Probabilistic indoor transmission modeling for influenza (sub)type viruses

Area of Science:

• Epidemiology
• Mathematical Modeling
• Infectious Disease Transmission

Background:

• Influenza poses a significant public health risk, particularly in indoor environments.
• Understanding transmission dynamics is crucial for effective control strategies.

Purpose of the Study:

• To employ a probability-based transmission modeling approach to assess influenza infection risk indoors.
• To analyze 10 years of surveillance data and experimental viral shedding data from Taiwan.

Main Methods:

• Integrated sentinel physician data and laboratory surveillance for influenza A (H1N1, H3N2), B, and RSV.
• Utilized the Wells-Riley mathematical model incorporating environmental factors (ventilation, breathing rates).
• Linked vaccine match rates and transmission estimations to predict control potential using basic reproduction number (R(0)) and asymptomatic infection proportion (theta).

Main Results:

• Developed a quantitative framework for infection risk and R(0) estimation for influenza A (H1N1, H3N2), and B.
• Successfully linked viral concentration in human fluid with quantum generation rates to estimate virus-specific infection risks.
• Influenza A (H3N2) demonstrated higher transmissibility and uncontrollable potential than A (H1N1) and B viruses.

Conclusions:

• A probabilistic transmission model effectively integrates virus-specific data (viral shedding, surveillance) to predict infection risks.
• The model provides valuable insights into influenza virus transmission dynamics in Taiwan.
• Findings highlight the differential transmissibility of influenza strains, informing public health interventions.