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On stochastic dynamic modeling of incidence data.

Emmanouil-Nektarios Kalligeris1,2, Alex Karagrigoriou2,3, Christina Parpoula4

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This study introduces a robust Markov Regime Switching Model for analyzing incidence rate data. It enhances epidemiological modeling by accurately capturing dynamic behaviors and reducing complexity in statistical analysis.

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Area of Science:

  • Epidemiology
  • Statistical Modeling
  • Biostatistics

Background:

  • Analyzing epidemiological incidence rate data requires robust models to capture dynamic behaviors.
  • Existing methods for modeling such data can be complex and lack robustness.

Purpose of the Study:

  • To propose and investigate a Markov Regime Switching Model of Conditional Mean with covariates for incidence rate data analysis.
  • To enhance the robustness and reduce the complexity of epidemiological data modeling.
  • To introduce a three-phase procedure for modeling incidence data.

Main Methods:

  • Utilized a Markov Regime Switching Model of Conditional Mean with covariates.
  • Employed penalized likelihood techniques and the Expectation Maximization algorithm for model component selection.
  • Applied Changepoint Detection Analysis for regime number selection, simplifying Likelihood Ratio Tests.
  • Tested a three-phase modeling procedure using real and simulated incidence data.

Main Results:

  • The proposed model demonstrates robustness in capturing dynamic behaviors of epidemiological data.
  • Changepoint Detection Analysis effectively reduces complexity in determining the number of regimes.
  • The three-phase procedure provides a validated framework for incidence data modeling.

Conclusions:

  • The Markov Regime Switching Model offers a robust and efficient approach for analyzing incidence rate data.
  • The integration of Changepoint Detection Analysis simplifies model selection and enhances interpretability.
  • The proposed methodology is effective for both real-world and simulated epidemiological datasets.