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Spatially explicit effective reproduction numbers from incidence and mobility data.

Cristiano Trevisin1, Enrico Bertuzzo2, Damiano Pasetto2

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This study introduces a new method to estimate the effective reproduction number, accounting for population movement. This improves real-time disease transmission tracking in connected communities.

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COVID-19disease generation intervalhuman mobilityinfection spreading mechanismsparticle filtering

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Area of Science:

  • Epidemiology
  • Mathematical Biology
  • Computational Statistics

Background:

  • Current methods for estimating effective reproduction numbers often ignore population mobility.
  • This oversight can lead to misrepresentation of infection spread in spatially connected networks (metapopulations).

Purpose of the Study:

  • To derive and implement a novel framework for estimating spatially explicit effective reproduction numbers.
  • To develop a Bayesian particle filtering tool for real-time estimation of these numbers.

Main Methods:

  • Derivation of renewal equations incorporating a connection matrix for mobility and containment.
  • Application of a Bayesian particle filtering approach to estimate spatially explicit effective reproduction numbers.
  • Validation using synthetic data and real-world COVID-19 surveillance data from Italy.

Main Results:

  • The proposed method explicitly models mobility fluxes between communities.
  • Spatially explicit effective reproduction numbers (ℛ(t)) were estimated for individual communities.
  • Comparison with disconnected models revealed potential improvements in disease transmission estimation.

Conclusions:

  • Accounting for mobility is crucial for accurate real-time estimation of disease transmission.
  • The developed Bayesian particle filtering tool offers a more robust approach to estimating effective reproduction numbers in metapopulations.
  • This methodology can enhance public health strategies by providing a more nuanced understanding of epidemic dynamics.