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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

DIR@IMTECH: CSIR-Institute of Microbial Technology

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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
 
Creator Singh, Prateek
Ujjainiya, Rajat
Prakash, Satyartha
Naushin, Salwa
Sardana, Viren
Bhatheja, Nitin
Singh, Ajay Pratap
Barman, Joydeb
Kumar, Kartik
Gayali, Saurabh
Khan, Raju
Rawat, Birendra Singh
Tallapaka, Karthik Bharadwaj
Anumalla, Mahesh
Lahiri, Amit
Kar, Susanta
Bhosale, Vivek
Srivastava, Mrigank
Mugale, Madhav Nilakanth
Pandey, C. P.
Khan, Shaziya
Katiyar, Shivani
Raj, Desh
Ishteyaque, Sharmeen
Khanka, Sonu
Rani, Ankita
Promila, .
Sharma, Jyotsna
Seth, Anuradha
Dutta, Mukul
Saurabh, Nishant
Veerapandian, Murugan
Venkatachalam, Ganesh
Bansal, Deepak
Gupta, Dinesh
Halami, Prakash M.
Peddha, Muthukumar Serva
Veeranna, Ravindra P.
Pal, Anirban
Singh, Ranvijay Kumar
Anandasadagopan, Suresh Kumar
Karuppanan, Parimala
Rahman, Syed Nasar
Selvakumar, Gopika
Venkatesan, Subramanian
Karmakar, Malay Kumar
Sardana, Harish Kumar
Kothari, Anamika
Parihar, Devendra Singh
Thakur, Anupma
Saifi, Anas
Gupta, Naman
Singh, Yogita
Reddu, Ritu
Gautam, Rizul
Mishra, Anuj
Mishra, Avinash
Gogeri, Iranna
Rayasam, Geethavani
Padwad, Yogendra
Patial, Vikram
Hallan, Vipin
Singh, Damanpreet
Tirpude, Narendra
Chakrabarti, Partha
Maity, Sujay Krishna
Ganguly, Dipyaman
Sistla, Ramakrishna
Balthu, Narender Kumar
Kumar, Kiran A.
Ranjith, Siva
Kumar, B. Vijay
Jamwal, Piyush Singh
Wali, Anshu
Ahmed, Sajad
Chouhan, Rekha
Gandhi, Sumit G.
Sharma, Nancy
Rai, Garima
Irshad, Faisal
Jamwal, Vijay Lakshmi
Paddar, Masroor Ahmad
Khan, Sameer Ullah
Malik, Fayaz
Ghosh, Debashish
Thakkar, Ghanshyam
Barik, S. K.
Tripathi, Prabhanshu
Satija, Yatendra Kumar
Mohanty, Sneha
Khan, Md Tauseef
Subudhi, Umakanta
Sen, Pradip
Kumar, Rashmi
Bhardwaj, Anshu
Gupta, Pawan
Sharma, Deepak
Tuli, Amit
Chaudhuri, Saumya Ray
Krishnamurthi, Srinivasan
Prakash, L.
Rao, Ch
Singh, B. N.
Chaurasiya, Arvindkumar
Chaurasiya, Meera
Bhadange, Mayuri
Likhitkar, Bhagyashree
Mohite, Sharada
Patil, Yogita
Kulkarni, Mahesh
Joshi, Rakesh
Pandya, Vaibhav
Mahajan, Sachin
Patil, Amita
Samson, Rachel
Vare, Tejas
Dharne, Mahesh
Giri, Ashok
Mahajan, Sachin
Paranjape, Shilpa
Sastry, G. Narahari
Kalita, Jatin
Phukan, Tridip
Manna, Prasenjit
Romi, Wahengbam
Bharali, Pankaj
Ozah, Dibyajyoti
Sahu, RaviKumar
Dutta, Prachurjya
Singh, Moirangthem Goutam
Gogoi, Gayatri
Tapadar, Yasmin Begam
Babu, Elapavalooru V. S. S. K.
Sukumaran, Rajeev K.
Nair, Aishwarya R.
Puthiyamadam, Anoop
Valappil, Prajeesh Kooloth
Prasannakumari, Adrash Velayudhan Pillai
Chodankar, Kalpana
Damare, Samir
Agrawal, Ved Varun
Chaudhary, Kumardeep
Agrawal, Anurag
Sengupta, Shantanu
Dash, Debasis
 
Subject QR Microbiology
 
Description 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.
 
