Using a multiplex serological assay to estimate time since SARS-CoV-2 infection and past clinical presentation in malagasy patients

使用多重血清学检测来估计马达加斯加患者感染 SARS-CoV-2 的时间和既往临床表现

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作者:Mame Diarra Bousso Ndiaye, Lova Tsikiniaina Rasoloharimanana, Solohery Lalaina Razafimahatratra, Rila Ratovoson, Voahangy Rasolofo, Paulo Ranaivomanana, Laurent Raskine, Jonathan Hoffmann, Rindra Randremanana, Niaina Rakotosamimanana, Matthieu Schoenhals

Background

The world is facing a 2019 coronavirus (COVID-19) pandemic caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). In this context, efficient serological assays are needed to accurately describe the humoral responses against the virus. These tools could potentially provide temporal and clinical characteristics and are thus paramount in developing-countries lacking sufficient ongoing COVID-19 epidemic descriptions.

Methods

We developed and validated a Luminex xMAP® multiplex serological assay targeting specific IgM and IgG antibodies against the SARS-CoV-2 Spike subunit 1 (S1), Spike subunit 2 (S2), Spike Receptor Binding Domain (RBD) and the Nucleocapsid protein (N). Blood samples collected periodically for 12 months from 43 patients diagnosed with COVID-19 in Madagascar were tested for these antibodies. A random forest algorithm was used to build a predictive model of time since infection and symptom presentation. Findings: The performance of the multiplex serological assay was evaluated for the detection of SARS-CoV-2 anti-IgG and anti-IgM antibodies. Both sensitivity and specificity were equal to 100% (89.85-100) for S1, RBD and N (S2 had a lower specificity = 95%) for IgG at day 14 after enrolment. This multiplex assay compared with two commercialized ELISA kits, showed a higher sensitivity. Principal Component Analysis was performed on serologic data to group patients according to time of sample collection and clinical presentations. The random forest algorithm built by this approach predicted symptom presentation and time since infection with an accuracy of 87.1% (95% CI = 70.17-96.37, p-value = 0.0016), and 80% (95% CI = 61.43-92.29, p-value = 0.0001) respectively. Interpretation: This study demonstrates that the statistical model predicts time since infection and previous symptom presentation using IgM and IgG response to SARS-CoV2. This tool may be useful for global surveillance, discriminating recent- and past- SARS-CoV-2 infection, and assessing disease severity. Fundings: This study was funded by the French Ministry for Europe and Foreign Affairs through the REPAIR COVID-19-Africa project coordinated by the Pasteur International Network association. WANTAI reagents were provided by WHO AFRO as part of a Sero-epidemiological "Unity" Study Grant/Award Number: 2020/1,019,828-0 P·O 202546047 and Initiative 5% grant n°AP-5PC-2018-03-RO.

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