Your browser doesn't support javascript.
loading
Mostrar: 20 | 50 | 100
Resultados 1 - 5 de 5
Filtrar
Mais filtros










Intervalo de ano de publicação
1.
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; 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; Kiran Kumar A; Siva Ranjith; Vijay B 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; Saroj 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; Prakash L; Ch V Rao; B N Singh; Arvindkumar Chaurasiya; Meera Chaurasiyar; Mayuri Bhadange; Bhagyashree Likhitkar; Sharada Mohite; Yogita Patil; Mahesh Kulkarni; Rakesh Joshi; Vaibhav Pandya; 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; PrajeeshKooloth Valappil; Adrash Velayudhan Pillai Prasannakumari; Kalpana Chodankar; Samir Damare; Ved Varun Agrawal; Kumardeep Chaudhary; Anurag Agrawal; Shantanu Sengupta; Debasis Dash.
Preprint em Inglês | medRxiv | ID: ppmedrxiv-21267889

RESUMO

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.

2.
Preprint em Inglês | bioRxiv | ID: ppbiorxiv-436930

RESUMO

Emergence of distinct viral clades has been observed in SARS-CoV2 variants across the world and India. Identification of the genomic diversity and the phylodynamic profiles of the prevalent strains of the country are critical to understand the evolution and spread of the variants. We performed whole-genome sequencing of 54 SARS-CoV2 strains collected from COVID-19 patients in Kolkata, West Bengal during August to October 2020. Phylogeographic and phylodynamic analyses were performed using these 54 and other sequences from India and abroad available in GISAID database. Spatio-temporal evolutionary dynamics of the pathogen across various regions and states of India over three different time periods in the year 2020 were analyzed. We estimated the clade dynamics of the Indian strains and compared the clade specific mutations and the co-mutation patterns across states and union territories of India over the time course. We observed that GR, GH and G (GISAID) or 20B and 20A (Nextstrain) clades were the prevalent clades in India during middle and later half of the year 2020. However, frequent mutations and co-mutations observed within the major clades across time periods do not show much overlap, indicating emergence of newer mutations in the viral population prevailing in the country. Further, we explored the possible association of specific mutations and co-mutations with the infection outcomes manifested within the Indian patients.

3.
Salwa Naushin; Viren Sardana; Rajat Ujjainiya; Nitin Bhatheja; Rintu Kutum; Akash Kumar Bhaskar; Shalini Pradhan; Satyartha Prakash; Raju Khan; Birendra Singh Rawat; Giriraj Ratan Chandak; Karthik Bharadwaj Tallapaka; Mahesh Anumalla; Amit Lahiri; Susanta Kar; Shrikant Ramesh Mulay; Madhav Nilakanth Mugale; Mrigank Srivastava; Shaziya Khan; Anjali Srivastava; Bhawna Tomar; Murugan Veerapandian; Ganesh Venkatachalam; Selvamani Raja Vijayakumar; Ajay Agarwal; Dinesh Gupta; Prakash M Halami; Muthukumar Serva Peddha; Gopinath M; Ravindra P Veeranna; Anirban Pal; Vinay Kumar Agarwal; Anil Ku Maurya; Ranvijay Kumar Singh; Ashok Kumar Raman; Suresh Kumar Anandasadagopan; Parimala Karupannan; Subramanian Venkatesan; Harish Kumar Sardana; Anamika Kothari; Rishabh Jain; Anupma Thakur; Devendra Singh Parihar; Anas Saifi; Jasleen Kaur; Virendra Kumar; Avinash Mishra; Iranna Gogeri; Geetha Vani 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; Firdaus Fatima; Shakshi Vashisht; Sarita Thakran; Akash Pratap Singh; Akanksha Sharma; Babita Sharma; Manikandan Subramanian; Yogendra Padwad; Vipin Hallan; Vikram Patial; Damanpreet Singh; Narendra Vijay Tirpude; 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 Raychaudhri; Amit Tuli; Pawan Gupta; Rashmi Kumar; Deepak Sharma; Rajesh P. Ringe; Amarnarayan D; Mahesh Kulkarni; Dhanasekaran Shanmugam; Mahesh Dharne; Syed G Dastager; Rakesh Joshi; Amita P. Patil; Sachin N Mahajan; Abu Junaid Khan; Vasudev Wagh; Rakeshkumar Yadav; Ajinkya Khilari; Mayuri Bhadange; Arvindkumar H. Chaurasiya; Shabda E Kulsange; Krishna khairnar; Shilpa Paranjape; Jatin Kalita; G.Narahari Sastry; Tridip Phukan; Prasenjit Manna; Wahengbam Romi; Pankaj Bharali; Dibyajyoti Ozah; Ravi Kumar Sahu; Elapaval VSSK Babu; Rajeev K Sukumaran; Aishwarya R Nair; Anoop Puthiyamadam; Prajeesh Kooloth Valappil; Adarsh Velayudhanpillai; Kalpana Chodankar; Samir Damare; Yennapu Madhavi; Ved Varun Agrawal; Sumit Dahiya; Anurag Agrawal; Debasis Dash; Shantanu Sengupta.
Preprint em Inglês | medRxiv | ID: ppmedrxiv-21249713

