key: cord-0901446-k1b0e2af authors: Pelleau, Stéphane; Woudenberg, Tom; Rosado, Jason; Donnadieu, Françoise; Garcia, Laura; Obadia, Thomas; Gardais, Soazic; Elgharbawy, Yasmine; Velay, Aurelie; Gonzalez, Maria; Nizou, Jacques Yves; Khelil, Nizar; Zannis, Konstantinos; Cockram, Charlotte; Merkling, Sarah Hélène; Meola, Annalisa; Kerneis, Solen; Terrier, Benjamin; de Seze, Jerome; Planas, Delphine; Schwartz, Olivier; Dejardin, François; Petres, Stéphane; von Platen, Cassandre; Pellerin, Sandrine Fernandes; Arowas, Laurence; de Facci, Louise Perrin; Duffy, Darragh; Cheallaigh, Clíona Ní; Dunne, Jean; Conlon, Niall; Townsend, Liam; Duong, Veasna; Auerswald, Heidi; Pinaud, Laurie; Tondeur, Laura; Backovic, Marija; Hoen, Bruno; Fontanet, Arnaud; Mueller, Ivo; Fafi-Kremer, Samira; Bruel, Timothée; White, Michael title: Kinetics of the Severe Acute Respiratory Syndrome Coronavirus 2 Antibody Response and Serological Estimation of Time Since Infection date: 2021-07-19 journal: J Infect Dis DOI: 10.1093/infdis/jiab375 sha: 3abce821729dfebaa3f424a6bec8d4e4b60db966 doc_id: 901446 cord_uid: k1b0e2af BACKGROUND: 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. METHODS: We developed a multiplex serological test for measuring antibodies to 5 SARS-CoV-2 antigens and the spike proteins of seasonal coronaviruses. We measured antibody responses in cohorts of hospitalized patients and healthcare workers followed for up to 11 months after symptoms. A mathematical model of antibody kinetics was used to quantify the duration of antibody responses. Antibody response data were used to train algorithms for estimating time since infection. RESULTS: One year after symptoms, we estimate that 36% (95% range, 11%–94%) of anti-Spike immunoglobulin G (IgG) remains, 31% (95% range, 9%–89%) anti-RBD IgG remains, and 7% (1%–31%) of anti-nucleocapsid IgG remains. The multiplex assay classified previous infections into time intervals of 0–3 months, 3–6 months, and 6–12 months. This method was validated using data from a seroprevalence survey in France, demonstrating that historical SARS-CoV-2 transmission can be reconstructed using samples from a single survey. CONCLUSIONS: In addition to diagnosing previous SARS-CoV-2 infection, multiplex serological assays can estimate the time since infection, which can be used to reconstruct past epidemics. surveillance information [2, 3] . Appropriately designed seroprevalence studies can provide estimates of the previously infected proportion of a population. Although no substitute for health system surveillance, seroprevalence studies have the advantage of accounting for asymptomatic cases and symptomatic individuals who do not present to health systems. Seroprevalence studies also provide information on the status of SARS-CoV-2 epidemics in situations where record-keeping by health systems is not possible, a common challenge in many low-and middle-income countries [4] . Infection with SARS-CoV-2 induces diverse humoral and cellular immune responses [5, 6] . Humoral immunity includes antibodies of several immunoglobulin isotypes targeting SARS-CoV-2 proteins, most notably spike (S) and nucleocapsid (N). The concentration of antibodies in blood varies substantially between individuals and with time since infection [6] [7] [8] [9] [10] [11] . Studies of the duration of immunity to SARS-CoV-1 demonstrated that antibodies remain detectable 6 years after infection [12] . Longitudinal follow-up of individuals infected with SARS-CoV-2 indicates a pattern of waning antibody responses consistent with other coronaviruses [13, 14] . Within the first 3 months, antibody levels boost sharply and wane rapidly. Over a longer interval of 8 months, antibody levels wane more slowly [6] . These observations can be explained by the biphasic nature of antibody kinetics [15] . Serological diagnostics typically classify a sample as positive if a measured antibody level is greater than a defined cutoff. Analysis of quantitative rather than binary antibody levels provides additional information; for example, antibody levels are associated with time since infection, symptom severity, and sex [16] . However, large variation in antibody levels between individuals prevents this from having predictive value: Detected antibodies could be from a recent infection, or due to immunological memory of older infections. This limitation has recently been overcome for a range of pathogens through the combination of multiplex assays and classification algorithms. Using machine learning algorithms to analyze quantitative antibody responses to multiple antigens, the time since previous infection can be estimated for Plasmodium falciparum malaria [17, 18] , Plasmodium vivax malaria [19] , and cholera [20] . In this study, we use multiplex assays to measure antibodies to SARS-CoV-2 in healthcare workers and hospitalized patients followed for up to 11 months after infection, and apply mathematical models to characterize antibody kinetics in the first year following infection. Classification algorithms were developed that minimize the reduction in the sensitivity of serological tests over time, in addition