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A Bayesian nonparametric method for detecting rapid changes in disease transmission.

Richard Creswell1, Martin Robinson1, David Gavaghan1

  • 1Department of Computer Science, University of Oxford, Oxford, United Kingdom.

Journal of Theoretical Biology
|November 15, 2022
PubMed
Summary
This summary is machine-generated.

EpiCluster, a new Bayesian nonparametric method, identifies changes in the time-varying reproduction number (Rt) to track infectious disease outbreaks. It automatically detects shifts in transmission dynamics, informing public health policy.

Keywords:
Bayesian nonparametricsCOVID-19Changepoint detectionEpidemiologyOutbreaksReproduction number

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Area of Science:

  • Epidemiology
  • Statistical Modeling
  • Infectious Disease Dynamics

Background:

  • The time-varying reproduction number (Rt) is crucial for understanding infectious disease outbreak growth or dissipation.
  • Identifying changes in Rt following interventions provides evidence for disease transmission dynamics and informs policy.
  • Current methods may lack the flexibility to automatically detect and quantify shifts in Rt.

Purpose of the Study:

  • To present EpiCluster, a novel Bayesian nonparametric method for estimating shifts in Rt within a renewal model framework.
  • To develop a method that automatically detects changepoints in Rt, allowing for piecewise-constant assumptions.
  • To provide a measure of uncertainty in Rt and changepoint estimations.

Main Methods:

  • Utilizes a Bayesian nonparametric approach based on the Pitman-Yor process.
  • Assumes Rt is piecewise-constant, with changepoints determined by incidence data and priors.
  • Introduces a prior that promotes sparsity in the number of changepoints.

Main Results:

  • EpiCluster accurately detects rapid changes in transmission for synthetic data with known Rt profiles.
  • The method successfully identified changepoints coinciding with non-pharmaceutical interventions during COVID-19 outbreaks in Australia and Hong Kong.
  • Demonstrates automated, real-time or retrospective detection of Rt shifts.

Conclusions:

  • EpiCluster offers a fast, user-friendly method for automated detection of transmission changes.
  • Bayesian nonparametric models can adapt parameter complexity to data volume and complexity, suitable for epidemiology.
  • The method has wide applicability in epidemiological modeling, particularly for infectious disease surveillance and policy.