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1. Spatio-temporal analysis of air pollution dynamics over Bangalore city during second wave of COVID-19

Author

Iranna Gogeri, K.C. Gouda, and S.T. Aruna

Subjects
Spatial modelling, Air pollution, Inverse distance weighting (IDW), COVID-19, Lockdown, Pre-lockdown, Geology, QE1-996.5, Geophysics. Cosmic physics, QC801-809
Abstract

The country wide lockdown implemented during 27th April to 14th June 2021 in order to prevent the spread of COVID-19 during the second wave in India. Effect of the restricted lockdown resulted in improved air quality. This study focuses on analyzing the spatio-temporal distribution analysis of major air pollutant concentration over Bangalore city in India. The inverse distance weighting (IDW) method is implemented for the spatial analysis in order to quantify the distribution of the pollutant concentrations at each location in the Urban city of Bangalore. The research considers the distinct periods of pre-lockdown and lockdown during the second wave of COVID-19 pandemic in 2021 to investigate the impact of reduced human activities on air quality over the city. The study mainly utilizes the air pollution data collected from Central Pollution Control Board (CPCB) monitoring stations across Bangalore, including measurements of pollutants such as PM2.5, PM10, O3, NO2, SO2, and CO. The IDW method is implemented to create the high-resolution pollution concentration maps for both the pre-lockdown and lockdown periods. This spatial distribution provides valuable insights into the variations in the pollution levels though out the Bangalore city. The comparative analysis of the concentration maps reveals significant changes in air pollution levels between the two periods; similarly, the temporal weekly average analysis also witnessed negative anomalies during the lockdown weeks. The results indicate substantial reductions in pollutant concentrations during the second wave COVID-19 lockdown, attributed to decreased vehicular emissions, industrial activities, and construction operations. The pre-lockdown period serves as a baseline for assessing the improvements in air quality during the lockdown. The spatio-temporal modeling approach enhances our understanding of the distribution patterns of air pollutants across the Bangalore metropolitan city. The findings underscore the potential benefits of implementing sustainable strategies to maintain improved air quality even after the pandemic subsides.

Published
2024
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2. Insights from a Pan India Sero-Epidemiological survey (Phenome-India Cohort) for SARS-CoV2

Author

Salwa Naushin, Viren Sardana, Rajat Ujjainiya, Nitin Bhatheja, Rintu Kutum, Akash Kumar Bhaskar, Shalini Pradhan, Satyartha Prakash, Raju Khan, Birendra Singh Rawat, Karthik Bharadwaj Tallapaka, Mahesh Anumalla, Giriraj Ratan Chandak, Amit Lahiri, Susanta Kar, Shrikant Ramesh Mulay, Madhav Nilakanth Mugale, Mrigank Srivastava, Shaziya Khan, Anjali Srivastava, Bhawana Tomar, Murugan Veerapandian, Ganesh Venkatachalam, Selvamani Raja Vijayakumar, Ajay Agarwal, Dinesh Gupta, Prakash M Halami, Muthukumar Serva Peddha, Gopinath M Sundaram, Ravindra P Veeranna, Anirban Pal, Vinay Kumar Agarwal, Anil Ku Maurya, Ranvijay Kumar Singh, Ashok Kumar Raman, Suresh Kumar Anandasadagopan, Parimala Karuppanan, Subramanian Venkatesan, Harish Kumar Sardana, Anamika Kothari, Rishabh Jain, Anupama Thakur, Devendra Singh Parihar, Anas Saifi, Jasleen Kaur, Virendra Kumar, Avinash Mishra, Iranna Gogeri, Geethavani Rayasam, Praveen Singh, Rahul Chakraborty, Gaura Chaturvedi, Pinreddy Karunakar, Rohit Yadav, Sunanda Singhmar, Dayanidhi Singh, Sharmistha Sarkar, Purbasha Bhattacharya, Sundaram Acharya, Vandana Singh, Shweta Verma, Drishti Soni, Surabhi Seth, Sakshi Vashisht, Sarita Thakran, Firdaus Fatima, Akash Pratap Singh, Akanksha Sharma, Babita Sharma, Manikandan Subramanian, Yogendra S Padwad, Vipin Hallan, Vikram Patial, Damanpreet Singh, Narendra Vijay Tripude, Partha Chakrabarti, Sujay Krishna Maity, Dipyaman Ganguly, Jit Sarkar, Sistla Ramakrishna, Balthu Narender Kumar, Kiran A Kumar, Sumit G Gandhi, Piyush Singh Jamwal, Rekha Chouhan, Vijay Lakshmi Jamwal, Nitika Kapoor, Debashish Ghosh, Ghanshyam Thakkar, Umakanta Subudhi, Pradip Sen, Saumya Ray Chaudhury, Rashmi Kumar, Pawan Gupta, Amit Tuli, Deepak Sharma, Rajesh P Ringe, Amarnarayan D, Mahesh Kulkarni, Dhansekaran Shanmugam, Mahesh S Dharne, Sayed G Dastager, Rakesh Joshi, Amita P Patil, Sachin N Mahajan, Abujunaid Habib Khan, Vasudev Wagh, Rakesh Kumar Yadav, Ajinkya Khilari, Mayuri Bhadange, Arvindkumar H Chaurasiya, Shabda E Kulsange, Krishna Khairnar, Shilpa Paranjape, Jatin Kalita, Narahari G Sastry, Tridip Phukan, Prasenjit Manna, Wahengbam Romi, Pankaj Bharali, Dibyajyoti Ozah, Ravi Kumar Sahu, Elapavalooru VSSK Babu, Rajeev Sukumaran, Aiswarya R Nair, Prajeesh Kooloth Valappil, Anoop Puthiyamadam, Adarsh Velayudhanpillai, Kalpana Chodankar, Samir Damare, Yennapu Madhavi, Ved Varun Aggarwal, Sumit Dahiya, Anurag Agrawal, Debasis Dash, and Shantanu Sengupta