to estimating time since previous infection from a single blood sample. Finally, we present a method for serological reconstruction of past SARS-CoV-2 transmission and validate its use with samples collected from a cross-sectional survey. A panel of 407 negative control serum or plasma samples was assembled from prepandemic cohorts (before December 2019) with ethical approval for broad antibody testing (Table 1) . A panel of 407 positive control serum samples was assembled from individuals with recent SARS-CoV-2 infection. This included 72 samples from Paris hospitals that cared for patients with COVID-19 [21, 22] ; 161 samples from healthcare workers in Strasbourg University Hospital who developed symptomatic PCR-confirmed COVID-19 [23] ; and 174 samples from community members of Crépy-en-Valois, France, collected between February and March 2020, and confirmed seropositive by flow cytometry-based testing [24, 25] . The duration of antibody responses following SARS-CoV-2 infection was studied in longitudinal cohorts of hospitalized patients and healthcare workers. Two hundred thirteen serum samples from 194 patients in Dublin hospitals were collected, with date post-symptom onset extending up to 4 months. Seven hundred twenty-four serum samples from 347 healthcare workers in Strasbourg hospitals were collected, with date postsymptom onset extending up to 9 months [26] . In April 2020, our team implemented a study of the seroprevalence of SARS-CoV-2 in healthcare workers from Institut Mutualiste Montsouris, a hospital in Paris. Serum samples were collected from 769 healthcare workers, and tested with our multiplex assay (Supplementary Figure 1 ). Healthcare workers who tested seropositive in April 2020 were invited to present a second sample in January 2021. In total we obtained follow-up samples from 29 healthcare workers. To demonstrate the serological reconstruction of past COVID-19 epidemics, 725 samples were collected from residents of the town of Crépy-en-Valois in France from 13 November to 17 December 2020. Past transmission levels estimated from serological data were validated using an independent source of data on intensive care unit (ICU) admissions for COVID-19 in Oise (the department containing the town of Crépy-en-Valois) extracted from the national public data on daily hospital admissions related to COVID-19 published by Sante Publique France, available on data.gouv.fr (file downloaded 18 April 2021 at https://www.data.gouv.fr/fr/datasets/ donnees-hospitalieres-relatives-a-lepidemie-de-covid-19/#). Daily records cover the period from 18 March 2020 to 18 April 2021. We established a 9-plex bead-based assay allowing simultaneous detection of antibodies to 5 SARS-CoV-2 antigens and 4 seasonal coronaviruses (spike proteins of NL63, 229E, HKU1, OC43) in 1 µL serum or plasma samples that were heatinactivated at 56°C for 30 minutes. SARS-CoV-2 antigens were from spike (whole trimeric spike [S], its receptor-binding domain [RBD] , and spike subunit 2 [S2]), nucleocapsid protein (N), and a membrane-envelope fusion protein (ME). ME and S2 antigens were purchased from Native Antigen (Oxford, United Kingdom) and all other antigens were produced as recombinant proteins at Institut Pasteur. The mass of proteins coupled on beads was optimized to generate a log-linear standard curve with a pool of 27 positive sera prepared from patients with reversetranscription quantitative PCR-confirmed SARS-CoV-2. We measured the levels of immunoglobulin G (IgG), immunoglobulin A (IgA), and immunoglobulin M (IgM) of each sample in 3 separate assays. In brief, serum was diluted 1:200 for IgG or 1:400 for IgA and IgM, and incubated with mixed antigencoupled beads for 30 minutes. Secondary antibodies conjugated to R-phycoerythrin (Jackson Immunoresearch) were used at 1:120, 1:200, or 1:400 for detection of specific IgG, IgA, and IgM, respectively. All dilutions and cycles of washing steps were done in phosphate-buffered saline supplemented with 1% bovine serum albumin and 0.05% (v/v) Tween-20. On each assay plate, 2 blanks (only beads, no serum) were included to control for background signal as well as a standard curve prepared from 2-fold serial dilutions (1/50 to 1/102 400) of a pool of positive controls. Plates were read using a Luminex MAGPIX system and the median fluorescence intensity (MFI) was used for analysis. A 5-parameter logistic curve was used to convert MFI to relative antibody units, relative to the standard curve performed on the same plate to account for interassay variations. The data from our multiplex assay was compared against data from 2 different neutralization assays with live virus using a subset of serum samples [26] (Supplementary Methods). SARS-CoV-2 antibody kinetics are described using a previously published mathematical model of the immunological processes underlying the generation and waning of antibody responses following infection (Supplementary Methods) [27] . For the estimation of time since previous infection, measurements of antibodies of 3 isotypes (IgG, IgM, IgA) to multiple SARS-CoV-2 antigens were used to create a training dataset. Samples where