RESUMO

To understand the spread of SARS-CoV2, in August and September 2020, the Council of Scientific and Industrial Research (India), conducted a sero-survey 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% with surrogate neutralization activity. Three-fourth recalled no symptoms. Repeat serology tests at 3 (n=346) and 6 (n=35) months confirmed stability of antibody response and neutralization potential. Local sero-positivity was higher in densely populated cities and was inversely correlated with a 30 day change in regional test positivity rates (TPR). Regional seropositivity above 10% was associated with declining TPR. Personal factors associated with higher odds of sero-positivity were high-exposure work (Odds Ratio, 95% CI, p value; 2{middle dot}23, 1{middle dot}92-2{middle dot}59, 6{middle dot}5E-26), use of public transport (1{middle dot}79, 1{middle dot}43-2{middle dot}24, 2{middle dot}8E-06), not smoking (1{middle dot}52, 1{middle dot}16-1{middle dot}99, 0{middle dot}02), non-vegetarian diet (1{middle dot}67, 1{middle dot}41-1{middle dot}99, 3{middle dot}0E-08), and B blood group (1{middle dot}36,1{middle dot}15-1{middle dot}61, 0{middle dot}001). Impact StatementWidespread asymptomatic and undetected SARS-CoV2 infection affected more than a 100 million Indians by September 2020. Declining new cases thereafter may be due to persisting humoral immunity amongst sub-communities with high exposure. FundingCouncil of Scientific and Industrial Research, India (CSIR)

4.
Preprint em Inglês | medRxiv | ID: ppmedrxiv-20054775

RESUMO

ObjectiveIn absence of any vaccine, the Corona Virus Disease 2019 (COVID-19) pandemic is being contained through a non-pharmaceutical measure termed Social Distancing (SD). However, whether SD alone is enough to flatten the epidemic curve is debatable. Using a Stochastic Computational Simulation Model, we investigated the impact of increasing SD, hospital beds and COVID-19 detection rates in preventing COVID-19 cases and fatalities. Research Design and MethodsThe Stochastic Simulation Model was built using the EpiModel package in R. As a proof of concept study, we ran the simulation on Kasaragod, the most affected district in Kerala. We added 3 compartments to the SEIR model to obtain a SEIQHRF (Susceptible-Exposed-Infectious-Quarantined-Hospitalised-Recovered-Fatal) model. ResultsImplementing SD only delayed the appearance of peak prevalence of COVID-19 cases. Doubling of hospital beds couldnt reduce the fatal cases probably due to its overwhelming number compared to the hospital beds. Increasing detection rates could significantly flatten the curve and reduce the peak prevalence of cases (increasing detection rate by 5 times could reduce case number to half). ConclusionsAn effective strategy to contain the epidemic spread of COVID-19 in India is to increase detection rates in combination with SD measures and increase in hospital beds. HIGHLIGHTSO_LIIncreased Detection of COVID-19 cases must accompany Social Distancing and Health Capacity Planning to reduce the burden of cases and fatalities. C_LIO_LIInterruptive Social Distancing is an effective alternative to continuous Social Distancing. C_LIO_LIGiven the overwhelming burden of COVID-19 fatalities, there is immediate need of co-ordination with the Private Healthcare Sector. C_LIO_LICOVID-19 cases will be peaking after May, 2020 giving us time for Healthcare Capacity Building in the government and private sector both. C_LI

5.
Preprint em Inglês | medRxiv | ID: ppmedrxiv-20043331

RESUMO

ObjectiveThe recent pandemic of novel coronavirus disease 2019 (COVID-19) is increasingly causing severe acute respiratory syndrome (SARS) and significant mortality. We aim here to identify the risk factors associated with mortality of coronavirus infected persons using a supervised machine learning approach. Research Design and MethodsClinical data of 1085 cases of COVID-19 from 13th January to 28th February, 2020 was obtained from Kaggle, an online community of Data scientists. 430 cases were selected for the final analysis. Random Forest classification algorithm was implemented on the dataset to identify the important predictors and their effects on mortality. ResultsThe Area under the ROC curve obtained during model validation on the test dataset was 0.97. Age was the most important variable in predicting mortality followed by the time gap between symptom onset and hospitalization. ConclusionsPatients aged beyond 62 years are at higher risk of fatality whereas hospitalization within 2 days of the onset of symptoms could reduce mortality in COVID-19 patients.

SELEÇÃO DE REFERÊNCIAS
DETALHE DA PESQUISA
...