Subjects
SARS-CoV2, antibody stability, sero-prevalence, neutralizing antibody, Medicine, Science, Biology (General), QH301-705.5
Abstract

To understand the spread of SARS-CoV2, in August and September 2020, the Council of Scientific and Industrial Research (India) conducted a serosurvey across its constituent laboratories and centers across India. Of 10,427 volunteers, 1058 (10.14%) tested positive for SARS-CoV2 anti-nucleocapsid (anti-NC) antibodies, 95% of which had surrogate neutralization activity. Three-fourth of these recalled no symptoms. Repeat serology tests at 3 (n = 607) and 6 (n = 175) months showed stable anti-NC antibodies but declining neutralization activity. Local seropositivity was higher in densely populated cities and was inversely correlated with a 30-day change in regional test positivity rates (TPRs). Regional seropositivity above 10% was associated with declining TPR. Personal factors associated with higher odds of seropositivity were high-exposure work (odds ratio, 95% confidence interval, p value: 2.23, 1.92–2.59,

Published
2021
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3. A machine learning-based approach to determine infection status in recipients of BBV152 whole virion inactivated SARS-CoV-2 vaccine for serological surveys

Author

Prateek Singh, Rajat Ujjainiya, Satyartha Prakash, Salwa Naushin, Viren Sardana, Nitin Bhatheja, Ajay Pratap Singh, Joydeb Barman, Kartik Kumar, Raju Khan, Karthik Bharadwaj Tallapaka, Mahesh Anumalla, Amit Lahiri, Susanta Kar, Vivek Bhosale, Mrigank Srivastava, Madhav Nilakanth Mugale, C.P Pandey, Shaziya Khan, Shivani Katiyar, Desh Raj, Sharmeen Ishteyaque, Sonu Khanka, Ankita Rani, null Promila, Jyotsna Sharma, Anuradha Seth, Mukul Dutta, Nishant Saurabh, Murugan Veerapandian, Ganesh Venkatachalam, Deepak Bansal, Dinesh Gupta, Prakash M Halami, Muthukumar Serva Peddha, Gopinath M Sundaram, Ravindra P Veeranna, Anirban Pal, Ranvijay Kumar Singh, Suresh Kumar Anandasadagopan, Parimala Karuppanan, Syed Nasar Rahman, Gopika Selvakumar, Subramanian Venkatesan, MalayKumar Karmakar, Harish Kumar Sardana, Animika Kothari, DevendraSingh 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 Tirpude, Partha Chakrabarti, Sujay Krishna Maity, Dipyaman Ganguly, Ramakrishna Sistla, Narender Kumar Balthu, A Kiran Kumar, 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, MasroorAhmad Paddar, Sameer Ullah Khan, Fayaz Malik, Debashish Ghosh, Ghanshyam Thakkar, S K 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, L Prakash, Ch V Rao, B N Singh, Arvindkumar Chaurasiya, Meera Chaurasiyar, Mayuri Bhadange, Bhagyashree Likhitkar, Sharada Mohite, Yogita Patil, Mahesh Kulkarni, Rakesh Joshi, Vaibhav Pandya, Sachin Mahajan, Amita Patil, Rachel Samson, Tejas Vare, Mahesh Dharne, Ashok Giri, Shilpa Paranjape, G. Narahari Sastry, Jatin Kalita, Tridip Phukan, Prasenjit Manna, Wahengbam Romi, Pankaj Bharali, Dibyajyoti Ozah, Ravi Kumar Sahu, Prachurjya Dutta, Moirangthem Goutam Singh, Gayatri Gogoi, Yasmin BegamTapadar, Elapavalooru VSSK Babu, Rajeev K Sukumaran, Aishwarya R Nair, Anoop Puthiyamadam, PrajeeshKooloth Valappil, Adrash Velayudhan Pillai Prasannakumari, Kalpana Chodankar, Samir Damare, Ved Varun Agrawal, Kumardeep Chaudhary, Anurag Agrawal, Shantanu Sengupta, and Debasis Dash