the time post-symptom onset was ≤14 days or unknown were excluded. In total we had 407 samples from prepandemic negative controls and 1402 positive samples. A random forests multiway classification algorithm was developed for categorizing samples into 4 classes: (1) negative; (2) infected <3 months ago; (3) infected 3-6 months ago; and (4) infected 6-12 months ago. The algorithm was calibrated to have 99% specificity for correctly classifying negative samples. Positive samples were classified according to the maximum number of votes. Uncertainty in classification performance was assessed via cross-validation with a training set comprising two-thirds of the data and a disjoint testing set comprising a third of the data, repeated 1000 times. Classification algorithms were implemented in R (version 3.4.3). Serum samples were biobanked at the Clinical Investigation and Access to BioResources platform at Institut Pasteur (Paris, France). Samples were obtained from consenting individuals through the CORSER study ( Antibodies of 3 isotypes (IgG, IgM, IgA) to 9 coronavirus antigens were measured in 407 prepandemic serum samples, and 1402 serum samples from individuals with previous SARS-CoV-2 infection. Nine hundred sixty-one of 1402 of the positive samples were from individuals with SARS-CoV-2 infection confirmed by PCR-based testing with available data on time post-symptom onset. Figure 1 presents the IgG, IgM, and IgA antibody responses to SARS-CoV-2 S, RBD, and N measured over time. Supplementary Figures 2 and 3 present the antibody responses to S2, ME, and the spike proteins of the 4 human seasonal coronaviruses. Notably, there is substantial interindividual variation in antibody responses, with antibody levels varying by orders of magnitude between individuals. As a measure of functional immunity, we applied live virus neutralizing assays to a subset of samples. There was substantial variation in neutralizing activity between individuals and over time (Supplementary Figure 4) . Our data on measured SARS-CoV-2 antibody responses were supplemented with data from 6 other longitudinal studies of the SARS-CoV-2 antibody response [6] [7] [8] [9] [10] [11] and 1 longitudinal study of the SARS-CoV-1 antibody response [12] . Supplementary Figure 5 provides a comparison of the measured antibody responses between the 8 studies. The data were rescaled so that the mean antibody response for each study equals 1 at day 14 after symptom onset. In addition to the large interindividual variation, there is notable variation in antibody levels between studies. A mathematical model of antibody kinetics was simultaneously fitted to data from all 8 studies. Supplementary Figure 6 provides an overview of the model fit to the data. Figure 2A provides example fits to data from 1 representative individual from each of the 8 studies. Figure 2B provides model predictions of the percentage antibody level remaining over the first 2 years postinfection, where 100% is assumed to be the antibody response at day 14 following symptom onset. There are considerable differences in the pattern of waning between isotypes and between antigens. Nucleocapsid IgA 6% (1%-30%) 3% (0%-13%) 0.6% (0%-4%) The percentage antibody level remaining over time is compared to the measured antibody level 14 days after symptom onset. Estimates are presented as the population median, with the 95% range due to interindividual variation. Abbreviations: IgA, immunoglobulin A; IgG, immunoglobulin G; IgM, immunoglobulin M; RBD, receptor-binding domain. 36% (95% range, 11%-94%) anti-S IgG remains, 31% (9%-89%) anti-RBD IgG remains, and 7% (1%-31%) anti-N IgG remains. The uncertainty represents the considerable degree of interindividual variation in the duration of the antibody responses. Antibodies of the IgM isotype waned more rapidly, with 6% (0%-27%) anti-S IgM remaining after 1 year, 9% (2%-32%) anti-RBD IgM remaining after 1 year, and 15% (4%-50%) anti-N IgM remaining after 1 year. Antibodies of the IgA isotype also waned rapidly, with 18% (4%-67%) anti-S IgA remaining after 1 year, 10% (3%-38%) anti-RBD IgA remaining after 1 year, and 3% (0%-13%) anti-N IgA remaining after 1 year. We also observed comparable reductions in titers for viral neutralization over time (Supplementary Figure 4) . The small number of samples prevented application of the antibody kinetic model to viral neutralization data, but measuring the reduction in paired samples we estimate a rate of decrease of -0.94 (interquartile range [IQR], -2.41 to 0.26) year -1 . Assuming simple exponential decay in neutralization activity over time, this corresponds to 39.3% (IQR, 8.9%-125.2%) neutralizing activity remaining after 1 year, broadly consistent with the long-term kinetics of anti-RBD IgG. Using a dataset on measured IgG, IgM, and IgA antibody levels from prepandemic negative controls and samples from individuals with SARS-CoV-2 infection confirmed by PCR-based testing and followed for up to 11 months after symptom onset, random forest binary classification algorithms were trained to identify individuals with previous SARS-CoV-2 infection ( Supplementary Figures 7 