Modeling Competing Infectious Pathogens From a Bayesian Perspective: Application to Influenza Studies With Incomplete Laboratory Results

Abstract

In seasonal influenza epidemics, pathogens such as respiratory syncytial virus (RSV) often cocirculate with influenza and cause influenza-like illness (ILI) in human hosts. However, it is often impractical to test for each potential pathogen or to collect specimens for each observed ILI episode, making inference about influenza transmission difficult. In the setting of infectious diseases, missing outcomes impose a particular challenge because of the dependence among individuals. We propose a Bayesian competing-risk model for multiple cocirculating pathogens for inference on transmissibility and intervention efficacies under the assumption that missingness in the biological confirmation of the pathogen is ignorable. Simulation studies indicate a reasonable performance of the proposed model even if the number of potential pathogens is misspecified. They also show that a moderate amount of missing laboratory test results has only a small impact on inference about key parameters in the setting of close contact groups. Using the proposed model, we found that a nonpharmaceutical intervention is marginally protective against transmission of influenza A in a study conducted in elementary schools.