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 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.

Published
2021

4. Assessment of Aerosol Optical Depth over Indian Subcontinent during COVID-19 lockdown (March-May 2020)

Author

Krushna Chandra Gouda, Iranna Gogeri, and Aruna Singanahalli ThippaReddy

Subjects
Aerosols, Air Pollutants, SARS-CoV-2, COVID-19, India, General Medicine, Management, Monitoring, Policy and Law, Pollution, Air Pollution, Communicable Disease Control, Humans, Pandemics, General Environmental Science, Environmental Monitoring
Abstract

The COVID-19 pandemic has created a major threat to human beings and huge losses over the globe. In order to control the pandemic spread, almost all parts of the world imposed lockdown. The imposed lockdown drastically impacted on reduction in the atmospheric pollutions and also resulted in net decrease in aerosol optical depth (AOD) in the atmosphere. In this study, the reduction in the AOD during the COVID-19 lockdown over the Indian subcontinent is being assessed using the moderate resolution imaging spectroradiometer (MODIS) satellite data available in Giovanni version 4.34 developed by NASA. The long-term mean analysis is computed considering 20 years (i.e., 2000-2019) data on Terra platform with a temporal resolution of daily and monthly and spatial resolution of 1 degree. The dataset of AOD with a temporal resolution of monthly was used for investigation of AOD anomaly for March, April and May 2020, and the seasonal variation (March to May 2020) is also assessed. Similarly, the daily scale dataset was used to investigate the percentage change in AOD during pre-lockdown and lockdown period with respect to long-term mean. The key findings in the present study show that reduction in AOD level over Indian subcontinent is approximately 14.75% during the lockdown period with spatial variation in the magnitude from region to region. The level of AOD is greatly reduced in the northern part of India (~ 22.53%), whereas changes in the southern part of India are much less (~ -0.31%); this may be due to ongoing anthropogenic activities during the lockdown period in this region. Furthermore, a positive AOD anomaly was observed in the eastern and central regions of India (i.e., over the states of Odisha, Chhattisgarh, Telangana, Jharkhand, West Bengal, Part of Maharashtra and Karnataka). However, negative AOD anomaly was observed in the north and northwest regions of India, whereas not much change in the AOD anomaly in other parts of the country. The overall assessment of the AOD level shows a net decrease over the Indian subcontinent during the lockdown period, i.e., March to May 2020. This kind of assessment study will surely help the government for the sustainable policy decisions for atmospheric pollution control by implementing proper lockdown procedures over various parts of the country.