and 8 ). Next, a random forest multiway classification was trained to simultaneously identify previous infection and estimate the time since infection ( Figure 3 ). The diagnostic algorithm identified samples from individuals with previous SARS-CoV-2 infection with 99% specificity and 98% (95% confidence interval [CI], 94%-99%) sensitivity ( Figure 3A ). The diagnostic test classified samples from individuals infected within the previous 3 months ( Figure 3B ). It was easier to distinguish recent infections (<3 months) from older infections (6-12 months There is a substantial degree of misclassification of samples with time since infection 3-6 months ago. This is due to the temporal imbalance in the training data. occurred 3-6 months ago. There was limited statistical signal to distinguish between infections that occurred 3-6 months ago and older infections occurring >6 months ago ( Figure 3C) . A breakdown of classification performance by time since infection is provided in Figure 3D . The diagnostic test had predictive power for samples of all categories, with the exception of samples from individuals infected 3-6 months ago. Many of these samples were incorrectly classified in the neighboring infection categories of 0-3 months or 6-12 months. One of the first reported clusters of SARS-CoV-2 transmission in France occurred in Crépy-en-Valois in Oise Department from late February to early March 2020 [24] . A team of scientists led by Institut Pasteur are conducting repeat cross-sectional surveys in this community. Seven hundred twenty-five serum samples collected between 13 November and 17 December 2020 were analyzed with our serological assay. Two hundred fifty-one of 725 (34.6%) samples were seropositive, indicating previous infection with SARS-CoV-2. Statistical algorithms for estimation of time since infection were applied to data from the 251 seropositive individuals. We estimated that 80.2% (95% CI, 47.3%-94.5%) were infected in the 6 months from December 2019 to May 2020, 0.0% (95% CI, 0.0%-52.1%) were infected in the 3 months from June to August, and 12.6% (95% CI, 0.0%-29.2%) were infected in the 3 months from September to November (Figure 4) . The serological estimates are consistent with a large first wave before June 2020, and a smaller second wave (or the beginning of a larger one) between September and December 2020. To assess the validity of these estimates, we compared with data on reported ICU admissions in Oise Department between 18 March 2020 and 1 December 2020. Of the ICU admissions, 56.0% were reported in the 3 months from March to May, 8.7% were reported in the 3 months from June to August, and 35.3% were reported in the 3 months from September to November. Based on data from a range of studies with up to 11 months of follow-up after symptom onset, we estimate that 31% (95% credible interval [CrI], 9%-89%) of anti-RBD IgG antibody levels remain 1 year after infection. Antibodies of other isotypes waned more rapidly, with 9% (95% CrI, 2%-32%) of anti-RBD IgM antibody levels remaining after 1 year, and 10% (95% CrI, 3%-38%) of anti-RBD IgA antibody levels remaining after 1 year. There was considerable variation in kinetic profiles between different SARS-CoV-2 antigens, with 7% (95% CrI, 1%-31%) of anti-N IgG antibody levels remaining after 1 year. Although the determinants of the duration of antigen-specific antibody responses remain poorly understood [16] , the diversity in patterns of antibody kinetics can be quantified using epidemiological studies, yielding valuable information for serological diagnostics. The majority of commercially available serological tests classify individuals as having previous SARS-CoV-2 infection if a measured antibody response is greater than a defined cutoff [28, 29] . Instead of reducing a complex antibody response to a binary data point, a more detailed serological signature based on quantitative measurements of multiple antibody responses provides 2 notable advantages [5] . First, the reduction in diagnostic sensitivity associated with waning antibodies is minimized-no reduction in sensitivity over the 11 months of follow-up was observed. Second, the time since previous infection can be estimated providing additional epidemiological information. For the current assay, time since previous infection was categorized into intervals of 0-3 months, 3-6 months, and 6-12 months. More precise classification is possible in theory, but this must be balanced against the statistical signal. For example, there was little statistical signal to discriminate between infections that occurred 6-9 months ago vs infections that occurred 9-12 months ago. It is anticipated that further assay improvements such as the incorporation of new antigens, more training samples with a range of time since infection, and better algorithms will lead to improvements in accuracy. Existing seroprevalence studies estimate the proportion of a population previously infected with SARS-CoV-2. The addition of a diagnostic tool capable of estimating time since infection allows for the serological reconstruction of past incidence trends. Thus, using samples