Identifying Influenza Virus Infection Severity and Outcome Signatures Through Artificial Intelligence-driven Analyses

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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.