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Inference of a Susceptible-Infectious stochastic model.

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Summary
This summary is machine-generated.

This study introduces a new statistical method to estimate parameters in epidemic models with time-varying infection rates. The generalized method of moments provides accurate estimations for complex infectious disease dynamics.

Keywords:
Estimating procedureGeneralized Method of MomentsTime inhomogeneous Wiener process

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Area of Science:

  • Epidemiology
  • Stochastic Processes
  • Statistical Inference

Background:

  • Susceptible-Infectious (SI) epidemic models describe disease spread where individuals do not develop lasting immunity.
  • Time-dependent transmission intensity is crucial for accurately modeling certain infectious diseases.
  • Classical inference methods are insufficient for diffusion processes with time-varying infinitesimal moments.

Purpose of the Study:

  • To develop a novel estimation procedure for parameters in time-inhomogeneous diffusion processes within SI epidemic models.
  • To address the limitations of traditional inference methods when dealing with time-dependent transmission intensity.
  • To provide a robust statistical framework for analyzing epidemic dynamics with evolving transmission rates.

Main Methods:

  • The study models epidemic dynamics using a time-inhomogeneous diffusion process, transformable into a nonhomogeneous Wiener process.
  • A generalized method of moments is employed for parameter estimation.
  • Simulation studies compare the proposed method with maximum likelihood estimation in both time-homogeneous and time-dependent scenarios, including periodic cases.

Main Results:

  • The proposed generalized method of moments effectively estimates parameters for time-inhomogeneous diffusion processes in SI models.
  • Simulation studies validate the procedure's accuracy, even with time-varying and periodic transmission intensity functions.
  • The method demonstrates superior performance compared to maximum likelihood estimation in certain time-dependent scenarios.

Conclusions:

  • A new, applicable statistical inference procedure is presented for SI epidemic models with time-dependent transmission.
  • The generalized method of moments offers a viable solution for parameter estimation in complex, evolving epidemic systems.
  • The methodology is successfully applied to a real-world dataset, demonstrating its practical utility in infectious disease analysis.