Literature DB >> 20950727

Methodology of the sensitivity analysis used for modeling an infectious disease.

Claire Okaïs1, Sylvain Roche, Marie-Laure Kürzinger, Benjamin Riche, Hélène Bricout, Tarik Derrough, François Simondon, René Ecochard.   

Abstract

Mathematical models may be used to help clarify dynamics of several infectious diseases. Because of the complexity of some models and the high degree of uncertainty in estimating many parameters, the present study proposes a rigorous framework for sensitivity analyses of mathematical models using as example a model to assess varicella and herpes zoster incidence. Its main steps are to assess the uncertainty of the factors to be studied, to evaluate qualitatively and quantitatively the impacts of these factors on model results, and to conduct an univariate and multivariate sensitivity analysis. The application of this technique may have considerable utility in the analysis of a wide variety of complex biological and epidemiological models.
Copyright © 2010 Elsevier Ltd. All rights reserved.

Entities:  

Mesh:

Year:  2010        PMID: 20950727     DOI: 10.1016/j.vaccine.2010.09.099

Source DB:  PubMed          Journal:  Vaccine        ISSN: 0264-410X            Impact factor:   3.641


  4 in total

Review 1.  Sensitivity analysis of infectious disease models: methods, advances and their application.

Authors:  Jianyong Wu; Radhika Dhingra; Manoj Gambhir; Justin V Remais
Journal:  J R Soc Interface       Date:  2013-07-17       Impact factor: 4.118

2.  Mathematical Modeling of "Chronic" Infectious Diseases: Unpacking the Black Box.

Authors:  Anthony T Fojo; Emily A Kendall; Parastu Kasaie; Sourya Shrestha; Thomas A Louis; David W Dowdy
Journal:  Open Forum Infect Dis       Date:  2017-08-14       Impact factor: 4.423

3.  Predictive validation of an influenza spread model.

Authors:  Ayaz Hyder; David L Buckeridge; Brian Leung
Journal:  PLoS One       Date:  2013-06-03       Impact factor: 3.240

4.  Active learning to understand infectious disease models and improve policy making.

Authors:  Lander Willem; Sean Stijven; Ekaterina Vladislavleva; Jan Broeckhove; Philippe Beutels; Niel Hens
Journal:  PLoS Comput Biol       Date:  2014-04-17       Impact factor: 4.475

  4 in total

北京卡尤迪生物科技股份有限公司 © 2022-2023.