Viral pathogens are an enduring threat to global public health. This project aims use viral genomic data to improve understanding of ongoing virus evolution and to make actionable inferences to reduce the global burden of viral infectious disease. In order to be relevant for public health interventions, analyses of viral sequence data need to be incredibly rapid, both in terms of computation and in terms of dissemination. To accomplish these goals, this project will create novel methodological tools to analyze evolutionary dynamics from influenza genetic sequence data and to analyze transmission patterns from outbreak sequence data. These methods will result in a real-time analysis platform, realized via the website, that provides constantly up-to-date analyses of a variety of viruses, including influenza virus, Ebola virus and Middle East respiratory syndrome coronavirus (MERS-CoV). This website would provide public health officials and other stakeholders an intuitive view of ongoing viral evolution and help to pinpoint targeted interventions. In the case of influenza, monitoring antigenic evolution of viral strains is of paramount importance. New antigenic variants of influenza that partially escape from prior human immunity emerge and rapidly sweep through the viral population. Such strains are less susceptible to vaccine-derived immunity and so antigenic evolution results in the need to frequently update the seasonal influenza vaccine. This project aims to develop tools to characterize circulating antigenic phenotypes from genetic and serological assay data and to develop methods to forecast strain dynamics and predict the makeup of the future influenza population. This forecasting is especially relevant to influenza vaccine strain selection, as a vaccine strain is chosen for the Northern Hemisphere in February for deployment the following winter. Accurate projections will aid in vaccine match for seasonal influenza viruses and result in improved vaccine efficacy. All predictions will be made in a public fashion on the website, allowing wide distribution and rapid dissemination to public health officials. In an outbreak scenario such as the 2014–2015 West African Ebola epidemic or the 2013–2015 MERS-CoV outbreak, the focus of public health interventions shifts from vaccination to early diagnosis, contact tracing, isolation and treatment. Viral genomic data can reveal otherwise hidden transmission patterns and aid in efficient contact tracing. Geographic spread is especially amenable to genomic inferences. This project will develop tools to make epidemiological inferences from outbreak sequence data. These inferences will be deployed on the website in real-time, allowing field epidemiologists to put samples into the great epidemic context and understand the transmission history leading to the case at hand. Such a system stands to make a real contribution to global public health and outbreak response.


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