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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 Srivastava, M.
dc.contributor.author Mugale, M.N.
dc.contributor.author Pandey, C.P.
dc.contributor.author Khan, S.
dc.contributor.author Katiyar, S.
dc.contributor.author Raj, D.
dc.contributor.author Ishteyaque, S.
dc.contributor.author Khanka, S.
dc.contributor.author Rani, A.
dc.contributor.author Promila
dc.contributor.author Sharma, J.
dc.contributor.author Seth, A.
dc.contributor.author Dutta, M.
dc.contributor.author Saurabh, N.
dc.contributor.author Veerapandian, M.
dc.contributor.author Venkatachalam, G.
dc.contributor.author Bansal, D.
dc.contributor.author Gupta, D.
dc.contributor.author Halami, P.M.
dc.contributor.author Peddha, M.S.
dc.contributor.author Veeranna, R.P.
dc.contributor.author Pal, A.
dc.contributor.author Singh, R.K.
dc.contributor.author Anandasadagopan, S.K.
dc.contributor.author Karuppanan, P.
dc.contributor.author Rahman, S.N.
dc.contributor.author Selvakumar, G.
dc.contributor.author Venkatesan, S.
dc.contributor.author Karmakar, M.K.
dc.contributor.author Sardana, H.K.
dc.contributor.author Kothari, A.
dc.contributor.author Parihar, D.S.
dc.contributor.author Thakur, A.
dc.contributor.author Saifi, A.
dc.contributor.author Gupta, N.
dc.contributor.author Singh, Y.
dc.contributor.author Reddu, R.
dc.contributor.author Gautam, R.
dc.contributor.author Mishra, A.
dc.contributor.author Mishra, A.
dc.contributor.author Gogeri, I.
dc.contributor.author Rayasam, G.
dc.contributor.author Padwad, Y.
dc.contributor.author Patial, V.
dc.contributor.author Hallan, V.
dc.contributor.author Singh, D.
dc.contributor.author Tirpude, N.
dc.contributor.author Chakrabarti, P.
dc.contributor.author Maity, S.K.
dc.contributor.author Ganguly, D.
dc.contributor.author Sistla, R.
dc.contributor.author Balthu, N.K.
dc.contributor.author Kumar, A.K.
dc.contributor.author Ranjith, S.
dc.contributor.author Kumar, B.V.
dc.contributor.author Jamwal, P.S.
dc.contributor.author Wali, A.
dc.contributor.author Ahmed, S.
dc.contributor.author Chouhan, R.
dc.contributor.author Gandhi, S.G.
dc.contributor.author Sharma, N.
dc.contributor.author Rai, G.
dc.contributor.author Irshad, F.
dc.contributor.author Jamwal, V.L.
dc.contributor.author Paddar, M.A.
dc.contributor.author Khan, S.U.
dc.contributor.author Malik, F.
dc.contributor.author Ghosh, D.
dc.contributor.author Thakkar, G.
dc.contributor.author Barik, S.K.
dc.contributor.author Tripathi, P.
dc.contributor.author Satija, Y.K.
dc.contributor.author Mohanty, S.
dc.contributor.author Khan, M.T.
dc.contributor.author Subudhi, U.
dc.contributor.author Sen, P.
dc.contributor.author Kumar, R.
dc.contributor.author Bhardwaj, A.
dc.contributor.author Gupta, P.
dc.contributor.author Sharma, D.
dc.contributor.author Tuli, A.
dc.contributor.author Chaudhuri, S.R.
dc.contributor.author Krishnamurthi, S.
dc.contributor.author Prakash, L.
dc.contributor.author Rao, C.V.
dc.contributor.author Singh, B.N.
dc.contributor.author Chaurasiya, A.
dc.contributor.author Chaurasiya, M.
dc.contributor.author Bhadange, M.
dc.contributor.author Likhitkar, B.
dc.contributor.author Mohite, S.
dc.contributor.author Patil, Y.
dc.contributor.author Kulkarni, M.
dc.contributor.author Joshi, R.
dc.contributor.author Pandya, V.
dc.contributor.author Mahajan, S.
dc.contributor.author Patil, A.
dc.contributor.author Samson, R.
dc.contributor.author Vare, T.
dc.contributor.author Dharne, M.
dc.contributor.author Giri, A.
dc.contributor.author Mahajan, S.
dc.contributor.author Paranjape, S.
dc.contributor.author Sastry, G.N.
dc.contributor.author Kalita, J.
dc.contributor.author Phukan, T.
dc.contributor.author Manna, P.
dc.contributor.author Romi, W.
dc.contributor.author Bharali, P.
dc.contributor.author Ozah, D.
dc.contributor.author Sahu, R.
dc.contributor.author Dutta, P.
dc.contributor.author Singh, M.G.
dc.contributor.author Gogoi, G.
dc.contributor.author Tapadar, Y.B.
dc.contributor.author Babu, E.V.S.S.K.
dc.contributor.author Sukumaran, R.K.
dc.contributor.author Nair, A.R.
dc.contributor.author Puthiyamadam, A.
dc.contributor.author Valappil, P.K.
dc.contributor.author Prasannakumari, A.V.P.
dc.contributor.author Chodankar, K.
dc.contributor.author Damare, S.
dc.contributor.author Agrawal, V.V.
dc.contributor.author Chaudhary, K.
dc.contributor.author Agrawal, A.
dc.contributor.author Sengupta, S.
dc.contributor.author Dash, D.
dc.date.accessioned 2023-07-28T05:01:26Z
dc.date.available 2023-07-28T05:01:26Z
dc.date.issued 2022
dc.identifier.citation Computers in Biology and Medicine, 146, 2022: 105419
dc.identifier.issn 0010-4825
dc.identifier.uri http://ore.immt.res.in/handle/2018/3140
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.
dc.language en
dc.publisher Elsevier
dc.relation.isreferencedby SCI
dc.rights Copyright [2022]. All efforts have been made to respect the copyright to the best of our knowledge. Inadvertent omissions, if brought to our notice, stand for correction and withdrawal of document from this repository.
dc.subject Biological Sciences
dc.subject Engineering
dc.subject Physical Sciences
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
dc.type Journal Article
dc.affiliation.author CSIR-IGIB, New Delhi 110025, India


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