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Estimation of under-reporting in epidemics using approximations.

Kokouvi Gamado1, George Streftaris2, Stan Zachary2

  • 1Biomathematics and Statistics Scotland, Edinburgh, UK. Kokouvi.Gamado@bioss.ac.uk.

Journal of Mathematical Biology
|October 28, 2016
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Summary

Under-reporting in epidemics can lead to inaccurate infection rate and reproduction number estimates. This study introduces faster Bayesian methods using approximations for improved epidemic modeling and analysis.

Keywords:
ApproximationsFinal size distributionMarkov chain Monte CarloReversible jumpStochastic SIR modelUnder-reporting

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Area of Science:

  • Epidemiology
  • Biostatistics
  • Computational Biology

Background:

  • Under-reporting in epidemics leads to under-estimation of infection rates and reproduction numbers.
  • Accurate epidemic assessment is crucial for effective public health interventions.
  • Bayesian data augmentation is a common approach for handling under-reporting in temporal epidemic data.

Purpose of the Study:

  • To develop faster estimation methods for epidemic data with under-reporting.
  • To compare novel approximation-based Markov chain Monte Carlo (MCMC) methods with existing Reversible Jump MCMC (RJMCMC) techniques.
  • To provide efficient inference tools for large-scale epidemic analysis.

Main Methods:

  • Utilized data augmentation techniques within a Bayesian framework.
  • Developed and applied approximation-based simple MCMC methods.
  • Compared the performance of new methods against traditional RJMCMC approaches.

Main Results:

  • Approximation-based MCMC methods provide faster estimations compared to RJMCMC.
  • The proposed methods offer a good balance between computational time and accuracy.
  • These techniques are particularly suitable for analyzing large epidemics.

Conclusions:

  • Approximation-based Bayesian inference offers a valuable and efficient alternative for epidemic modeling with under-reporting.
  • The developed methods enhance the speed of analysis without significant loss of accuracy.
  • This research contributes to more robust and timely epidemic surveillance and control strategies.