Published
2021

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

Author

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, Susanta Kar, Vivek Bhosale, Mrigank Srivastava, Madhav Nilakanth Mugale, C.P. Pandey, Shaziya Khan, Shivani Katiyar, Desh Raj, Sharmeen Ishteyaque, Sonu Khanka, Ankita Rani, null 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 Kumar Singh, Suresh Kumar Anandasadagopan, Parimala Karuppanan, Syed Nasar Rahman, Gopika Selvakumar, Subramanian Venkatesan, Malay Kumar Karmakar, Harish Kumar 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 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, S.K. 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, L. Prakash, Ch V. Rao, B.N. Singh, Arvindkumar Chaurasiya, Meera Chaurasiyar, Mayuri Bhadange, Bhagyashree Likhitkar, Sharada Mohite, Yogita Patil, Mahesh Kulkarni, Rakesh Joshi, Vaibhav Pandya, Sachin Mahajan, Amita Patil, Rachel Samson, Tejas Vare, Mahesh Dharne, Ashok Giri, Shilpa Paranjape, G. Narahari Sastry, Jatin Kalita, Tridip Phukan, Prasenjit Manna, Wahengbam Romi, Pankaj Bharali, Dibyajyoti Ozah, Ravi Kumar Sahu, Prachurjya Dutta, Moirangthem Goutam Singh, Gayatri Gogoi, Yasmin Begam Tapadar, Elapavalooru VSSK. Babu, Rajeev K. Sukumaran, Aishwarya R. Nair, Anoop Puthiyamadam, Prajeesh Kooloth Valappil, Adrash Velayudhan Pillai Prasannakumari, Kalpana Chodankar, Samir Damare, Ved Varun Agrawal, Kumardeep Chaudhary, Anurag Agrawal, Shantanu Sengupta, and Debasis Dash

Subjects
Machine Learning, COVID-19 Vaccines, Vaccines, Inactivated, SARS-CoV-2, Virion, COVID-19, Humans, Viral Vaccines, Health Informatics, Pandemics, Computer Science Applications
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.

Published
2022

6. Author response: Insights from a Pan India Sero-Epidemiological survey (Phenome-India Cohort) for SARS-CoV2

Author

Mrigank Srivastava, Subramanian Venkatesan, Vipin Hallan, Shabda E Kulsange, Raju Khan, Rekha Chouhan, Narendra Vijay Tripude, Sakshi Vashisht, Rahul Chakraborty, Jatin Kalita, Selvamani Raja Vijayakumar, Jasleen Kaur, Susanta Kar, R.V.K. Singh, Abujunaid Habib Khan, Sundaram Acharya, Shrikant R. Mulay, Dayanidhi Singh, Nitika Kapoor, Pinreddy Karunakar, Sachin N Mahajan, Rohit Yadav, Anjali Srivastava, Prakash M. Halami, Anirban Pal, Anamika Kothari, Bhawana Tomar, Babita Sharma, Anil Ku Maurya, Mahesh J. Kulkarni, Akash Pratap Singh, Arvindkumar H Chaurasiya, Shantanu Sengupta, Saumya Ray Chaudhury, Partha Chakrabarti, Viren Sardana, Anas Saifi, Prasenjit Manna, Aiswarya R Nair, Mahesh S. Dharne, Ravindra P Veeranna, Tridip Phukan, Muthukumar Serva Peddha, Amita P Patil, Birendra Singh Rawat, Gopinath M Sundaram, Mayuri Bhadange, Shweta Verma, Umakanta Subudhi, Vijay Lakshmi Jamwal, Vasudev Wagh, Firdaus Fatima, Ganesh Venkatachalam, Debashish Ghosh, Avinash Mishra, Manikandan Subramanian, Rintu Kutum, Rajesh P. Ringe, Sumit G. Gandhi, Purbasha Bhattacharya, Devendra Singh Parihar, Shilpa Paranjape, Dipyaman Ganguly, Yennapu Madhavi, Shalini Pradhan, Yogendra Padwad, Parimala Karuppanan, Sarita Thakran, Ved Varun Aggarwal, Pawan Gupta, Piyush Singh Jamwal, Pankaj Bharali, Satyartha Prakash, Amit Tuli, Jit Sarkar, Samir Damare, Ajay Agarwal, Rakeshkumar Yadav, Balthu Narender Kumar, Anoop Puthiyamadam, Giriraj R. Chandak, Murugan Veerapandian, Debasis Dash, Ajinkya Khilari, Sumit Dahiya, Mahesh Anumalla, Sharmistha Sarkar, Kalpana Chodankar, Anupama Thakur, Ghanshyam Thakkar, Narahari G Sastry, Iranna Gogeri, Deepak Sharma, Surabhi Seth, Pradip Sen, Harish Kumar Sardana, Vinay Kumar Agarwal, Wahengbam Romi, Rashmi Kumar, Adarsh Velayudhanpillai, Nitin Bhatheja, Ashok Kumar Raman, Praveen Singh, Virendra Kumar, Sunanda Singhmar, Dinesh Gupta, Shaziya Khan, Damanpreet Singh, Madhav Nilakanth Mugale, Rajat Ujjainiya, Karthik Bharadwaj Tallapaka, Elapavalooru V.S.S.K. Babu, Kiran A Kumar, Rakesh Joshi, Rajeev K. Sukumaran, Amarnarayan D, Sujay Krishna Maity, Dhansekaran Shanmugam, Akanksha Sharma, Akash Kumar Bhaskar, Krishna Khairnar, Gaura Chaturvedi, Amit Lahiri, Vikram Patial, Suresh Kumar Anandasadagopan, Prajeesh Kooloth Valappil, Drishti Soni, Rishabh Jain, Sistla Ramakrishna, Dibyajyoti Ozah, Salwa Naushin, Ravi Kumar Sahu, Geethavani Rayasam, Anurag Agrawal, Sayed G Dastager, and Vandana Singh

