A machine learning-based approach to determine infection status in recipients of BBV152 (Covaxin) whole-virion inactivated SARS-CoV-2 vaccine for serological surveys
Prateek Singh, Rajat Ujjainiya, Satyartha Prakash, Salwa Naushin, Viren Sardana, Nitin Bhatheja, Ajay Pratap Singh, Joydeb Barman, Kartik Kumar, Saurabh Gayali, Raju Khan, Birendra Singh Rawat, Karthik Bharadwaj Tallapaka, Mahesh Anumalla, Amit Lahiri, S.K. Kar, Vivek Bhosale, Mrigank Srivastava, Madhav Nilakanth Mugale, Chandra Prakash Pandey, Shaziya Khan, Shivani Katiyar, Desh Raj, Sharmeen Ishteyaque, Sonu Khanka, Ankita Rani, Promila Promila, Jyotsna Sharma, Anuradha Seth, Mukul Dutta, Nishant Saurabh, Murugan Veerapandian, Ganesh Venkatachalam, Deepak Bansal, Dinesh Gupta, Prakash M. Halami, Muthukumar Serva Peddha, Ravindra P. Veeranna, Anirban Pal, Ranvijay Singh, Suresh Kumar Anandasadagopan, Parimala Karuppanan, Syed Nasar Rahman, Gopika Selvakumar, Subramanian Venkatesan, Malay Karmakar, H. K. Sardana, Anamika Kothari, Devendra Singh Parihar, Anupma Thakur, Anas Saifi, Naman Gupta, Yogita Singh, Ritu Reddu, Rizul Gautam, Anuj Mishra, Avinash Mishra, Iranna Gogeri, Geethavani Rayasam, Yogendra Padwad, Vikram Patial, Vipin Hallan, Damanpreet Singh, Narendra Vijay Tirpude, Partha Chakrabarti, Sujay Krishna Maity, Dipyaman Ganguly, Ramakrishna Sistla, Narender Kumar Balthu, Kiran Kumar A, Siva Ranjith, B. Vijay Kumar, Piyush Singh Jamwal, Anshu Wali, Sajad Ahmed, Rekha Chouhan, Sumit G. Gandhi, Nancy Sharma, Garima Rai, Faisal Irshad, Vijay Lakshmi Jamwal, Masroor Ahmad Paddar, Sameer Ullah Khan, Fayaz Malik, Debashish Ghosh, Ghanshyam Thakkar, Saroj Kanta Barik, Prabhanshu Tripathi, Yatendra Kumar Satija, Sneha Mohanty, Md. Tauseef Khan, Umakanta Subudhi, Pradip Sen, Rashmi Kumar, Anshu Bhardwaj, Pawan Gupta, Deepak Sharma, Amit Tuli, Saumya Ray Chaudhuri, Srinivasan Krishnamurthi
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.