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On modelling airborne infection risk.

Yannis Drossinos1, Nikolaos I Stilianakis2,3

  • 1Thermal Hydraulics & Multiphase Flow Laboratory, Institute of Nuclear & Radiological Sciences and Technology, Energy & Safety, National Centre for Scientific Research "Demokritos", Agia Paraskevi 15314, Greece.

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|July 25, 2024
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Summary

This study extends airborne infection risk analysis to the population level using epidemiological models. Infection risk increases with shorter viral incubation periods and longer exposure times within an epidemic.

Keywords:
Gammaitoni–Nucci infection risk modelSARS-CoV-2 transmissionWells–Riley infection risk modelaerosolinfectious diseasesinfectious respiratory particle (IRP)

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Area of Science:

  • Epidemiology
  • Infectious Disease Modeling
  • Public Health

Background:

  • Airborne infection risk is typically assessed in enclosed spaces using models like Wells-Riley (WR).
  • Existing models often focus on individual exposure rather than population-level dynamics.
  • Understanding transmission modes (airborne vs. contact) is crucial for accurate risk assessment.

Purpose of the Study:

  • To extend airborne infection risk estimation to the population level.
  • To integrate epidemiological models with existing dose-response models (e.g., WR).
  • To analyze the impact of pathogen and host factors on airborne infection risk.

Main Methods:

  • Utilized an epidemiological model explicitly considering airborne and contact transmission.
  • Linked epidemiological models with the Wells-Riley and Gammaitoni-Nucci models.
  • Calculated time-dependent infection risk based on pathogen and individual properties.

Main Results:

  • Airborne infection quanta are influenced by droplet properties, pathogen biology, and individual behavior.
  • Epidemic infection risk is dependent on the viral latent period and the timing of infection.
  • Infection risk escalates with decreasing latent periods and prolonged presence in an epidemic setting.

Conclusions:

  • The study provides a population-level framework for airborne infection risk assessment.
  • Epidemiological models offer a more comprehensive approach than traditional dose-response models alone.
  • Factors influencing transmission dynamics, such as latent period, significantly impact population-level infection risk.