We thank NIHR BioResource volunteers for their participation, and gratefully acknowledge NIHR BioResource centres, NHS Trusts and staff for their contribution. We thank the National Institute for Health and Care Research, NHS Blood and Transplant, and Health Data Research UK as part of the Digital Innovation Hub Programme. The views expressed are those of the author(s) and not necessarily those of the NHS, the NIHR or the Department of Health and Social Care. We are also grateful for the generous support of CVC Capital Partners, the Evelyn Trust (20/75), UKRI COVID Immunology Consortium, Addenbrooke's Charitable Trust (12/20A), the UKRI/NIHR through the UK Coronavirus Immunology Consortium (UK-CIC), and the Botnar Research Centre for Child Health (BRCCH) for their financial support. H.R. would like to thank the Lopez–Loreta Foundation for their support. J.N., J.W. and E.H. would like to acknowledge the Australian Federal Government's Medical Research Future Fund (MRFF-ARAPC ARG76435), the Western Australian Department of Health, the Spinnaker Health Research Foundation and the McCusker Charitable Foundation for their support and contributions to this research program. K.G.C.S. acknowledges support from Wellcome Investigator and Collaborative Awards (200871/Z/16/ Z; 219506/Z/19/Z) and a Medical Research Council Programme Grant (MR/L019027). We would also like to thank the NIHR Cambridge Clinic Research Facility outreach team for enrollment of patients; and the NIHR Cambridge Biomedical Research Centre Cell Phenotyping Hub and the CRUK Cambridge Institute flow cytometry core facility for their support with flow and mass cytometry.

The biology driving individual patient responses to SARS-CoV-2 infection remains ill understood. Here, we developed a patient-centric framework leveraging detailed longitudinal phenotyping data and covering a year post-disease onset, from 215 SARS-CoV-2 infected subjects with differing disease severities. Our analyses revealed distinct “systemic recovery” profiles, with specific progression and resolution of the inflammatory, immune, metabolic and clinical responses. In particular, we found a strong inter- and intra-patient temporal covariation of innate immune cell numbers, kynurenine metabolites and lipid metabolites, which highlighted candidate immunologic and metabolic pathways influencing the restoration of homeostasis, the risk of death and that of long COVID. Based on these data, we identified a composite signature predictive of systemic recovery at the patient level, using a joint model on cellular and molecular parameters measured soon after disease onset. New predictions can be generated using the online tool http://shiny.mrc- bsu.cam.ac.uk/apps/covid-19-systemic-recovery-prediction-app, designed to test our findings prospectively.