Mathematical modeling | Mathematical analysis | Sensitivity analysis
Sensitivity analysis studies the relation between the uncertainty in a model-based the inference and the uncertainties in the model assumptions. Sensitivity analysis can play an important role in epidemiology, for example in assessing the influence of the unmeasured confounding on the causal conclusions of a study. It is also important in all mathematical modelling studies of epidemics. Sensitivity analysis can be used in epidemiology, for example in assessing the influence of the unmeasured confounding on the causal conclusions of a study. The use of sensitivity analysis in mathematical modelling of infectious disease is suggested in on the Coronavirus disease 2019 outbreak. Given the significant uncertainty at play, the use of sensitivity analysis to apportion the output uncertainty into input parameters is crucial in the context of Decision-making. Examples of applications of sensitivity analysis to modelling of COVID-19 are and. in particular, the time of intervention time in containing the pandemic spread is identified as a key parameter. (Wikipedia).
Overview of various methods for sensitivity analysis in the UQ of subsurface systems
From playlist Uncertainty Quantification
Chapter 12 Sensitivity Specificity Predictive Values Odds Ratios
Ever wandered how to calculate sensitivity, specificity, positive and negative predictive values or odds ratios or even simply what these terms mean? Watch this short lecture.
From playlist Medical Statistics
Sensitivity (Electrical Engineering)
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From playlist Advanced Circuit Design
Sensitivity Specificity PPV and NPV
We end of this series by looking at the accuracy and use of medical tests through the concepts of sensitivity, specificity,positive and negative predictive values.
From playlist Learning medical statistics with python and Jupyter notebooks
04-2 Sensitivity Analysis Global
Sobol' and regionalized sensitivity analysis
From playlist QUSS GS 260
Muin Khoury on Precision Public Health in the Era of Precision Medicine
Muin J. Khoury MD, PhD, from the CDC Office of Public Health Genomics, delivers the keynote address at the 2015 Stanford Center for Population Health Sciences Annual Colloquium. He discusses some of the challenges of precision medicine, including how your zip code is more important for you
From playlist Stanford Population Health Sciences
City-Scale Simulation Model of COVID-19 Spread
Forecasting models for COVID-19 at the national and state levels have been common, but few have accounted for SARS-CoV-2 transmission at city or smaller scales. In this talk, we describe two complementary Mathematica epidemiological simulation models—one an agent-based model (ABM), the oth
From playlist Wolfram Technology Conference 2021
Prob & Stats - Bayes Theorem (2 of 24) What is the Sensitivity of a Test?
Visit http://ilectureonline.com for more math and science lectures! In this video I will explain what is and give examples of the sensitivity of a test. The sensitivity of a test indicates the probability that the subject will have a POSITIVE result when the subject is actually POSITIVE.
From playlist PROB & STATS 4 BAYES THEOREM
Epidemiological Models for Influenza and COVID-19
Dr. Robert Nachbar who is a project director in Wolfram Solutions, discusses spread of an infectious virus within a population using compartmental models. Bob got his doctorate of organic chemistry Brown University, and received the Wolfram Innovator Award in 2012. He joined Wolfram Soluti
From playlist Coronavirus Datasets and Analysis in the Wolfram Language
04-3 Sensitivity Analysis Trees
Sensitivity analysis using classification and regression trees
From playlist QUSS GS 260
Stanford Seminar - Computational Epidemiology: The Role of Big Data and Pervasive Informatics
"Computational Epidemiology: The role of big data and pervasive informatics" - Madhav Marathe of Virginia Tech Colloquium on Computer Systems Seminar Series (EE380) presents the current research in design, implementation, analysis, and use of computer systems. Topics range from integrated
From playlist Engineering
Understanding the relationship between the epidemiology of, and immune ... by Rebecca Chisholm
DISCUSSION MEETING : MATHEMATICAL AND STATISTICAL EXPLORATIONS IN DISEASE MODELLING AND PUBLIC HEALTH ORGANIZERS : Nagasuma Chandra, Martin Lopez-Garcia, Carmen Molina-Paris and Saumyadipta Pyne DATE & TIME : 01 July 2019 to 11 July 2019 VENUE : Madhava Lecture Hall, ICTS, Bangalore
From playlist Mathematical and statistical explorations in disease modelling and public health
Mathematical and Statistical modelling in the fight to control Tuberculosis by Emma McBryde
DISCUSSION MEETING : MATHEMATICAL AND STATISTICAL EXPLORATIONS IN DISEASE MODELLING AND PUBLIC HEALTH ORGANIZERS : Nagasuma Chandra, Martin Lopez-Garcia, Carmen Molina-Paris and Saumyadipta Pyne DATE & TIME : 01 July 2019 to 11 July 2019 VENUE : Madhava Lecture Hall, ICTS, Bangalore
From playlist Mathematical and statistical explorations in disease modelling and public health
Public Health Seminar. Structural Considerations in Social Epidemiologic Analysis.
Recorded October 11, 2013. Jay Kaufman, Ph.D.
From playlist Public Health: Graduate Seminars (2013 - 2015)
Why COVID-19 Models Are Often Wrong
To understand what epidemiological models can tell us, it helps to first understand what they can’t. In this explainer, we break down how epidemiological models are built and dispel some of the common misunderstandings about their applications. Read the full article at Quanta Magazine: ht
From playlist Explainers
Yuseob Kim - Adaptive evolution in pathogen populations under complex demographic dynamics
PROGRAM: School and Discussion Meeting on Population Genetics and Evolution PROGRAM LINK: http://www.icts.res.in/program/PGE2014 DATES: Saturday 15 Feb, 2014 - Monday 24 Feb, 2014 VENUE: Physics Auditorium, IISc, Bangalore Just as evolution is central to our understanding of biology, p
From playlist School and Discussion Meeting on Population Genetics and Evolution
14 Data Analytics: Indicator Methods
Lecture on the use of indicators for spatial estimation and simulation.
From playlist Data Analytics and Geostatistics