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May 21, 2026Proceedings of the National Academy of SciencesOpen Access

Detecting changepoints in dynamical systems: Modeling time-varying transmission of seasonal influenza

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Authors

AOAjay OzaKOKatie M. O’BrienJGJames P. Gleeson

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Overview

Randomized trial detects changes in seasonal influenza transmission dynamics, suggesting improved forecasting methods.

Key Points

  • This study aims to develop a framework for detecting changepoints in influenza transmission dynamics to improve epidemic forecasting.
  • Applied a piecewise constant function within a deterministic compartmental model.
  • Used hospitalized case data from four influenza A seasons in Ireland (2019/2020, 2022-2025).
  • Employed iterated filtering, kernel density estimation, and a stochastic search algorithm for changepoint detection.
  • Identified consistent changepoint patterns across influenza seasons, notable during increased social mixing times.
  • Developed a universal changepoint model enabling medium-term forecasting.
  • Demonstrated a robust method for capturing abrupt shifts in transmission dynamics.

Cite This Study

Oza et al. (2026) studied this question.

synapsesocial.com/papers/6a0ea1c1be05d6e3efb60890https://doi.org/10.1073/pnas.2533861123
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