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DOI 10.1016/j.compbiomed.2022.105419

Медицинская библиотека · Computers in biology and medicine · 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.

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, A Kiran Kumar, 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, Ray Chaudhuri Saumya, 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, Tapadar Yasmin Begam, Babu Elapavalooru Vssk, Sukumaran Rajeev K, Nair Aishwarya R, Puthiyamadam Anoop, Valappil Prajeesh Kooloth, Pillai Prasannakumari Adrash Velayudhan, Chodankar Kalpana, Damare Samir, Agrawal Ved Varun, Chaudhary Kumardeep, Agrawal Anurag, Sengupta Shantanu, Dash Debasis

СтатьяПоддержка исследования: вне правительства СШАПеревод в очереди

Аннотация

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.

Для цитирования: 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, A Kiran Kumar, 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, Ray Chaudhuri Saumya, 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, Tapadar Yasmin Begam, Babu Elapavalooru Vssk, Sukumaran Rajeev K, Nair Aishwarya R, Puthiyamadam Anoop, Valappil Prajeesh Kooloth, Pillai Prasannakumari Adrash Velayudhan, Chodankar Kalpana, Damare Samir, Agrawal Ved Varun, Chaudhary Kumardeep, Agrawal Anurag, Sengupta Shantanu, Dash Debasis. 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. 2022. DOI: 10.1016/j.compbiomed.2022.105419.

Предметные рубрики MeSH

COVID-19COVID-19 VaccinesHumansMachine LearningPandemicsSARS-CoV-2Vaccines, InactivatedViral VaccinesVirion
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