Developing novel methods to assess the pandemic potential of influenza A viruses using mathematical modeling and evolutionary methods

PROJECT SUMMARY/ABSTRACT Influenza viruses are a constant public health threat. Their high mutation rates, combined with a large number of infections each year, generate novel variants; additionally, novel influenza viruses from animal reservoirs like swine and wild birds can occasionally infect humans. Seasonal influenza viruses cause circa 500,000 deaths annually, and pandemic influenza viruses have caused between 0.5-50 million deaths. However, despite the acknowledged risk of pandemic influenza virus emergence, there are limited methods to assess the pandemic potential of influenza strains. This proposal aims to develop two complementary methods to assess the risk of novel influenza isolates, using mathematical modeling and evolutionary analyses. I hypothesize that these approaches will prove useful in the estimation of important epidemiological parameters and in the quantification of aggregate pandemic risk of IAV strains. The methods will leverage data already commonly gathered for novel influenza viruses but broaden the applicability and increase the throughput of these established datasets and approaches. In Aim 1, I will use data from experimental transmission studies in animal models to estimate population-level epidemiological parameters. To do so, I will extend existing epidemiological approaches based on serology to estimate the rates of onward transmission based on viral titers. This method allows for the estimation of key parameters of interest, such as the basic reproduction number and the generation interval, prior to virus establishment in humans. In Aim 2, I will use comparative evolutionary approaches to quantify and predict pandemic risk. I will use phylogenetic comparative methods and ancestral and descendant state analysis to estimate the pandemic risk of novel influenza viruses based on their evolutionary history. This method would increase the speed with which risk assessments could be generated and would help to identify key influenza gene segments that are important in predicting risk. Taken together, these methods will improve our ability to accurately assess the pandemic risk of influenza isolates, which could help to improve public health guidance, vaccine reformulations, and therapeutic development. Furthermore, such methods could be easily extended to other viral pathogens of concern.