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Incorporating Time-Dose-Response into Legionella Outbreak Models.

Bidya Prasad1, Kerry A Hamilton1, Charles N Haas1

  • 1Department of Civil, Architectural, and Environmental Engineering, Drexel University, Philadelphia, PA, USA.

Risk Analysis : an Official Publication of the Society for Risk Analysis
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Summary

This study introduces a new model for legionellosis outbreaks, integrating host-pathogen dynamics. The novel approach enhances understanding of disease transmission and improves outbreak prediction accuracy.

Keywords:
Epidemic modelingin vivo kinetics; Legionellamathematical epidemiologyoutbreak modeltime-dose response

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Area of Science:

  • Epidemiology
  • Infectious Disease Modeling
  • Microbiology

Background:

  • Legionellosis outbreaks pose significant public health risks.
  • Existing models often lack detailed host-pathogen dynamics and time-dependent factors.
  • Understanding in vivo host-pathogen interactions is crucial for accurate outbreak prediction.

Purpose of the Study:

  • To develop a novel, robust outbreak model for legionellosis by incorporating in vivo host-pathogen dynamics.
  • To generate and evaluate dose-response and time-dose-response (TDR) models for Legionella longbeachae.
  • To apply the best-fit TDR model to real-world L. pneumophila outbreak scenarios.

Main Methods:

  • Utilized maximum likelihood estimation to generate dose-response and TDR models for Legionella longbeachae in mice.
  • Incorporated the best-fit TDR model into L. pneumophila outbreak models for a spa in Japan and an aquarium in Melbourne.
  • Tested the TDR model against various incubation distributions and non-time-dependent models.

Main Results:

  • The beta-Poisson with exponential-reciprocal dependency model best-fit the murine dosing study (minimized deviance of 32.9).
  • This TDR model performed consistently well in the Japan outbreak (deviances 32-35).
  • Time-dependent models showed low minimized deviances (around 8) in the Melbourne outbreak analysis.

Conclusions:

  • Incorporating a time factor into outbreak distributions significantly improves model fits.
  • The novel model provides valuable insights into in vivo host-pathogen dynamics.
  • This approach enhances the robustness and accuracy of legionellosis outbreak modeling.