Publisher Elsevier
 
Date 2022-07
 
Type Article
PeerReviewed
 
Relation https://www.sciencedirect.com/science/article/pii/S0010482522002116?via%3Dihub
http://crdd.osdd.net/open/3067/
 
Identifier Singh, Prateek and Ujjainiya, Rajat and Prakash, Satyartha and Naushin, Salwa and Sardana, Viren and Bhatheja, Nitin and Singh, Ajay Pratap and Barman, Joydeb and Kumar, Kartik and Gayali, Saurabh and Khan, Raju and Rawat, Birendra Singh and Tallapaka, Karthik Bharadwaj and Anumalla, Mahesh and Lahiri, Amit and Kar, Susanta and Bhosale, Vivek and Srivastava, Mrigank and Mugale, Madhav Nilakanth and Pandey, C. P. and Khan, Shaziya and Katiyar, Shivani and Raj, Desh and Ishteyaque, Sharmeen and Khanka, Sonu and Rani, Ankita and Promila, . and Sharma, Jyotsna and Seth, Anuradha and Dutta, Mukul and Saurabh, Nishant and Veerapandian, Murugan and Venkatachalam, Ganesh and Bansal, Deepak and Gupta, Dinesh and Halami, Prakash M. and Peddha, Muthukumar Serva and Veeranna, Ravindra P. and Pal, Anirban and Singh, Ranvijay Kumar and Anandasadagopan, Suresh Kumar and Karuppanan, Parimala and Rahman, Syed Nasar and Selvakumar, Gopika and Venkatesan, Subramanian and Karmakar, Malay Kumar and Sardana, Harish Kumar and Kothari, Anamika and Parihar, Devendra Singh and Thakur, Anupma and Saifi, Anas and Gupta, Naman and Singh, Yogita and Reddu, Ritu and Gautam, Rizul and Mishra, Anuj and Mishra, Avinash and Gogeri, Iranna and Rayasam, Geethavani and Padwad, Yogendra and Patial, Vikram and Hallan, Vipin and Singh, Damanpreet and Tirpude, Narendra and Chakrabarti, Partha and Maity, Sujay Krishna and Ganguly, Dipyaman and Sistla, Ramakrishna and Balthu, Narender Kumar and Kumar, Kiran A. and Ranjith, Siva and Kumar, B. Vijay and Jamwal, Piyush Singh and Wali, Anshu and Ahmed, Sajad and Chouhan, Rekha and Gandhi, Sumit G. and Sharma, Nancy and Rai, Garima and Irshad, Faisal and Jamwal, Vijay Lakshmi and Paddar, Masroor Ahmad and Khan, Sameer Ullah and Malik, Fayaz and Ghosh, Debashish and Thakkar, Ghanshyam and Barik, S. K. and Tripathi, Prabhanshu and Satija, Yatendra Kumar and Mohanty, Sneha and Khan, Md Tauseef and Subudhi, Umakanta and Sen, Pradip and Kumar, Rashmi and Bhardwaj, Anshu and Gupta, Pawan and Sharma, Deepak and Tuli, Amit and Chaudhuri, Saumya Ray and Krishnamurthi, Srinivasan and Prakash, L. and Rao, Ch and Singh, B. N. and Chaurasiya, Arvindkumar and Chaurasiya, Meera and Bhadange, Mayuri and Likhitkar, Bhagyashree and Mohite, Sharada and Patil, Yogita and Kulkarni, Mahesh and Joshi, Rakesh and Pandya, Vaibhav and Mahajan, Sachin and Patil, Amita and Samson, Rachel and Vare, Tejas and Dharne, Mahesh and Giri, Ashok and Mahajan, Sachin and Paranjape, Shilpa and Sastry, G. Narahari and Kalita, Jatin and Phukan, Tridip and Manna, Prasenjit and Romi, Wahengbam and Bharali, Pankaj and Ozah, Dibyajyoti and Sahu, RaviKumar and Dutta, Prachurjya and Singh, Moirangthem Goutam and Gogoi, Gayatri and Tapadar, Yasmin Begam and Babu, Elapavalooru V. S. S. K. and Sukumaran, Rajeev K. and Nair, Aishwarya R. and Puthiyamadam, Anoop and Valappil, Prajeesh Kooloth and Prasannakumari, Adrash Velayudhan Pillai and Chodankar, Kalpana and Damare, Samir and Agrawal, Ved Varun and Chaudhary, Kumardeep and Agrawal, Anurag and Sengupta, Shantanu and Dash, Debasis (2022) A machine learning-based approach to determine infection status in recipients of BBV152 (Covaxin) whole-virion inactivated SARS-CoV-2 vaccine for serological surveys. COMPUTERS IN BIOLOGY AND MEDICINE, 146.