The Ravaging Respiratory Infection: Fighting Influenza Using Mechanistic Models

Respiratory viruses, including influenza virus, cause a significant number of infections each year and are traditionally difficult to treat. This is in part due to the short window where antivirals are efficacious. But, if we can effectively forecast the tit-for-tat between the pathogen and the host, we may be able to identify new preventative and therapeutic regimens that are more effective. In this webinar, I will show how we can use an integrative model-experiment exchange to establish the dynamical connections between virus spread in the lung, control by host immune responses, lung damage inflicted throughout the infection, and how these relate to disease severity. I will describe the mathematical models and analyses and how experiments can be designed to validate the model predictions and gain confidence in our ability to predict disease progression, potential complications, and therapeutic options and efficacy.