Systematic comparison of epidemic growth patterns using two different estimation approaches.

ABSTRACT:

Background

Different estimation approaches are frequently used to calibrate mathematical models to epidemiological data, particularly for analyzing infectious disease outbreaks. Here, we use two common methods to estimate parameters that characterize growth patterns using the generalized growth model (GGM) calibrated to real outbreak datasets.

Materials and methods

Data from 31 outbreaks are used to fit the GGM to the ascending phase of each outbreak and estimate the parameters using both least squares (LSQ) and maximum likelihood estimation (MLE) methods. We utilize parametric bootstrapping to construct confidence intervals for parameter estimates. We compare the results including RMSE, Anscombe residual, and 95% prediction interval coverage. We also evaluate the correlatio

SUBMITTER: Lee Y 

PROVIDER: S-EPMC7691176 | biostudies-literature | 2021

REPOSITORIES: biostudies-literature