculosis bacteria that affect the lungs of a host. Previous research had concentrated on mathematical modeling of transmission dynamics of tuberculosis without considering the impact of compliance rate to particulate respirator by healthcare workers on the transmission. Therefore, how compliance rate to particulate respirator reduces the transmission of tuberculosis is an active question, and we develop a new system of ordinary differential equations that explicitly explores the impact of compliance rate to particulate respirator by healthcare workers upon transmission. Rigorous analysis of the model shows that the disease-free equilibrium point is locally asymptotically stable when the basic reproduction number, Ro < 1. This is established through the analysis of characteristic equation. Basic reproduction, Ro is the number of new cases that an existing case generates on average over the infectious period in a susceptible population. We also show that the endemic equilibrium point is locally asymptotically stable for Ro > 1, by using Routh-Hurwitz criteria for stability. Sensitivity analysis is carried out to determine the relative importance of the model parameters to the disease transmission. The result of the sensitivity analysis shows that the most sensitive parameter is β (Human-to-human transmission rate), followed by Λ (Human recruitment rate). Also, the result shows that increase in ψ (compliance rate to particulate respirator by healthcare workers) leads to decrease in Ro which reduces tuberculosis spread among healthcare workers.

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