Assessing parameter identifiability in compartmental dynamic models using a computational approach: application to infectious disease transmission models.

ABSTRACT:

Background

Mathematical modeling is now frequently used in outbreak investigations to understand underlying mechanisms of infectious disease dynamics, assess patterns in epidemiological data, and forecast the trajectory of epidemics. However, the successful application of mathematical models to guide public health interventions lies in the ability to reliably estimate model parameters and their corresponding uncertainty. Here, we present and illustrate a simple computational method for assessing parameter identifiability in compartmental epidemic models.

Methods

We describe a parametric bootstrap approach to generate simulated data from dynamical systems to quantify parameter uncertainty and identifiability. We calculate confidence intervals and mean squared error of estimat

SUBMITTER: Roosa K 

PROVIDER: S-EPMC6332839 | biostudies-literature | 2019 Jan

REPOSITORIES: biostudies-literature