Thomas Obadia, Solen Kernéis, Konstantinos Zannis, Cliona Ni Cheallaigh, Laura Garcia, María Victoria González, Tom Woudenberg, Samira Fafi-Kremer, Stéphane Petres, Jérôme De Seze, Benjamin Terrier, François Dejardin, Delphine Planas, Marija Backovic, Ivo Mueller, Françoise Donnadieu, Laurence Arowas, Charlotte Cockram, Aurélie Velay, Darragh Duffy, Bruno Hoen, Nizar Khelil, Louise Perrin de Facci, Sarah H. Merkling, Timothée Bruel, Heidi Auerswald, Annalisa Meola, Arnaud Fontanet, Jacques Yves Nizou, Niall Conlon, Cassandre Von Platen, Soazic Gardais, Yasmine Elgharbawy, Olivier Schwartz, Michael A. White, Liam Townsend, Jason Rosado, Stéphane Pelleau, Malaria : parasites et hôtes - Malaria : parasites and hosts, Institut Pasteur [Paris] (IP), Laboratoire de Virologie [Strasbourg], Institut Mutualiste de Montsouris (IMM), Département de Génomes et Génétique - Department of Genomes and Genetics, Interactions Virus-Insectes - Insect-Virus Interactions (IVI), Institut Pasteur [Paris] (IP)-Centre National de la Recherche Scientifique (CNRS), Virologie Structurale - Structural Virology, GHU AP-HP Centre Université de Paris, CHU Pitié-Salpêtrière [AP-HP], Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU), CIC Strasbourg (Centre d’Investigation Clinique Plurithématique (CIC - P) ), Université de Strasbourg (UNISTRA)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Nouvel Hôpital Civil de Strasbourg-Hôpital de Hautepierre [Strasbourg], Virus et Immunité - Virus and immunity (CNRS-UMR3569), Plateforme technologique Production et purification de protéines recombinantes – Production and Purification of Recombinant Proteins Technological Platform (PPR), Centre de Recherche Translationnelle - Center for Translational Science (CRT), Investigation Clinique et d’Accès aux Ressources Biologiques (Plate-forme) - Clinical Investigation and Access to BioResources (ICAReB), Immunologie Translationnelle - Translational Immunology lab, St James's University Hospital, Leeds Teaching Hospitals NHS Trust, Trinity College Dublin, Unité de Virologie / Virology Unit [Phnom Penh], Institut Pasteur du Cambodge, Réseau International des Instituts Pasteur (RIIP)-Réseau International des Instituts Pasteur (RIIP), Direction de la recherche médicale de l'Institut Pasteur, Epidémiologie des Maladies Emergentes - Emerging Diseases Epidemiology, Pasteur-Cnam Risques infectieux et émergents (PACRI), Institut Pasteur [Paris] (IP)-Conservatoire National des Arts et Métiers [CNAM] (CNAM), HESAM Université - Communauté d'universités et d'établissements Hautes écoles Sorbonne Arts et métiers université (HESAM)-HESAM Université - Communauté d'universités et d'établissements Hautes écoles Sorbonne Arts et métiers université (HESAM)-Institut Pasteur [Paris] (IP)-Conservatoire National des Arts et Métiers [CNAM] (CNAM), HESAM Université - Communauté d'universités et d'établissements Hautes écoles Sorbonne Arts et métiers université (HESAM)-HESAM Université - Communauté d'universités et d'établissements Hautes écoles Sorbonne Arts et métiers université (HESAM), Michael, White, Institut Pasteur [Paris], Centre National de la Recherche Scientifique (CNRS)-Institut Pasteur [Paris], Institut Pasteur [Paris]-Centre National de la Recherche Scientifique (CNRS), Centre National de Référence Maladies auto-immunes Systémiques Rares [CHU Pitié-Salpêtrière], Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-CHU Pitié-Salpêtrière [AP-HP], Sorbonne Université (SU)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU), Institut National de la Santé et de la Recherche Médicale (INSERM)-Université de Strasbourg (UNISTRA)-Hôpital de Hautepierre [Strasbourg]-Nouvel Hôpital Civil de Strasbourg, Virus et Immunité - Virus and immunity, Institut Pasteur [Paris]-Conservatoire National des Arts et Métiers [CNAM] (CNAM)-Institut Pasteur [Paris]-Conservatoire National des Arts et Métiers [CNAM] (CNAM), Institut Pasteur [Paris]-Conservatoire National des Arts et Métiers [CNAM] (CNAM), Service de Département de médecine interne et immunologie clinique [CHU Pitié-Salpêtrière] (DMIIC), Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-CHU Pitié-Salpêtrière [AP-HP], and Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)
Infection with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) induces a complex antibody response that varies by orders of magnitude between individuals and over time. Waning antibody levels lead to reduced sensitivity of serological diagnostic tests over time. This undermines the utility of serological surveillance as the SARS-CoV-2 pandemic progresses into its second year. Here we develop a multiplex serological test for measuring antibodies of three isotypes (IgG, IgM, IgA) to five SARS-CoV-2 antigens (Spike (S), receptor binding domain (RBD), Nucleocapsid (N), Spike subunit 2, Membrane-Envelope fusion) and the Spike proteins of four seasonal coronaviruses. We measure antibody responses in several cohorts of French and Irish hospitalized patients and healthcare workers followed for up to eleven months after symptom onset. The data are analysed with a mathematical model of antibody kinetics to quantify the duration of antibody responses accounting for inter-individual variation. One year after symptoms, we estimate that 36% (95% range: 11%, 94%) of anti-S IgG remains, 31% (9%, 89%) anti-RBD IgG remains, and 7% (1%, 31%) anti-N IgG remains. Antibodies of the IgM isotype waned more rapidly, with 9% (2%, 32%) anti-RBD IgM remaining after one year. Antibodies of the IgA isotype also waned rapidly, with 10% (3%, 38%) anti-RBD IgA remaining after one year. Quantitative measurements of antibody responses were used to train machine learning algorithms for classification of previous infection and estimation of time since infection. The resulting diagnostic test classified previous infections with 99% specificity and 98% (95% confidence interval: 94%, 99%) sensitivity, with no evidence for declining sensitivity over the time scale considered. The diagnostic test also provided accurate classification of time since infection into intervals of 0 – 3 months, 3 – 6 months, and 6 – 12 months. Finally, we present a computational method for serological reconstruction of past SARS-CoV-2 transmission using the data from this test when applied to samples from a single cross-sectional sero-prevalence survey.