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Sensitivity of Model-Based Epidemiological Parameter Estimation to Model Assumptions

  • A.L. Lloyd
Chapter

Abstract

Estimation of epidemiological parameters from disease outbreak data often proceeds by fitting a mathematical model to the data set. The resulting parameter estimates are subject to uncertainty that arises from errors (noise) in the data; standard statistical techniques can be used to estimate the magnitude of this uncertainty. The estimates are also dependent on the structure of the model used in the fitting process and so any uncertainty regarding this structure leads to additional uncertainty in the parameter estimates. We argue that if we lack detailed knowledge of the biology of the transmission process, parameter estimation should be accompanied by a structural sensitivity analysis, in addition to the standard statistical uncertainty analysis. Here we focus on the estimation of the basic reproductive number from the initial growth rate of an outbreak as this is a setting in which parameter estimation can be surprisingly sensitive to details of the time course of infection.

Keywords

Latent Period Basic Reproduction Number Severe Acute Respiratory Syndrome Infectious Period Initial Growth Rate 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer Science+Business Media B.V. 2009

Authors and Affiliations

  • A.L. Lloyd
    • 1
  1. 1.Biomathematics Graduate Program and Department of MathematicsNorth Carolina State UniversityRaleighUSA

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