A Methodology For Performing Global Uncertainty And Sensitivity Analysis In Systems Biology
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This work develops methods for applying existing analytical tools to perform analyses on a variety of mathematical and computer models and provides a complete methodology for performing these analyses, in both deterministic and stochastic settings, and proposes novel techniques to handle problems encountered during these types of analyses.About:
This article is published in Journal of Theoretical Biology.The article was published on 2008-09-07 and is currently open access. It has received 2014 citations till now. The article focuses on the topics: Uncertainty analysis & Sensitivity analysis.read more
Citations
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A model of hydrology and water quality for stormwater control measures
TL;DR: A computer model that simulates the dynamic hydrologic and water quality processes of SCMs was developed and scenario testing showed that deeper SCMs have greater inorganic N removal efficiencies because they have more stored volume of relatively N-deplete water, and therefore have a greater capacity to dilute relativelyN-rich inflow.
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A sensitivity and uncertainty analysis of a continental-scale water quality model of pathogen pollution in African rivers
TL;DR: In this article, a sensitivity and uncertainty analysis using Latin hypercube sampling on the WorldQual water quality model was performed on a continental-scale pathogen river pollution model, and the analysis showed that only four parameters dominate output uncertainty over 93% of the study area, implying that model performance can be substantially improved by reducing the uncertainty of a small number of parameters.
Posted Content
Bifurcation Thresholds and Optimal Control in Transmission Dynamics of Arboviral Diseases
TL;DR: A model for the control of arboviral diseases which takes into account an imperfect vaccine combined with some other control measures already studied in the literature is derived and a Pontryagin’s maximum principle is used to characterize the optimal control.
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Effects of pesticide dose on Holling II predator-prey model with feedback control.
Jin Yang,Yuanshun Tan +1 more
TL;DR: The Holling II predator–prey system with pesticide dose response non-linear pulses demonstrates that the density of pest population not only can be controlled below the threshold but also can avoid some negative effects due to pesticide application, confirming the importance of biological control.
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Validation of a Stochastic Discrete Event Model Predicting Virus Concentration on Nurse Hands
TL;DR: A model was developed to predict virus concentration on nurses’ hands using data from a bacteriophage tracer study conducted in Tucson, Arizona, in an urgent care facility and Wilcoxon rank sum tests showed no significant differences in distributions of model‐predicted and experimentally measured virus concentrations on hands.
References
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Book
An Introduction to Multivariate Statistical Analysis
TL;DR: In this article, the distribution of the Mean Vector and the Covariance Matrix and the Generalized T2-Statistic is analyzed. But the distribution is not shown to be independent of sets of Variates.
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A comparison of three methods for selecting values of input variables in the analysis of output from a computer code
TL;DR: In this paper, two sampling plans are examined as alternatives to simple random sampling in Monte Carlo studies and they are shown to be improvements over simple sampling with respect to variance for a class of estimators which includes the sample mean and the empirical distribution function.
Journal Article
Factorial sampling plans for preliminary computational experiments
TL;DR: The proposed experimental plans are composed of individually randomized one-factor-at-a-time designs, and data analysis is based on the resulting random sample of observed elementary effects, those changes in an output due solely to changes in a particular input.
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Factorial sampling plans for preliminary computational experiments
TL;DR: In this article, the problem of designing computational experiments to determine which inputs have important effects on an output is considered, and experimental plans are composed of individually randomized one-factor-at-a-time designs, and data analysis is based on the resulting random sample of observed elementary effects.