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Pathogen Phylodynamics: Unifying Evolution, Infection and Immunity

Final Report Summary - PATHPHYLODYN (Pathogen Phylodynamics: Unifying Evolution, Infection and Immunity)

The project PATHPHYLODYN sought to develop and apply new mathematical, computational and statistical methods to analyse the vast and growing volume of genetic data from infectious diseases. The application of such methods has the potental to allow us to ask new questions about how pathogens cause infection, transmit and generate epidemics. To achieve this aim we brought together concepts from different areas of biology, including population dynamics, phylogenetics, population genetics, spatial ecology, and immunology. The project worked on several lines of research. First, we developed new methods for estimating, from very large sets of virus genomes, how fast viruses are adapting and evolving. These techniques were applied to human viruses including HIV-1 and influenza. We also studied how this adaptation is linked to the molecular structure of virus proteins, and developed bioinformatic pipelines for very large viromic data sets. Second, we developed new mathematical techniques for pathogen genome analysis that require less computation time than previous approaches, and showed how such methods can incorporate realistic models of genomic sampling models. This work involved the application of ideas from control engineering to evolutionary inference. Third, we investigated how immune responses and virus populations respond and adapt in response to each other. We provided new measurements of, and new theories for, the within-host evolution of hepatitis C virus and other chronic infections. Further, we developed tools for the analysis of a new type of genetic data, immune repertoire sequences, which provide a description of the diversity of antibodies within an individual. Using these data and new methods we tested hypotheses about how the immune system fine-tunes itself during HIV infection, or after influenza vaccination, or in older versus younger people. Lastly, we used evolutionary methods to analyse the Zika virus epidemic that emerged in the Americas in 2015, the yellow fever epidemic in Brazil, and most recently, the COVID-19 pandemic. Our work on Zika viruses has helped to reveal the origins of this epidemic and its subsequent spread through the Americas, and onward to Africa. We helped devise and create the global scientific nomenclature system for SARS-CoV-2 (B.1.1.7 P.1 etc) that is used worldwide, and we helped track the early spread of the virus. Through our research we have created new computer code and software packages, which are openly-available for other researchers around the world to use.