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Determining disease attributes from epidemic trajectories.

Mark P Rast1, Luke I Rast1

  • 1Department of Astrophysical and Planetary Sciences, Laboratory for Atmospheric and Space Physics, University of Colorado, Boulder, 80309, CO, USA.

Infectious Disease Modelling
|January 15, 2026
PubMed
Summary
This summary is machine-generated.

Early inference of infectious disease properties is crucial for public health. This study shows that monitoring epidemic trajectories can accurately determine key disease attributes like infectiousness and duration distributions.

Keywords:
Disease attribute kernelsInfectious disease epidemiologyInverse problemPoisson generalized linear modelStochastic SIR model

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Area of Science:

  • Epidemiology
  • Mathematical Biology
  • Public Health

Background:

  • Effective public health strategies rely on timely and accurate understanding of infectious disease characteristics.
  • Inferring disease properties from observed epidemic data is essential for outbreak response.

Purpose of the Study:

  • To assess the feasibility of inferring infectious disease attributes from stochastic epidemic trajectories.
  • To evaluate the recoverability of population mean infectiousness and infection duration distributions.

Main Methods:

  • Construction of stochastic Kermack-McKendrick model trajectories.
  • Application of Poisson Generalized Linear Model (GLM) regression for integral kernel estimation.
  • Inversion techniques applied to simulated epidemic data with and without observational error.

Main Results:

  • Infectious disease attributes, including population mean infectiousness and infection duration/survival distributions, are recoverable from epidemic trajectories.
  • Both multi-trajectory and regularized single-trajectory inversions yield accurate results.
  • Recovered distributions enable solving for individual infectiousness profiles under self-similarity assumptions.

Conclusions:

  • Aggressive monitoring of stochastic epidemic evolution in well-mixed populations allows for determination of critical disease spread characteristics.
  • This approach supports early and reliable inference of novel infectious disease attributes.
  • Findings have implications for proactive infectious disease management and control strategies.