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Accurately summarizing an outbreak using epidemiological models takes time.

B K M Case1,2, Jean-Gabriel Young1,2,3, Laurent Hébert-Dufresne1,2

  • 1Vermont Complex Systems Center, University of Vermont, Burlington, VT 05405, USA.

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Summary
This summary is machine-generated.

Epidemiological models are crucial for understanding disease outbreaks, but parameter estimation can be unreliable. This study reveals that key outbreak statistics like reproductive number are often poorly identified, especially with limited data.

Keywords:
Bayesian statisticsepidemiological modellingpractical identifiability

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Area of Science:

  • Epidemiology
  • Mathematical Biology
  • Infectious Disease Modeling

Background:

  • Recent outbreaks (Mpox, Ebola, COVID-19, influenza, RSV) increased reliance on epidemiological models.
  • Estimating key parameters from these models faces practical identifiability (PI) challenges.

Purpose of the Study:

  • Investigate the practical identifiability of eight common statistics from the susceptible-infectious-recovered model.
  • Introduce a novel measure to quantify learning in Bayesian analysis of prevalence data.

Main Methods:

  • Employed a new measure to assess practical identifiability (PI) in epidemiological models.
  • Analyzed the PI of eight statistics within the susceptible-infectious-recovered (SIR) model framework.
  • Utilized Bayesian analysis of prevalence data.

Main Results:

  • Basic reproductive number and final outbreak size are frequently poorly identified.
  • Peak intensity, peak timing, and initial growth rate show better identifiability.
  • PI is particularly problematic for slow-growing or less-severe outbreaks.

Conclusions:

  • Inferences from epidemiological models may be unreliable with limited data.
  • The reliability of parameter estimation varies significantly across different outbreak statistics.
  • Further research is needed to improve the robustness of epidemiological modeling.