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Phage Phenomics: Physiological Approaches to Characterize Novel Viral Proteins
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Systematic comparison of epidemic growth patterns using two different estimation approaches.

Yiseul Lee1, Kimberlyn Roosa1,2, Gerardo Chowell1,3

  • 1Department of Population Health Sciences, School of Public Health, Georgia State University, Atlanta, GA, 30303, USA.

Infectious Disease Modelling
|December 9, 2020
PubMed
Summary

Least squares (LSQ) and maximum likelihood estimation (MLE) methods provide similar results for generalized growth model (GGM) parameter estimation in infectious disease outbreaks. Good model fit to early outbreak data is key for consistent epidemic growth pattern characterization.

Keywords:
Epidemiological modelsGeneralized growth modelLeast squares estimationMaximum likelihood estimationParameter estimation

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Area of Science:

  • Epidemiology
  • Mathematical Modeling
  • Statistical Analysis

Background:

  • Mathematical models are crucial for analyzing infectious disease outbreaks.
  • Various estimation approaches calibrate these models to epidemiological data.
  • The generalized growth model (GGM) is used to characterize epidemic growth patterns.

Purpose of the Study:

  • To compare parameter estimation for the GGM using least squares (LSQ) and maximum likelihood estimation (MLE).
  • To assess the reliability of these methods in characterizing epidemic growth patterns using real outbreak data.

Main Methods:

  • Fitted the GGM to the ascending phase of 31 real outbreak datasets.
  • Employed LSQ and MLE for parameter estimation.
  • Utilized parametric bootstrapping for confidence intervals and compared RMSE, Anscombe residuals, and prediction interval coverage.

Main Results:

  • LSQ and MLE yielded similar parameter estimates, RMSE, and prediction interval coverage across most outbreaks.
  • Parameter estimates were comparable when the GGM showed a good fit to the early growth phase.
  • Systematic deviations in model fit were observed in two outbreaks, leading to differing parameter estimates.

Conclusions:

  • LSQ and MLE are comparable methods for GGM parameter estimation in epidemic growth analysis.
  • The consistency of results depends on the model's adequate fit to the early phase of the outbreak data.