scholarly journals A SYSTEM FOR THE AUTOMATION OF ONE-AT-A-TIME SENSITIVITY ANALYSIS IN ODE SYSTEMS

2020 ◽  
Vol 5 (2) ◽  
Author(s):  
Matheus Fajardo Galvão ◽  
Marcelo Lobosco
Keyword(s):  
Sensitivity Analysis ◽  
Large Volume ◽  
Single Parameter ◽  
Identification Of Parameters ◽  
Time Sensitivity ◽  
Perform Sensitivity Analysis ◽  
Parameter Values ◽  
The Relationship ◽  
Large Model ◽  
Ode Systems

Sensitivity analysis is a widely used tool in computing modeling, being used fordistinct purposes, such as a better understanding of the relationship between parameter values in amathematical model and its results, and the identification of parameters whose change in their valuesimplies in a larger variation in the results of a model, among others purposes. Several approachescan be used to perform sensitivity analysis. One of the simplest is one-at-a-time, which analyzes theimpact of changing a single parameter on a model while keeping the others one fixed. Depending onthe number of parameters and the variations adopted, the sensitivity analysis of a large model maygenerate a large volume amount of results and it can be hard to identify the most sensitive parametersof the model. This paper presents a tool that automates the identification of these parameters.

Time-varying sensitivity analysis of an influenza model with interventions

2021 ◽  
Author(s):  
S. A. Pedro ◽  
H. Rwezaura ◽  
J. M. Tchuenche
Keyword(s):  
Sensitivity Analysis ◽  
De Novo ◽  
Control Strategies ◽  
Health Planning ◽  
Critical Threshold ◽  
State Variables ◽  
Treatment Rate ◽  
Epidemic Size ◽  
Parameter Values ◽  
The Relationship

We formulate an influenza model with treatment and vaccination. Both time invariant and time-dependent uncertainty analyses and sensitivity analysis of the model parameter values are carried out to understand the dependence of the reproduction numbers and model state variables on their components. Results show that the relationship between treatment and epidemic size is nonlinear and that there exists a critical threshold treatment rate under which treatment is beneficial. Sensitivity analysis suggests that the most significant parameters are those related to infection transmission, infectiousness, duration of infectiousness and waning immunity. Further, there are important instances when the relationship between some parameters and model outputs changes behavior from negatively to positively correlated or vice versa because all sensitivity indices, except [Formula: see text] are functions of other parameters and thus will change with the change in parameter values. For example, treatment helps to lower the epidemic size, but may then become a “source” of infection likely due to resistance de novo. This knowledge is critical for proper public health planning and guidance of control strategies.

Modelling of Nitrification at the Mogden Treatment Works

1989 ◽  
Vol 21 (4-5) ◽  
pp. 305-314
Author(s):  
J. P. Lumbers ◽  
S. C. Cook ◽  
G. A. Thomas
Keyword(s):  
Sensitivity Analysis ◽  
Monte Carlo Simulation ◽  
River Basin Management ◽  
Independent Data ◽  
Activated Sludge Process ◽  
Basin Management ◽  
Alternative Control ◽  
Control Actions ◽  
Parameter Values ◽  
Generalised Sensitivity Analysis

An application of a dynamic model of the activated sludge process is described within the context of real-time river basin management. The model has been calibrated and validated on independent data and then applied to investigate losses of nitrification at the Mogden Works. Monte Carlo simulation and generalised sensitivity analysis were found to be effective ways of identifying appropriate parameter values and their importance. The prediction of unmeasured states such as the autotroph population enabled the effects of alternative control actions to be better understood and the most suitable measures found.

Kriging-Based Reliability Analysis and Variance-Based Sensitivity Analysis for an Idler Shaft

2015 ◽  
Author(s):  
Weimin Cui ◽  
Wei Guo ◽  
Zhongchao Sun ◽  
Tianxiang Yu
Keyword(s):  
Sensitivity Analysis ◽  
Influence Factors ◽  
Random Variables ◽  
Kriging Model ◽  
Element Analysis ◽  
Performance Function ◽  
The Finite Element Analysis ◽  
Inner Diameter ◽  
Sensitivity Method ◽  
The Relationship

In order to analyze the reason of failure and improve the reliability of the idler shaft, this paper studies the reliability and sensitivity for the idler shaft based on Kriging model and Variance Methods respectively. The finite element analysis (FEA) of idler shaft is studied in ABAQUS firstly. Then, combining the performance function and various random variables, the Kriging model of idler shaft is established and verified. Based on Kriging model which has been established, the relationship between random variables and the response value is studied, and the function reliability is calculated which explains why the failure of the idler shaft occurred frequently in service. Finally, the variance-based sensitivity method is used for sensitivity analysis of influence factors, the result shows that the reliability of idler shaft is sensitive to the inner diameter of body A and inner diameter of body B, which could contribute for the analysis and further improvement of idler shaft.

