Project Summary - Project 1 The objective of this project is to generate an integrated, systems-level dataset that will enable development of models that predict disease severity or long-term sequelae in individuals infected with Lassa virus, Ebola virus or SARS-CoV-2, and protective responses to vaccines. The central hypothesis of Project 1 is that multiple variables, including individual physiological, metabolic and immunological factors, influence survival or development of long-term sequelae after infection by pathogenic RNA viruses. Additionally, we seek to determine whether or not responses to vaccines will be protective. To test this hypothesis, we have assembled unique West African and United States cohorts of individuals who are at risk for Lassa, Ebola and COVID-19 or who have survived these illnesses. Clinical trials of Lassa vaccines have recently been initiated at our clinical sites in West Africa, and we are also studying the durability of Ebola vaccines and the potential of vaccination to prevent viral reactivation in Ebola survivors. The proposed project brings together a strategically organized set of cutting edge systems tools to capture the overall immunome, antibody-ome, and metabolome of Ebola, Lassa, and COVID-19 survivors. We will use machine learning to identify unique signatures of persistent infection/disease, providing a path to diagnose, treat, and manage persistent disease following viral infection. In Aim 1, we will define physiological and metabolic attributes that distinguish Lassa, Ebola and COVID-19 survivors, non-survivors, and individuals that develop post-infection sequelae by compiling and analyzing clinical, immunological and nontraditional data, including data from wearables. In Aim 2, we will utilize high-throughput technologies, including PhIP-Seq, VirScan, and Systems Serology, to derive deep datasets to identify attributes of the humoral immune responses of Lassa, Ebola and COVID-19 patients, survivors, and vaccinees that lead to different outcomes. In Aim 3, we will characterize anti-coronavirus immune responses in West Africans and compare the results to United States cohorts, with the goal of identifying and characterizing potentially protective responses to SARS-CoV-2. Finally, in Aim 4 we will integrate heterogeneous data types to investigate the importance of host and virus factors in determining responses to vaccines and outcome of infection with different variants of our three viruses of interest. We will work with the Modeling Core to integrate these heterogenous data types using a combination of advanced modeling, machine learning tools, and related technologies to identify predictive biosignatures that inform: personalized treatment across sex, age, and racial differences; management strategies in acutely infected individuals and those with long term viral syndromes such as PASC; and potential targets for advanced therapeutics and improved vaccines.