from a seroprevalence study collected at a single time point, we can discriminate between a scenario of constant SARS-CoV-2 transmission and a scenario where transmission occurs in distinct epidemic waves. This diagnostic technology has a range of possible applications. For countries that experienced a double wave of SARS-CoV-2 epidemics, it has been challenging to quantify the relative magnitude of these waves due to the challenges of implementing PCR-based diagnostic testing [30] . Furthermore, there are many epidemiological settings where it is unknown if SARS-CoV-2 transmission was constant over time, or occurred as distinct epidemic waves [4] . Estimates of time since previous SARS-CoV-2 infection in individual samples can be combined to provide a serological reconstruction of previous SARS-CoV-2 transmission in a population. Following validation of our statistical method on simulated data, we demonstrated that our test could accurately identify the 2 epidemic peaks in the town of Crépy-en-Valois using samples collected in a single cross-section. This finding was consistent with daily reported ICU admissions data collected continuously over 1 year in the Oise Department. The minor differences between the serologically reconstructed epidemic profile and that from public health surveillance data may be because the town of Crépy-en-Valois is not necessarily representative of the wider Oise Department, as the town was specifically targeted by the study team because of its early levels of SARS-CoV-2 transmission. Furthermore, we did not analyze data on COVID-19-specific ICU admissions before March 2020, and ICU admissions may not necessarily be representative of community-level infection events [31] . There are several limitations to this study. Estimates of the duration of antibody responses are based on data from multiple studies, each using a unique immunoassay [6] [7] [8] [9] [10] [11] [12] . Every immunoassay may differ in terms of background reactivity, cross-reactivity with other pathogens, protein formulation, dynamic range, and reproducibility. We believe that the benefit of drawing on multiple data sources outweighs the benefit of having a smaller, more homogeneous database, especially since the mathematical model of antibody kinetics is sufficiently flexible to incorporate data from multiple assays. Our selection of a mechanistic mathematical model of antibody kinetics is a potential limitation. The model is based on a mechanistic understanding of the immunological processes underlying the generation and persistence of antibodies, and imposes a flexible functional form on how antibody levels change over time. Although this approach has been validated in a range of applications [16] [17] [18] [19] , there will be instances where the model fails to capture the true pattern of antibody kinetics, for example in immunodeficient individuals. An advantage of a mechanistic model vs a nonparametric statistical model is the ability to make projections forward in time. We have provided predictions up to 2 years following infection, for example, by estimating that 16% (95% range, 5%-48%) of anti-RBD IgG antibodies remain after 2 years. There is a risk to providing predictions beyond the timescale of the data, but these predictions can be easily refuted via continued longitudinal studies. Seroprevalence studies are playing a critical role in monitoring the progress of the SARS-CoV-2 pandemic. In the early stages of the pandemic, immunoassays had the advantage of measuring high antibody levels in the initial months after infection. As the pandemic progresses, seroprevalence will become more challenging to accurately measure due to waning antibody responses and increased vaccine-induced immunity. Multiplex assays and algorithms accounting for how antibody levels change over time may be an important tool for ensuring the ongoing utility of serosurveillance. 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The French COVID cohort is supported by the REACTing consortium and by the French Directorate General for Health. Jessica Vanhomwegen (Institut Pasteur) is thanked for provision of a MAGPIX machine. Jérôme Hadjadj and Laura Barnabei (Institut Imagine, Paris) are thanked for their work on the Hôpital Cochin study. Dionicia Gamboa (Cayetano Heredia University, Lima) is thanked for sharing negative control samples from Peru. Jetsumon Sattabongkot (Mahidol University, Bangkok) is thanked for sharing negative control samples from Thailand. Marie-Noelle Ungeheuer and Blanca Liliana Perlaza are thanked for processing samples at the Clinical Investigation and Access to BioResources platform in Institut Pasteur. We thank all patients and healthcare workers who kindly agreed for samples to be used for medical research purposes.Data availability. All data and code used for reproducing the results are freely available on GitHub: https://github.com/ MWhite-InstitutPasteur/SARSCoV2_AB_kinetics. All authors have submitted the ICMJE Form for Disclosure of Potential Conflicts of Interest. Conflicts that the editors consider relevant to the content of the manuscript have been disclosed.