Establishing a Detection Threshold for Acoustic-Based External Leak Detection Systems

2020 ◽  
Author(s):  
Mathew Bussière ◽  
Mark Stephens ◽  
Marzie Derakhshesh ◽  
Yue Cheng ◽  
Lorne Daniels
Keyword(s):  
Sensitivity Analysis ◽  
Detection Threshold ◽  
Leak Detection ◽  
Detection Sensitivity ◽  
Sensitivity Threshold ◽  
Detection Systems ◽  
Threshold Measure ◽  
Full Scale Tests ◽  
Parameter Values ◽  
Very High

Abstract A better understanding of the sensitivity threshold of external leak detection systems can assist pipeline operators in predicting detection performance for a range of possible leak scenarios, thereby helping them to make more informed decisions regarding procurement and deployment of such systems. The analysis approach described herein was developed to characterize the leak detection sensitivity of select fiber optic cable-based systems that employ Distributed Acoustic Sensing (DAS). The detection sensitivity analysis consisted of two steps. The first step involved identifying a suitable release parameter capable of providing a defensible basis for defining detection sensitivity; the second step involved the application of logistic regression analysis to characterize detection sensitivity as a function of the chosen release parameter. The detection sensitivity analysis described herein provides a means by which to quantitatively determine the leak detection sensitivity threshold for each technology and sensor deployment position evaluated in a set of full-scale tests. The chosen sensitivity threshold measure was the release parameter value associated with release events having a 90% probability of being detected. Thresholds associated with a higher probability level of 95% were also established for comparison purposes. The calculated sensitivity thresholds can be interpreted to mean that release events associated with release parameter values above the sensitivity threshold have a very high likelihood (either 90 or 95%) of being detected.

Sensitivity Analysis

2021 ◽  
pp. 251-262
Author(s):  
Timothy E. Essington
Keyword(s):  
Sensitivity Analysis ◽  
Parameter Uncertainty ◽  
Monte Carlo Analysis ◽  
Interactive Effects ◽  
Structure Parameter ◽  
Ecological Processes ◽  
Individual Parameter ◽  
Global Sensitivity ◽  
Versus Parameter ◽  
Parameter Values

The chapter “Sensitivity Analysis” reviews why sensitivity analysis is a critical component of mathematical modeling, and the different ways of approaching it. A sensitivity analysis is an attempt to identify the parts of the model (i.e. structure, parameter values) that are most important for governing the output. It is an important part of modeling because it is used to quantify the degree of uncertainty in the model prediction and, in many cases, is the main goal of the model (i.e. the model was developed to identify the most important ecological processes). The chapter covers the idea of “local” versus “global” sensitivity analysis via individual parameter perturbation, and how interactive effects of parameters can be revealed via Monte Carlo analysis. Structural versus parameter uncertainty is also explained and explored.

scholarly journals A Bayesian Sensitivity Analysis to Partition Body Mass Index Into Components of Body Composition: An Application to Head and Neck Cancer Survival

2019 ◽  
Vol 188 (11) ◽  
pp. 2031-2039
Author(s):  
Patrick T Bradshaw ◽  
Jose P Zevallos ◽  
Kathy Wisniewski ◽  
Andrew F Olshan
Keyword(s):  
Body Mass Index ◽  
Sensitivity Analysis ◽  
Body Composition ◽  
Head And Neck Cancer ◽  
Head And Neck ◽  
Body Mass ◽  
Neck Cancer ◽  
Hazard Ratio ◽  
Unit Increase ◽  
The Relationship

Abstract Previous studies have suggested a “J-shaped” relationship between body mass index (BMI, calculated as weight (kg)/height (m)2) and survival among head and neck cancer (HNC) patients. However, BMI is a vague measure of body composition. To provide greater resolution, we used Bayesian sensitivity analysis, informed by external data, to model the relationship between predicted fat mass index (FMI, adipose tissue (kg)/height (m)2), lean mass index (LMI, lean tissue (kg)/height (m)2), and survival. We estimated posterior median hazard ratios and 95% credible intervals for the BMI-mortality relationship in a Bayesian framework using data from 1,180 adults in North Carolina with HNC diagnosed between 2002 and 2006. Risk factors were assessed by interview shortly after diagnosis and vital status through 2013 via the National Death Index. The relationship between BMI and all-cause mortality was convex, with a nadir at 28.6, with greater risk observed throughout the normal weight range. The sensitivity analysis indicated that this was consistent with opposing increases in risk with FMI (per unit increase, hazard ratio = 1.04 (1.00, 1.08)) and decreases with LMI (per unit increase, hazard ratio = 0.90 (0.85, 0.95)). Patterns were similar for HNC-specific mortality but associations were stronger. Measures of body composition, rather than BMI, should be considered in relation to mortality risk.

scholarly journals Parameter sensitivity analysis on a mathematical model of reepithelialization

2019 ◽  
Vol 5 (1) ◽  
pp. 81-84
Author(s):  
Jacquelyn Dawn Parente ◽  
Knut Möller ◽  
Bala Amala Kannan ◽  
Sabine Hensler ◽  
Claudia Kuhlbach ◽  
...  
Keyword(s):  
Mathematical Model ◽  
Sensitivity Analysis ◽  
Wound Healing ◽  
Chronic Wounds ◽  
Parameter Sensitivity ◽  
Parameter Sensitivity Analysis ◽  
Adhesion Coefficient ◽  
Optimal Distance ◽  
Parameter Values

