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RLadyBug-An R package for stochastic epidemic models.

Michael Höhle1, Ulrike Feldmann1

  • 1Department of Statistics, University of Munich, Ludwigstr. 33, 80539 Munich, Germany.

Computational Statistics & Data Analysis
|April 15, 2020
PubMed
Summary
This summary is machine-generated.

RLadyBug is a new R package for simulating and analyzing epidemic models. It enables parameter estimation for the susceptible-exposed-infectious-recovered (SEIR) model using statistical inference methods.

Keywords:
MCMCRS4SEIR modelSIR modelStochastic modelling

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Area of Science:

  • Epidemiology
  • Computational Biology
  • Statistical Modeling

Background:

  • Stochastic epidemic models are crucial for understanding infectious disease outbreaks.
  • Accurate parameter estimation is essential for effective disease control and public health interventions.
  • Existing statistical software may lack comprehensive tools for complex epidemic model analysis.

Purpose of the Study:

  • To introduce RLadyBug, an R package designed for the simulation, visualization, and estimation of stochastic epidemic models.
  • To provide tools for parameter estimation in the susceptible-exposed-infectious-recovered (SEIR) model.
  • To facilitate statistical inference, including maximum likelihood and Bayesian methods, for transmission models.

Main Methods:

  • Development of an S4 package in R named RLadyBug.
  • Implementation of simulation and visualization functionalities for epidemic models.
  • Integration of maximum likelihood and Bayesian inference algorithms for parameter estimation.

Main Results:

  • RLadyBug enables the simulation and visualization of stochastic epidemic processes.
  • The package supports parameter estimation for the SEIR model through both maximum likelihood and Bayesian approaches.
  • It provides a framework for calculating confidence intervals and performing hypothesis testing.

Conclusions:

  • RLadyBug offers a valuable tool for researchers and public health professionals studying infectious disease dynamics.
  • The package advances the availability of statistical software for the analysis of transmission models.
  • It supports robust parameter estimation and statistical inference for epidemic modeling.