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A Machine Learning-Based Approach to Determine Infection Status in Recipients of BBV152 (Covaxin) whole-virion Inactivated SARS-CoV-2 Vaccine for Serological Surveys

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dc.contributor.author Singh, P
dc.contributor.author Ujjainiya, R
dc.contributor.author Prakash, S
dc.contributor.author Naushin, S
dc.contributor.author Sardana, V
dc.contributor.author Bhatheja, N
dc.contributor.author Singh, A P
dc.contributor.author Barman, J
dc.contributor.author Kumar, K
dc.contributor.author Gayali, S
dc.contributor.author Khan, R
dc.contributor.author Rawat, B S
dc.contributor.author Tallapaka, K B
dc.contributor.author Anumalla, M
dc.contributor.author Lahiri, A
dc.contributor.author Kar, S
dc.contributor.author Bhosale, V
dc.contributor.author Sukumaran, RK
dc.date.accessioned 2023-01-17T11:07:05Z
dc.date.available 2023-01-17T11:07:05Z
dc.date.issued 2022-07
dc.identifier.citation Computers in biology and medicine ;146:105419 en_US
dc.identifier.uri https://doi.org/10.1016/j.compbiomed.2022.105419
dc.identifier.uri http://localhost:8080/xmlui/handle/123456789/4179
dc.description.abstract Data science has been an invaluable part of the COVID-19 pandemic response with multiple applications, ranging from tracking viral evolution to understanding the vaccine effectiveness. Asymptomatic breakthrough infections have been a major problem in assessing vaccine effectiveness in populations globally. Serological discrimination of vaccine response from infection has so far been limited to Spike protein vaccines since whole virion vaccines generate antibodies against all the viral proteins. Here, we show how a statistical and machine learning (ML) based approach can be used to discriminate between SARS-CoV-2 infection and immune response to an inactivated whole virion vaccine (BBV152, Covaxin). For this, we assessed serial data on antibodies against Spike and Nucleocapsid antigens, along with age, sex, number of doses taken, and days since last dose, for 1823 Covaxin recipients. An ensemble ML model, incorporating a consensus clustering approach alongside the support vector machine model, was built on 1063 samples where reliable qualifying data existed, and then applied to the entire dataset. Of 1448 self-reported negative subjects, our ensemble ML model classified 724 to be infected. For method validation, we determined the relative ability of a random subset of samples to neutralize Delta versus wild-type strain using a surrogate neutralization assay. We worked on the premise that antibodies generated by a whole virion vaccine would neutralize wild type more efficiently than delta strain. In 100 of 156 samples, where ML prediction differed from self-reported uninfected status, neutralization against Delta strain was more effective, indicating infection. We found 71.8% subjects predicted to be infected during the surge, which is concordant with the percentage of sequences classified as Delta (75.6%–80.2%) over the same period. Our approach will help in real-world vaccine effectiveness assessments where whole virion vaccines are commonly used. en_US
dc.language.iso en en_US
dc.publisher Elsevier en_US
dc.subject COVID-19 en_US
dc.subject SARS-CoV-2 en_US
dc.subject covaxin en_US
dc.subject BBV152 en_US
dc.subject machine learning en_US
dc.subject ensemble methods en_US
dc.subject infection en_US
dc.title A Machine Learning-Based Approach to Determine Infection Status in Recipients of BBV152 (Covaxin) whole-virion Inactivated SARS-CoV-2 Vaccine for Serological Surveys en_US
dc.type Article en_US


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  • 2022
    Research articles authored by NIIST researchers published in 2022

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