Development of an Interspecies Nested Dose-Response Model for Mycobacterium avium subspecies paratuberculosis

Kirk J Breuninger1, Mark H Weir1

  • 1Department of Public Health, Temple University, Philadelphia, PA, USA.

Insights

Mycobacterium avium subspecies paratuberculosis (MAP) infection risk can now be quantified. A new nested dose-response model, developed from existing studies, accurately predicts MAP infection probability following oral exposure.

Area of Science:

  • Microbiology
  • Veterinary Medicine
  • Epidemiology

Background:

  • Mycobacterium avium subspecies paratuberculosis (MAP) causes Johne's disease in cattle and is linked to Crohn's disease in humans.
  • Quantitative microbial risk assessments (QMRA) for MAP have been limited by the absence of dose-response functions.
  • Understanding MAP's infectivity is crucial for public health and animal health risk management.

Purpose of the Study:

  • To develop a nested dose-response model for MAP infection following oral exposure.
  • To establish a quantitative framework for assessing the risk of MAP infection.
  • To enable more accurate QMRA for MAP.

Main Methods:

  • Literature search to identify studies suitable for dose-response modeling.
  • Optimization of data to one-parameter exponential or two-parameter beta-Poisson models.
  • Nesting analysis to assess the pooling of data across different host species.

Main Results:

  • Three of four datasets showed good fit to at least one dose-response model.
  • The beta-Poisson model provided a good fit for three datasets.
  • Two datasets from sheep and red deer were successfully nested using the beta-Poisson model (α = 0.0978, N50 = 2.70 × 10^2 CFU), demonstrating interspecies applicability and MAP's high infectivity.

Conclusions:

  • A robust nested dose-response model for MAP oral exposure has been developed.
  • The model demonstrates successful interspecies data nesting, highlighting MAP's infectivity.
  • This model is recommended for future QMRA research on MAP infection risk.

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