ABSTRACT
Influenza and associated diseases remain significant sources of economic and public health burden. Every year,
around three to five million people come down with severe cases of influenza with another 300,000 to 500,000
more who die worldwide. Amongst those who are disproportionately affected are individuals with chronic
conditions such as diabetes, obesity, cardiovascular disease, children and individuals aged 65 years and older.
For example, 90% of deaths from seasonal influenza and 70% of influenza-associated hospitalization belong to
this age group. There is also a significant association between developing a heart attack within a week of getting
infected with influenza A or B viruses. Why some people are more susceptible can be due to a multitude of
factors such as immunosenescence, pre-existing immunity, genetics, diet, environment and other underlying
diseases, which may contribute to the severity of influenza. Yet, it remains difficult for scientists and clinicians to
determine the trajectory of disease severity for any given parameter or category. This is largely due to assigning
an outcome with a particular correlate using a single assay or method. Given the multitude of factors that may
influence disease progression or immunity, a method by which different datasets are integrated to predict the
outcome would be highly beneficial in guiding clinical practices. Towards this goal, we hypothesize that a
multimodal network approach in analyzing different metrics will identify features from complex datasets that are
predictive of influenza disease outcome. These features include host, commensal microbial and viral factors,
and identifiable interactions between them associated with disease and immunity. This will potentially reveal new
insights into influenza virus-host interactions and transform clinical practices. We will utilize established
immunological assays, sequencing approaches and metadata in order to take advantage of existing methods
and infrastructures and apply it to our novel bioinformatics and artificial intelligence workflows. To do this, we will
i) generate a comprehensive systems level multimodal dataset including both viral and host factors to assess
differential influenza virus infection severity signatures in a human cohort, and ii) utilize multimodal network
analysis and machine learning to identify features and interactions predictive of the trajectory of disease severity
due to influenza virus infection.
