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A mechanistic modeling and estimation framework for environmental pathogen surveillance.

Matthew Wascher1, Colin J Klaus2, Chance Alvarado3

  • 1Division of Epidemiology, College of Public Health, The Ohio State University, United States of America; Department of Mathematics, Applied Mathematics, and Statistics, Case Western Reserve University, United States of America.

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|August 22, 2024
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Summary

Environmental pathogen surveillance, like for SARS-CoV-2, faces challenges due to variable shedding. This study develops a model to link environmental data to infected individuals, improving public health insights.

Keywords:
Environmental dustEnvironmental pathogen surveillanceInter-individual variationPathogen sheddingPoisson processSARS-CoV-2

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Area of Science:

  • Environmental microbiology
  • Epidemiology
  • Mathematical modeling

Background:

  • Environmental pathogen surveillance is crucial for disease monitoring, particularly for SARS-CoV-2.
  • Variability in pathogen shedding among infected individuals complicates data interpretation.
  • Integrating environmental data into public health requires robust modeling frameworks.

Purpose of the Study:

  • To develop a mechanistic modeling and estimation framework connecting environmental pathogen data to the number of infected individuals.
  • To address the challenge of heterogeneous pathogen shedding in environmental surveillance.
  • To provide a method for estimating infected populations using environmental pathogen levels.

Main Methods:

  • Modeled infected individuals shedding pathogens via a Poisson process with time-varying rates (λt).
  • Incorporated random shedding curves to account for inter-individual variation.
  • Developed a two-step Bayesian inference framework for parameter calibration and estimation.
  • Applied the framework to synthetic data and a SARS-CoV-2 case study in isolation rooms.

Main Results:

  • The framework models environmental pathogen levels as a Poisson process influenced by infected individuals, shedding, and removal.
  • Identifiable model parameters were determined from environmental data.
  • High inter-individual shedding variation led to wide credible intervals for infected individuals.
  • The model can distinguish between no infection and low infection levels, and between moderate and high infection levels.

Conclusions:

  • The developed framework provides a method to estimate the number of infected individuals from environmental pathogen surveillance data.
  • Despite wide credible intervals, the model shows potential for differentiating infection levels, aiding public health response.
  • Accounting for inter-individual shedding variability is critical for accurate environmental surveillance interpretation.