A machine learning-based approach to determine infection status in recipients of BBV152 whole virion inactivated SARS-CoV-2 vaccine for serological surveys
Authors: Singh Prateek , Ujjainiya Rajat , Prakash Satyartha , Naushin Salwa , Sardana Viren , Bhatheja Nitin , Singh Ajay Pratap , Barman Joydeb , Kumar Kartik , Khan Raju , 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 , Sundaram Gopinath M , Veeranna Ravindra P , Pal Anirban , Singh Ranvijay Kumar , Anandasadagopan Suresh Kumar , Karuppanan Parimala , Rahman Syed Nasar , Selvakumar Gopika , Venkatesan Subramanian , Karmakar MalayKumar , Sardana Harish Kumar , Kothari Animika , Parihar DevendraSingh , 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 , Kiran Kumar 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 MasroorAhmad , 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 V , Singh B N , Chaurasiya Arvindkumar , Chaurasiyar 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 Ravi Kumar , Dutta Prachurjya , Singh Moirangthem Goutam , Gogoi Gayatri , BegamTapadar Yasmin , Babu Elapavalooru VSSK , Sukumaran Rajeev K , Nair Aishwarya R , Puthiyamadam Anoop , Valappil PrajeeshKooloth , Pillai Prasannakumari Adrash Velayudhan , Chodankar Kalpana , Damare Samir , Agrawal Ved Varun , Chaudhary Kumardeep , Agrawal Anurag , Sengupta Shantanu , Dash Debasis 
Published: December 2021

Data science has been an invaluable part of the COVID-19 pandemic response with multiple applications, ranging from tracking viral evolution to understanding the effectiveness of interventions. Asymptomatic breakthrough infections have been a major problem during the ongoing surge of Delta variant globally. Serological discrimination of vaccine response from infection has so far been limited to Spike protein vaccines used in the higher-income regions. Here, we show for the first time how statistical and machine learning (ML) approaches can discriminate SARS-CoV-2 infection from immune response to an inactivated whole virion vaccine (BBV152, Covaxin, India), thereby permitting real-world vaccine effectiveness assessments from cohort-based serosurveys in Asia and Africa where such vaccines are commonly used. Briefly, we accessed serial data on Anti-S and Anti-NC antibody concentration values, along with age, sex, number of doses, and number of days since the last vaccine dose for 1823 Covaxin recipients. An ensemble ML model, incorporating a consensus clustering approach alongside the support vector machine (SVM) model, was built on 1063 samples where reliable qualifying data existed, and then applied to the entire dataset. Of 1448 self-reported negative subjects, 724 were classified as infected. Since the vaccine contains wild-type virus and the antibodies induced will neutralize wild type much better than Delta variant, we determined the relative ability of a random subset of such samples to neutralize Delta versus wild type strain. In 100 of 156 samples, where ML prediction differed from self-reported uninfected status, Delta variant, was neutralized more effectively than the wild type, which cannot happen without infection. The fraction rose to 71.8% (28 of 39) in 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.