Generalizing the Wells-Riley Infection Probability: A Superstatistical Scheme for Indoor Infection Risk Estimation

Area of Science:

• Epidemiology
• Infectious Disease Modeling
• Statistical Physics

Background:

• Airborne transmission is the primary route for SARS-CoV-2 (COVID-19).
• Estimating indoor infection risk is challenging due to data limitations and environmental/immunological variations.
• Existing models often assume homogeneous indoor environments, neglecting real-world complexities.

Purpose of the Study:

• To develop a generalized infection probability model for indoor spaces considering environmental and immunological heterogeneities.
• To investigate the effectiveness of current biosafety measures like the six-foot rule.
• To explore the role of nonadditive entropies in understanding indoor epidemiology.

Main Methods:

• Generalized Wells-Riley infection probability model.
• Superstatistical approach with a gamma-distributed exposure rate across indoor subvolumes.
• Susceptible-Exposed-Infected (SEI) dynamics model incorporating Tsallis entropic index (q) for environmental homogeneity.
• Cumulative-dose mechanism for infection activation based on host immunity.

Main Results:

• The six-foot social distancing guideline is insufficient to guarantee biosafety in indoor environments.
• Even brief exposure times (15 minutes) can pose significant infection risks.
• The Tsallis entropic index (q) quantifies deviations from well-mixed indoor air conditions.

Conclusions:

• A generalized SEI model provides a more realistic framework for indoor infection dynamics.
• Nonadditive entropies are crucial for understanding the complexities of indoor epidemiology.
• The study highlights the need for advanced biosafety protocols beyond simple distancing, considering individual immune responses.