AbstractReepithelialization is the single requirement to define a wound as healed when the barrier function of the skin is restored. An existing reepithelialization mathematical model (RM) simulates wound healing in vitro. This work performs a parameter sensitivity analysis on an existing RM to see how robust the model is for changing wound healing rates for application to chronic wounds (inhibition) and wound healing therapies (activation). The existing RM balances the optimal distance between cells and basal membrane segments (BMs) according to the calculation of intercellular pressure and adhesion force. The RM mimics cell behavior and their interaction by passive migration, which is the displacement of cells from its initial position. First, this work reproduces the RM. The initial case recreates the interaction of a cell with its surrounding cells, while the second case recreates the interaction of the cell with its nearest BMs. These two cases were implemented in MATLAB to estimate optimal distance, intercellular pressure, an adhesive force between cells and the BMs. The analysis computes movement vectors and new positions of each cell at different time steps. Parameter sensitivity analysis was then conducted on the adhesion coefficient, where the original value in the RM was unknown. The results obtained at the assumed original parameter values are similar to the existing RM. As a result of the parameter sensitivity analysis, increasing the adhesion coefficient increases cell movement. High basal adhesion causes passive movement of cells, which in the simulation results is seen as a cellular movement towards wound closure. The existing RM is robust to changing adhesion coefficient values which change the rate of the advancing reepithelialization front. Future work includes fitting adhesion coefficient parameter values to an in vitro wounded tissue visualized by live dyes in treatment therapy experiments.

scholarly journals A Simple Time-Varying Sensitivity Analysis (TVSA) for Assessment of Temporal Variability of Hydrological Processes

Water ◽  
2020 ◽  
Vol 12 (9) ◽  
pp. 2463 ◽  
Author(s):  
Yelena Medina ◽  
Enrique Muñoz
Keyword(s):  
Sensitivity Analysis ◽  
Temporal Variability ◽  
Time Windows ◽  
Computational Cost ◽  
Time Varying ◽  
Moving Window ◽  
Time Sensitivity ◽  
Annual Time ◽  
Low Computational Cost ◽  
High Computational Cost

Time-varying sensitivity analysis (TVSA) allows sensitivity in a moving window to be estimated and the time periods in which the specific components of a model can affect its performance to be identified. However, one of the disadvantages of TVSA is its high computational cost, as it estimates sensitivity in a moving window within an analyzed series, performing a series of repetitive calculations. In this article a function to implement a simple TVSA with a low computational cost using regional sensitivity analysis is presented. As an example of its application, an analysis of hydrological model results in daily, monthly, and annual time windows is carried out. The results show that the model allows the time sensitivity of a model with respect to its parameters to be detected, making it a suitable tool for the assessment of temporal variability of processes in models that include time series analysis. In addition, it is observed that the size of the moving window can influence the estimated sensitivity; therefore, analysis of different time windows is recommended.

scholarly journals Relationship of Obstructive Sleep Apnoea with Diabetic Retinopathy: A Meta-Analysis

2017 ◽  
Vol 2017 ◽  
pp. 1-5 ◽  
Author(s):  
Zhenliu Zhu ◽  
Fengying Zhang ◽  
Yunxia Liu ◽  
Shuqin Yang ◽  
Chunting Li ◽  
...  
Keyword(s):  
Diabetes Mellitus ◽  
Sensitivity Analysis ◽  
Diabetic Retinopathy ◽  
Publication Bias ◽  
Meta Analysis ◽  
Sleep Apnoea ◽  
Obstructive Sleep ◽  
Increased Risk ◽  
Relationship Of ◽  
The Relationship

Until now, the relationship of obstructive sleep apnoea (OSA) with diabetic retinopathy (DR) was controversial. This meta-analysis was performed to obtain definitive conclusion on this topic. Relevant articles were searched on databases of Pubmed, Google Scholar, and Chinese National Knowledge Infrastructure (CNKI). The articles were selected according to inclusion and exclusion criteria. Odds ratio (OR) with 95% confidence interval (CI) was used to evaluate the relationship of OSA with risk of DR.I2andPvalue were used to assess the presence of heterogeneity.I2≥ 50% orP<0.05indicated significant heterogeneity. Sensitivity analysis was performed to evaluate the robustness of pooled results. Begg’s funnel plot and Egger’s regression analysis were adopted to assess publication bias. 6 eligible studies were selected in the present meta-analysis. The pooled results indicated that OSA was significantly associated with increased risk of DR (OR = 2.01, 95% CI = 1.49–2.72). Subgroup analysis based on type of diabetes mellitus suggested that OSA was related to DR in both Type 1 and Type 2 diabetes mellitus. Sensitivity analysis demonstrated that pooled results were robust. No significant publication bias was observed (P=0.128). The results indicate that OSA is related to increased risk of DR.

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