TY  - JOUR
N1  - The copyright of this article belongs to Elsevier
ID  - open3067
UR  - https://www.sciencedirect.com/science/article/pii/S0010482522002116?via%3Dihub
A1  - Singh, Prateek
A1  - Ujjainiya, Rajat
A1  - Prakash, Satyartha
A1  - Naushin, Salwa
A1  - Sardana, Viren
A1  - Bhatheja, Nitin
A1  - Singh, Ajay Pratap
A1  - Barman, Joydeb
A1  - Kumar, Kartik
A1  - Gayali, Saurabh
A1  - Khan, Raju
A1  - Rawat, Birendra Singh
A1  - Tallapaka, Karthik Bharadwaj
A1  - Anumalla, Mahesh
A1  - Lahiri, Amit
A1  - Kar, Susanta
A1  - Bhosale, Vivek
A1  - Srivastava, Mrigank
A1  - Mugale, Madhav Nilakanth
A1  - Pandey, C. P.
A1  - Khan, Shaziya
A1  - Katiyar, Shivani
A1  - Raj, Desh
A1  - Ishteyaque, Sharmeen
A1  - Khanka, Sonu
A1  - Rani, Ankita
A1  - Promila, .
A1  - Sharma, Jyotsna
A1  - Seth, Anuradha
A1  - Dutta, Mukul
A1  - Saurabh, Nishant
A1  - Veerapandian, Murugan
A1  - Venkatachalam, Ganesh
A1  - Bansal, Deepak
A1  - Gupta, Dinesh
A1  - Halami, Prakash M.
A1  - Peddha, Muthukumar Serva
A1  - Veeranna, Ravindra P.
A1  - Pal, Anirban
A1  - Singh, Ranvijay Kumar
A1  - Anandasadagopan, Suresh Kumar
A1  - Karuppanan, Parimala
A1  - Rahman, Syed Nasar
A1  - Selvakumar, Gopika
A1  - Venkatesan, Subramanian
A1  - Karmakar, Malay Kumar
A1  - Sardana, Harish Kumar
A1  - Kothari, Anamika
A1  - Parihar, Devendra Singh
A1  - Thakur, Anupma
A1  - Saifi, Anas
A1  - Gupta, Naman
A1  - Singh, Yogita
A1  - Reddu, Ritu
A1  - Gautam, Rizul
A1  - Mishra, Anuj
A1  - Mishra, Avinash
A1  - Gogeri, Iranna
A1  - Rayasam, Geethavani
A1  - Padwad, Yogendra
A1  - Patial, Vikram
A1  - Hallan, Vipin
A1  - Singh, Damanpreet
A1  - Tirpude, Narendra
A1  - Chakrabarti, Partha
A1  - Maity, Sujay Krishna
A1  - Ganguly, Dipyaman
A1  - Sistla, Ramakrishna
A1  - Balthu, Narender Kumar
A1  - Kumar, Kiran A.
A1  - Ranjith, Siva
A1  - Kumar, B. Vijay
A1  - Jamwal, Piyush Singh
A1  - Wali, Anshu
A1  - Ahmed, Sajad
A1  - Chouhan, Rekha
A1  - Gandhi, Sumit G.
A1  - Sharma, Nancy
A1  - Rai, Garima
A1  - Irshad, Faisal
A1  - Jamwal, Vijay Lakshmi
A1  - Paddar, Masroor Ahmad
A1  - Khan, Sameer Ullah
A1  - Malik, Fayaz
A1  - Ghosh, Debashish
A1  - Thakkar, Ghanshyam
A1  - Barik, S. K.
A1  - Tripathi, Prabhanshu
A1  - Satija, Yatendra Kumar
A1  - Mohanty, Sneha
A1  - Khan, Md Tauseef
A1  - Subudhi, Umakanta
A1  - Sen, Pradip
A1  - Kumar, Rashmi
A1  - Bhardwaj, Anshu
A1  - Gupta, Pawan
A1  - Sharma, Deepak
A1  - Tuli, Amit
A1  - Chaudhuri, Saumya Ray
A1  - Krishnamurthi, Srinivasan
A1  - Prakash, L.
A1  - Rao, Ch
A1  - Singh, B. N.
A1  - Chaurasiya, Arvindkumar
A1  - Chaurasiya, Meera
A1  - Bhadange, Mayuri
A1  - Likhitkar, Bhagyashree
A1  - Mohite, Sharada
A1  - Patil, Yogita
A1  - Kulkarni, Mahesh
A1  - Joshi, Rakesh
A1  - Pandya, Vaibhav
A1  - Mahajan, Sachin
A1  - Patil, Amita
A1  - Samson, Rachel
A1  - Vare, Tejas
A1  - Dharne, Mahesh
A1  - Giri, Ashok
A1  - Mahajan, Sachin
A1  - Paranjape, Shilpa
A1  - Sastry, G. Narahari
A1  - Kalita, Jatin
A1  - Phukan, Tridip
A1  - Manna, Prasenjit
A1  - Romi, Wahengbam
A1  - Bharali, Pankaj
A1  - Ozah, Dibyajyoti
A1  - Sahu, RaviKumar
A1  - Dutta, Prachurjya
A1  - Singh, Moirangthem Goutam
A1  - Gogoi, Gayatri
A1  - Tapadar, Yasmin Begam
A1  - Babu, Elapavalooru V. S. S. K.
A1  - Sukumaran, Rajeev K.
A1  - Nair, Aishwarya R.
A1  - Puthiyamadam, Anoop
A1  - Valappil, Prajeesh Kooloth
A1  - Prasannakumari, Adrash Velayudhan Pillai
A1  - Chodankar, Kalpana
A1  - Damare, Samir
A1  - Agrawal, Ved Varun
A1  - Chaudhary, Kumardeep
A1  - Agrawal, Anurag
A1  - Sengupta, Shantanu
A1  - Dash, Debasis
Y1  - 2022/07//
N2  - 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.
PB  - Elsevier
JF  - COMPUTERS IN BIOLOGY AND MEDICINE
VL  - 146
KW  - BBV152; COVID-19; Covaxin; Ensemble methods; Infection; Machine learning; SARS-CoV-2.
TI  - A machine learning-based approach to determine infection status in recipients of BBV152 (Covaxin) whole-virion inactivated SARS-CoV-2 vaccine for serological surveys
ER  -