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Inferring spatial source of disease outbreaks using maximum entropy.

Mehrad Ansari1, David Soriano-Paños2,3, Gourab Ghoshal4

  • 1Department of Chemical Engineering, University of Rochester, Rochester, New York 14627, USA.

Physical Review. E
|August 17, 2022
PubMed
Summary
This summary is machine-generated.

This study introduces a maximum entropy framework for disease outbreak modeling. It accurately predicts epidemic trajectories and infers infection origins, even with incomplete data.

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Area of Science:

  • Epidemiology
  • Mathematical Biology
  • Network Science

Background:

  • Mathematical modeling is crucial for predicting epidemic trajectories and informing policy decisions.
  • Inferring disease origins is challenging for current models due to data limitations.
  • Existing models are sensitive to noisy and incomplete epidemiological data.

Purpose of the Study:

  • To develop a robust framework for epidemiological modeling and origin inference.
  • To address limitations of current models in handling sparse and noisy data.
  • To provide calibrated probabilities for infection origins and reliable early-stage outbreak predictions.

Main Methods:

  • A maximum entropy framework was developed to fit epidemiological models.
  • The framework incorporates a prior belief model to enhance robustness against noise.
  • Model performance was evaluated using simulated data on synthetic and real-world networks.

Main Results:

  • The proposed framework accurately predicts future disease trajectories.
  • It provides calibrated probabilities for infection origins with high confidence.
  • The method successfully infers early disease dynamics and the epidemic seed, even at advanced stages.

Conclusions:

  • The maximum entropy framework offers a reliable approach for disease outbreak modeling, especially with limited data.
  • This method advances the capability of inferring disease origins and reconstructing early epidemic dynamics.
  • It demonstrates the potential for contact-tracing and origin identification beyond the initial outbreak phase.