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Area of Science:

  • Epidemiology
  • Mathematical Modeling
  • Computational Statistics

Background:

  • The reproduction number (R) is a key indicator for epidemic spread.
  • Accurate estimation of R is crucial for monitoring and control strategies.
  • Existing methods may not fully capture complex spatio-temporal dynamics.

Purpose of the Study:

  • To develop a novel, robust estimation procedure for the reproduction number.
  • To enable simultaneous analysis of multiple regional epidemic time series.
  • To provide tools for dynamic visualization of epidemic spread.

Main Methods:

  • Convex optimization within a proximal-based inverse problem formulation.
  • Incorporation of constraints for piecewise smoothness.
  • Multivariate analysis with graph-based spatial regularization for time series.

Main Results:

  • Simulations demonstrate the effectiveness of the proposed estimation approach.
  • Comparative analysis of reproduction numbers across different countries.
  • Joint analysis of French departments revealing temporal co-evolution patterns.

Conclusions:

  • The proposed method offers an effective way to estimate and monitor the reproduction number.
  • The multivariate and spatial approach enhances understanding of epidemic dynamics.
  • Dynamic mapping provides valuable insights into regional epidemic interdependencies.