A machine learning-based approach to determine infection status in recipients of BBV152 (Covaxin) whole-virion inactivated SARS-CoV-2 vaccine for serological surveys

被引:12
作者
Singh, Prateek [1 ,2 ]
Ujjainiya, Rajat [1 ,2 ]
Prakash, Satyartha [1 ]
Naushin, Salwa [1 ,2 ]
Sardana, Viren [1 ,2 ]
Bhatheja, Nitin [1 ]
Singh, Ajay Pratap [1 ,2 ]
Barman, Joydeb [1 ]
Kumar, Kartik [1 ]
Gayali, Saurabh [1 ]
Khan, Raju [2 ,3 ]
Rawat, Birendra Singh [4 ]
Tallapaka, Karthik Bharadwaj [5 ]
Anumalla, Mahesh [5 ]
Lahiri, Amit [2 ,6 ]
Kar, Susanta [2 ,6 ]
Bhosale, Vivek [2 ,6 ]
Srivastava, Mrigank [2 ,6 ]
Mugale, Madhav Nilakanth [2 ,6 ]
Pandey, C. P. [2 ,6 ]
Khan, Shaziya [2 ,6 ]
Katiyar, Shivani [2 ,6 ]
Raj, Desh [2 ,6 ]
Ishteyaque, Sharmeen [2 ,6 ]
Khanka, Sonu [2 ,6 ]
Rani, Ankita [2 ,6 ]
Promila [2 ,6 ]
Sharma, Jyotsna [2 ,6 ]
Seth, Anuradha [2 ,6 ]
Dutta, Mukul [2 ,6 ]
Saurabh, Nishant [6 ]
Veerapandian, Murugan [2 ,7 ]
Venkatachalam, Ganesh [2 ,7 ]
Bansal, Deepak [2 ,8 ]
Gupta, Dinesh [8 ]
Halami, Prakash M. [2 ,9 ]
Peddha, Muthukumar Serva [2 ,9 ]
Veeranna, Ravindra P. [2 ,9 ]
Pal, Anirban [2 ,10 ]
Singh, Ranvijay Kumar [2 ,11 ]
Anandasadagopan, Suresh Kumar [2 ,12 ]
Karuppanan, Parimala [12 ]
Rahman, Syed Nasar [2 ,12 ]
Selvakumar, Gopika [12 ]
Venkatesan, Subramanian [2 ,12 ]
Karmakar, Malay Kumar [2 ,13 ]
Sardana, Harish Kumar [2 ,14 ]
Kothari, Anamika [14 ]
Parihar, Devendra Singh [2 ,14 ]
Thakur, Anupma [2 ,14 ]
机构
[1] CSIR Inst Genom & Integrat Biol, New Delhi, India
[2] Acad Sci & Innovat Res AcSIR, Ghaziabad 201002, India
[3] CSIR Adv Mat & Proc Res Inst, Bhopal, India
[4] CSIR Cent Bldg Res Inst, Roorkee, Uttar Pradesh, India
[5] CSIR Ctr Cellular Mol Biol, Hyderabad, India
[6] CSIR Cent Drug Res Inst, Lucknow, Uttar Pradesh, India
[7] CSIR Cent Electrochem Res Inst, Karaikkudi, Tamil Nadu, India
[8] CSIR Cent Elect Engn Rese Arch Inst, Pilani, Rajasthan, India
[9] CSIR Cent Food Technol Res Inst, Mysore, Karnataka, India
[10] CSIR Cent Inst Med Aromat Plants, Lucknow, Uttar Pradesh, India
[11] CSIR Cent Inst Min & Fuel Res, Dhanbad, Bihar, India
[12] CSIR Cent Leather Res Inst, Chennai, Tamil Nadu, India
[13] CSIR Cent Mech Engn Res Inst, Durgapur, India
[14] CSIR Cent Sci Instruments Org, Chandigarh, India
[15] CSIR Cent Salt Marine Chem Res Inst, Bhavnagar, Gujarat, India
[16] CSIR Fourth Paradigm Inst, Bengaluru, India
[17] CSIR Headquarters, Rafi Marg, New Delhi, India
[18] CSIR Inst Himalayan Bioresource Technol, Palampur, Himachal Prades, India
[19] CSIR Indian Inst Chem Biol, Kolkata, India
[20] CSIR Indian Inst Chem Technol, Hyderabad, India
[21] CSIR Indian Inst Integrat Med, Jammu, Jammu & Kashmir, India
[22] CSIR Indian Inst Petr, Dehra Dun, Uttarakhand, India
[23] CSIR Indian Inst Toxicol Res, Lucknow, Uttar Pradesh, India
[24] CSIR Inst Minerals & Mat Technol, Bhubaneswar, India
[25] CSIR Inst Microbial Technol, Chandigarh, India
[26] CSIR Natl Aerosp Labs, Bengaluru, India
[27] CSIR Natl Bot Res Inst, Lucknow, Uttar Pradesh, India
[28] CSIR Natl Chem Lab, Pune, Maharashtra, India
[29] CSIR Natl Environm Engn Res Inst, Nagpur, Maharashtra, India
[30] CSIR North East Inst Sci & Technol, Jorhat, Assam, India
[31] CSIR Natl Geophys Res Inst, Hyderabad, India
[32] CSIR Natl Inst Interdisciplinary Sci & Technol, Thiruvananthapuram, Kerala, India
[33] CSIR Natl Inst Oceanog, Panaji, Goa, India
[34] CSIR Natl Phys Lab, New Delhi, India
关键词
COVID-19; SARS-CoV-2; Covaxin; BBV152; Machine learning; Ensemble methods; Infection;
D O I
10.1016/j.compbiomed.2022.105419
中图分类号
Q [生物科学];
学科分类号
07 ; 0710 ; 09 ;
摘要
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.
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