Model Validation and Assessment of Uncertainty in a Deterministic Model for Gonorrhea (1995)
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BibTeX
@MISC{Givens95modelvalidation,
author = {Geof H. Givens and James P. Hughes},
title = {Model Validation and Assessment of Uncertainty in a Deterministic Model for Gonorrhea},
year = {1995}
}
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Abstract
Deterministic mathematical models of disease transmission have been used to forecast the course of epidemics and to investigate how various hypothetical interventions or changes in assumptions would alter these predictions. The inputs for such models include parameters such as rates of partner change and rates of disease transmission, and the model outputs are usually predicted incidence rates for the study population and for various subpopulations in each year of the simulation. Considerable uncertainty may exist about the correct values of both type of parameters. Model validation (adjusting parameters by fitting the model to real data) is often done in an iterative, ad-hoc fashion and relatively little work has been done on the difficult issue of attaching a measure of uncertainty to the predictions of these models. We describe a Bayesian approach to estimation, fitting and uncertainty analysis in complex, nonlinear deterministic models. The sampling importance resampling algorithm ...
Keyphrases
model validation deterministic model disease transmission partner change difficult issue various hypothetical intervention deterministic mathematical model correct value ad-hoc fashion model output incidence rate real data little work nonlinear deterministic model various subpopulation uncertainty analysis study population considerable uncertainty bayesian approach