Subjects
medicine.medical_specialty, business.industry, Environmental health, Cohort, Epidemiology, Medicine, Phenome, business
Published
2021

7. Insights from a Pan India Sero-Epidemiological survey (Phenome-India Cohort) for SARS-CoV2

Author

Rajeev K. Sukumaran, Amarnarayan D, Sujay Krishna Maity, Ravindra P Veeranna, Ganesh Venkatachalam, Dayanidhi Singh, Pinreddy Karunakar, Sundaram Acharya, Pawan Gupta, Narendra Vijay Tirpude, Anoop Puthiyamadam, Giriraj R. Chandak, Nitika Kapoor, Babita Sharma, Mahesh Anumalla, Ajinkya Khilari, Dipyaman Ganguly, Prakash M. Halami, Partha Chakrabarti, Birendra Singh Rawat, Gopinath M Sundaram, Shweta Verma, Rakeshkumar Yadav, Purbasha Bhattacharya, Rintu Kutum, Surabhi Seth, Shilpa Paranjape, Sharmistha Sarkar, Viren Sardana, Sunanda Singhmar, Kalpana Chodankar, Mayuri Bhadange, Ajay Agarwal, Ghanshyam Thakkar, Suresh Kumar Anandasadagopan, Debasis Dash, A. Kiran Kumar, Praveen Singh, Debashish Ghosh, Rekha Chouhan, Rajesh P. Ringe, Drishti Soni, Sumit Dahiya, Rohit Yadav, Deepak Sharma, Jasleen Kaur, Yennapu Madhavi, Dibyajyoti Ozah, Satyartha Prakash, Amit Tuli, Syed G. Dastager, Pradip Sen, Arvindkumar H Chaurasiya, Yogendra Padwad, Vipin Hallan, Jit Sarkar, Akanksha Sharma, Samir Damare, Parimala Karuppanan, Anjali Srivastava, Piyush Singh Jamwal, Pankaj Bharali, Tridip Phukan, Amit Lahiri, Bhawna Tomar, Muthukumar Serva Peddha, Amita P Patil, Selvamani Raja Vijayakumar, Salwa Naushin, Sachin N Mahajan, Mahesh J. Kulkarni, Damanpreet Singh, Anil Ku Maurya, Shabda E Kulsange, Virendra Kumar, Akash Kumar Bhaskar, Ravi Kumar Sahu, Krishna Khairnar, Geethavani Rayasam, Prajeesh Kooloth-Valappil, Rashmi Kumar, Madhav Nilakanth Mugale, Shrikant R. Mulay, Susanta Kar, Balthu Narender Kumar, Umakanta Subudhi, Aiswarya R Nair, Anurag Agrawal, Gaura Chaturvedi, Vikram Patial, Sumit G. Gandhi, Abu Junaid Khan, Adarsh Velayudhanpillai, Anupma Thakur, Anirban Pal, Anamika Kothari, Devendra Singh Parihar, Sarita Thakran, Shantanu Sengupta, Shakshi Vashisht, Nitin Bhatheja, Vasudev Wagh, Firdaus Fatima, Prasenjit Manna, Ashok Kumar Raman, Mahesh S. Dharne, Vijay Lakshmi Jamwal, Vandana Singh, Murugan Veerapandian, G. Narahari Sastry, Manikandan Subramanian, Ran Vijay Kumar Singh, Iranna Gogeri, Harish Kumar Sardana, Mrigank Srivastava, Subramanian Venkatesan, Raju Khan, Dhanasekaran Shanmugam, Rahul Chakraborty, Jatin Kalita, Anas Saifi, Sistla Ramakrishna, Rishabh Jain, Saumya Ray Chaudhury, Shaziya Khan, Rajat Ujjainiya, Karthik Bharadwaj Tallapaka, Elapavalooru V.S.S.K. Babu, Avinash Mishra, Wahengbam Romi, Shalini Pradhan, Ved Varun Aggarwal, Dinesh Gupta, Vinay Kumar Agarwal, Akash Pratap Singh, and Rakesh Joshi

