Probabilistic indoor transmission modeling for influenza (sub)type viruses

SummaryObjectivesTo use a probability based transmission modeling approach to examine the influenza risk of infection virus in indoor environments. This was based on 10 years of data gathered from influenza-like illness sentinel physician and laboratory surveillance, and experimental viral shedding data in Taiwan.MethodsWe integrated sentinel physician-reported cases and positive rates of influenza A (H1N1), A (H3N2), influenza B, and respiratory syncytial virus in Taiwan using the Wells–Riley mathematical model. This model incorporates environmental factors such as room ventilation and breathing rates. We also linked vaccine match rate with related transmission estimations to predict the controllable potential using a control model characterized by basic reproduction number (R0) and proportion of asymptomatic infections (θ).ResultsA quantitative framework was developed to better understand the infection risk and R0 estimates of A (H1N1), A (H3N2), and B viruses. The viral concentration in human fluid was linked successfully with quantum generation rates to estimate virus-specific infection risks. Our results revealed that A (H3N2) virus had a higher transmissibility and uncontrollable potential than the A (H1N1) and B viruses.ConclusionsProbabilistic transmission model can incorporate virus-specific data on experimental viral shedding, long-term sentinel physician and laboratory surveillance to predict virus-specific infection risks in Taiwan.