creators_name: Singh, Prateek creators_name: Ujjainiya, Rajat creators_name: Prakash, Satyartha creators_name: Naushin, Salwa creators_name: Sardana, Viren creators_name: Bhatheja, Nitin creators_name: Singh, Ajay Pratap creators_name: Barman, Joydeb creators_name: Kumar, Kartik creators_name: Gayali, Saurabh creators_name: Khan, Raju creators_name: Rawat, Birendra Singh creators_name: Tallapaka, Karthik Bharadwaj creators_name: Anumalla, Mahesh creators_name: Lahiri, Amit creators_name: Kar, Susanta creators_name: Bhosale, Vivek creators_name: Srivastava, Mrigank creators_name: Mugale, Madhav Nilakanth creators_name: Pandey, C. P. creators_name: Khan, Shaziya creators_name: Katiyar, Shivani creators_name: Raj, Desh creators_name: Ishteyaque, Sharmeen creators_name: Khanka, Sonu creators_name: Rani, Ankita creators_name: Promila, . creators_name: Sharma, Jyotsna creators_name: Seth, Anuradha creators_name: Dutta, Mukul creators_name: Saurabh, Nishant creators_name: Veerapandian, Murugan creators_name: Venkatachalam, Ganesh creators_name: Bansal, Deepak creators_name: Gupta, Dinesh creators_name: Halami, Prakash M. creators_name: Peddha, Muthukumar Serva creators_name: Veeranna, Ravindra P. creators_name: Pal, Anirban creators_name: Singh, Ranvijay Kumar creators_name: Anandasadagopan, Suresh Kumar creators_name: Karuppanan, Parimala creators_name: Rahman, Syed Nasar creators_name: Selvakumar, Gopika creators_name: Venkatesan, Subramanian creators_name: Karmakar, Malay Kumar creators_name: Sardana, Harish Kumar creators_name: Kothari, Anamika creators_name: Parihar, Devendra Singh creators_name: Thakur, Anupma creators_name: Saifi, Anas creators_name: Gupta, Naman creators_name: Singh, Yogita creators_name: Reddu, Ritu creators_name: Gautam, Rizul creators_name: Mishra, Anuj creators_name: Mishra, Avinash creators_name: Gogeri, Iranna creators_name: Rayasam, Geethavani creators_name: Padwad, Yogendra creators_name: Patial, Vikram creators_name: Hallan, Vipin creators_name: Singh, Damanpreet creators_name: Tirpude, Narendra creators_name: Chakrabarti, Partha creators_name: Maity, Sujay Krishna creators_name: Ganguly, Dipyaman creators_name: Sistla, Ramakrishna creators_name: Balthu, Narender Kumar creators_name: Kumar, Kiran A. creators_name: Ranjith, Siva creators_name: Kumar, B. Vijay creators_name: Jamwal, Piyush Singh creators_name: Wali, Anshu creators_name: Ahmed, Sajad creators_name: Chouhan, Rekha creators_name: Gandhi, Sumit G. creators_name: Sharma, Nancy creators_name: Rai, Garima creators_name: Irshad, Faisal creators_name: Jamwal, Vijay Lakshmi creators_name: Paddar, Masroor Ahmad creators_name: Khan, Sameer Ullah creators_name: Malik, Fayaz creators_name: Ghosh, Debashish creators_name: Thakkar, Ghanshyam creators_name: Barik, S. K. creators_name: Tripathi, Prabhanshu creators_name: Satija, Yatendra Kumar creators_name: Mohanty, Sneha creators_name: Khan, Md Tauseef creators_name: Subudhi, Umakanta creators_name: Sen, Pradip creators_name: Kumar, Rashmi creators_name: Bhardwaj, Anshu creators_name: Gupta, Pawan creators_name: Sharma, Deepak creators_name: Tuli, Amit creators_name: Chaudhuri, Saumya Ray creators_name: Krishnamurthi, Srinivasan creators_name: Prakash, L. creators_name: Rao, Ch creators_name: Singh, B. N. creators_name: Chaurasiya, Arvindkumar creators_name: Chaurasiya, Meera creators_name: Bhadange, Mayuri creators_name: Likhitkar, Bhagyashree creators_name: Mohite, Sharada creators_name: Patil, Yogita creators_name: Kulkarni, Mahesh creators_name: Joshi, Rakesh creators_name: Pandya, Vaibhav creators_name: Mahajan, Sachin creators_name: Patil, Amita creators_name: Samson, Rachel creators_name: Vare, Tejas creators_name: Dharne, Mahesh creators_name: Giri, Ashok creators_name: Mahajan, Sachin creators_name: Paranjape, Shilpa creators_name: Sastry, G. Narahari creators_name: Kalita, Jatin creators_name: Phukan, Tridip creators_name: Manna, Prasenjit creators_name: Romi, Wahengbam creators_name: Bharali, Pankaj creators_name: Ozah, Dibyajyoti creators_name: Sahu, RaviKumar creators_name: Dutta, Prachurjya creators_name: Singh, Moirangthem Goutam creators_name: Gogoi, Gayatri creators_name: Tapadar, Yasmin Begam creators_name: Babu, Elapavalooru V. S. S. K. creators_name: Sukumaran, Rajeev K. creators_name: Nair, Aishwarya R. creators_name: Puthiyamadam, Anoop creators_name: Valappil, Prajeesh Kooloth creators_name: Prasannakumari, Adrash Velayudhan Pillai creators_name: Chodankar, Kalpana creators_name: Damare, Samir creators_name: Agrawal, Ved Varun creators_name: Chaudhary, Kumardeep creators_name: Agrawal, Anurag creators_name: Sengupta, Shantanu creators_name: Dash, Debasis type: article datestamp: 2022-12-15 10:14:59 lastmod: 2022-12-15 10:14:59 metadata_visibility: show 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 ispublished: pub subjects: QR keywords: BBV152; COVID-19; Covaxin; Ensemble methods; Infection; Machine learning; SARS-CoV-2. note: The copyright of this article belongs to Elsevier 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. date: 2022-07 date_type: published publication: COMPUTERS IN BIOLOGY AND MEDICINE volume: 146 publisher: Elsevier refereed: TRUE official_url: https://www.sciencedirect.com/science/article/pii/S0010482522002116?via%3Dihub citation: 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.