Subjects
Male, Time Factors, 030204 cardiovascular system & hematology, Antibodies, Viral, Serology, 0302 clinical medicine, Risk Factors, Seroepidemiologic Studies, Pandemic, Epidemiology, 030212 general & internal medicine, Longitudinal Studies, Biology (General), General Neuroscience, neutralizing antibody, General Medicine, Universal precautions, Cohort, Host-Pathogen Interactions, Medicine, Female, medicine.medical_specialty, 2019-20 coronavirus outbreak, QH301-705.5, Science, India, Lower risk, Risk Assessment, General Biochemistry, Genetics and Molecular Biology, Odds, COVID-19 Serological Testing, 03 medical and health sciences, Predictive Value of Tests, Environmental health, medicine, Humans, Medical history, General Immunology and Microbiology, business.industry, SARS-CoV-2, antibody stability, Industrial research, COVID-19, Odds ratio, Antibodies, Neutralizing, Confidence interval, Immunity, Humoral, sero-prevalence, SARS-CoV2, business, Biomarkers, Demography
Abstract

Background: India has been amongst the most affected nations during the SARS CoV2 pandemic, with sparse data on country wide spread of asymptomatic infections and antibody persistence. This longitudinal cohort study was aimed to evaluate SARS CoV2 seropositivity rate as a marker of infection and evaluate temporal persistence of antibodies with neutralization capability and to infer possible risk factors for infection. Methods: Council of Scientific and Industrial Research, India (CSIR) with its more than 40 laboratories and centers in urban and semi urban settings spread across the country piloted the pan country surveillance. 10427 adult individuals working in CSIR laboratories and their family members based on voluntary participation were assessed for antibody presence and stability was analyzed over 6 months utilizing qualitative Elecysys SARS CoV2 specific antibody kit and GENScript cPass SARS CoV2 Neutralization Antibody Detection Kit. Along with demographic information, possible risk factors were evaluated through self to be filled online forms with data acquired on blood group type, occupation type, addiction and habits including smoking and alcohol, diet preferences, medical history and transport type utilized. Symptom history and information on possible contact and compliance with COVID 19 universal precautions was also obtained. Findings:1058 individuals (10.14%) had antibodies against SARS CoV2. A follow up on 346 seropositive individuals after three months revealed stable to higher antibody levels against SARS CoV2 but declining plasma activity for neutralizing SARS CoV2 receptor binding domain and ACE2 interaction. A repeat sampling of 35 individuals, at six months, revealed declining antibody levels while the neutralizing activity remained stable compared to three months. Majority of seropositive individuals (75%) did not recall even one of nine symptoms since March 2020. Fever was the most common symptom with one fourth reporting loss of taste or smell. Significantly associated risks for seropositivity (Odds Ratio, 95% CI, p value) were observed with usage of public transport (1.79, 1.43 to 2∙24, 2.81561x10-6), occupational responsibilities such as security, housekeeping personnel etc. (2.23, 1.92 to 2.59, 6.43969x10-26), non smokers (1.52, 1.16 to 1.99, 0.02) and non vegetarianism (1.67, 1.41 to 1.99, 3.03821x10-8). An iterative regression analysis was confirmatory and led to only modest changes to estimates. Predilections for seropositivity was noted with specific ABO blood groups; O was associated with a lower risk. Interpretation: In a first of its kind study from India, we report the seropositivity in a country wide cohort and identify variable susceptible associations for contacting infection. Serology and Neutralizing Antibody response provides much sought for general insights on the immune response to the virus among Indians and will be an important resource for designing vaccination strategies. Funding: Council of Scientific and Industrial Research, India (CSIR)

Published
2021

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