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1.
Comput Biol Med ; 146: 105419, 2022 07.
Article in English | MEDLINE | ID: covidwho-1803804

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.


Subject(s)
COVID-19 , Viral Vaccines , COVID-19/epidemiology , COVID-19/prevention & control , COVID-19 Vaccines/therapeutic use , Humans , Machine Learning , Pandemics , SARS-CoV-2 , Vaccines, Inactivated , Virion
2.
Nat Commun ; 13(1): 1726, 2022 04 01.
Article in English | MEDLINE | ID: covidwho-1773977

ABSTRACT

Immunization is expected to confer protection against infection and severe disease for vaccines while reducing risks to unimmunized populations by inhibiting transmission. Here, based on serial serological studies of an observational cohort of healthcare workers, we show that during a Severe Acute Respiratory Syndrome -Coronavirus 2 Delta-variant outbreak in Delhi, 25.3% (95% Confidence Interval 16.9-35.2) of previously uninfected, ChAdOx1-nCoV19 double vaccinated, healthcare workers were infected within less than two months, based on serology. Induction of anti-spike response was similar between groups with breakthrough infection (541 U/ml, Inter Quartile Range 374) and without (342 U/ml, Inter Quartile Range 497), as was the induction of neutralization activity to wildtype. This was not vaccine failure since vaccine effectiveness estimate based on infection rates in an unvaccinated cohort were about 70% and most infections were asymptomatic. We find that while ChAdOx1-nCoV19 vaccination remains effective in preventing severe infections, it is unlikely to be completely able to block transmission and provide herd immunity.


Subject(s)
Asymptomatic Infections , COVID-19 , COVID-19/epidemiology , COVID-19/prevention & control , Health Personnel , Humans , Immunization , SARS-CoV-2 , Vaccination
3.
2021.
Preprint in English | Other preprints | ID: ppcovidwho-296195

ABSTRACT

Immunization is expected to confer protection against infection and severe disease for vaccinees, while reducing risks to unimmunized populations by inhibiting transmission. Here, based on serial serological studies, we show that during a severe SARS-CoV2 Delta-variant outbreak in Delhi, 25.3% (95% CI 16.9 - 35.2) of previously uninfected, ChAdOx1-nCoV19 double vaccinated, healthcare-workers (HCW) were infected within a period of less than two months, based on serology. Induction of anti-spike response was similar between groups with breakthrough infection (541 U/ml, IQR 374) or not (342 U/ml, IQR 497), as was induction of neutralization activity to wildtype. Most infections were unrecognized. The Delta-variant thus causes frequent unrecognized breakthrough infections in adequately immunized subjects, reducing any herd-effect of immunity, and requiring reinstatement of preventive measures such as masking.

4.
Sci Rep ; 11(1): 23210, 2021 12 01.
Article in English | MEDLINE | ID: covidwho-1545637

ABSTRACT

SARS-CoV2 pandemic exposed the limitations of artificial intelligence based medical imaging systems. Earlier in the pandemic, the absence of sufficient training data prevented effective deep learning (DL) solutions for the diagnosis of COVID-19 based on X-Ray data. Here, addressing the lacunae in existing literature and algorithms with the paucity of initial training data; we describe CovBaseAI, an explainable tool using an ensemble of three DL models and an expert decision system (EDS) for COVID-Pneumonia diagnosis, trained entirely on pre-COVID-19 datasets. The performance and explainability of CovBaseAI was primarily validated on two independent datasets. Firstly, 1401 randomly selected CxR from an Indian quarantine center to assess effectiveness in excluding radiological COVID-Pneumonia requiring higher care. Second, curated dataset; 434 RT-PCR positive cases and 471 non-COVID/Normal historical scans, to assess performance in advanced medical settings. CovBaseAI had an accuracy of 87% with a negative predictive value of 98% in the quarantine-center data. However, sensitivity was 0.66-0.90 taking RT-PCR/radiologist opinion as ground truth. This work provides new insights on the usage of EDS with DL methods and the ability of algorithms to confidently predict COVID-Pneumonia while reinforcing the established learning; that benchmarking based on RT-PCR may not serve as reliable ground truth in radiological diagnosis. Such tools can pave the path for multi-modal high throughput detection of COVID-Pneumonia in screening and referral.


Subject(s)
COVID-19/complications , Deep Learning , Expert Systems , Image Processing, Computer-Assisted/methods , Pneumonia/diagnosis , Radiography, Thoracic/methods , Tomography, X-Ray Computed/methods , Algorithms , COVID-19/virology , Humans , Incidence , India/epidemiology , Neural Networks, Computer , Pneumonia/diagnostic imaging , Pneumonia/epidemiology , Pneumonia/virology , Retrospective Studies , SARS-CoV-2/isolation & purification
5.
Trans R Soc Trop Med Hyg ; 2021 Aug 13.
Article in English | MEDLINE | ID: covidwho-1356713

ABSTRACT

BACKGROUND: Most of the reported risk score models for coronavirus disease 2019 (COVID-19) mortality are based on the levels of inflammatory markers, comorbidities or various treatment modalities, and there is a paucity of risk score models based on clinical symptoms and comorbidities. METHODS: To address this need, age, clinical symptoms and comorbidities were used to develop a COVID-19 scoring system (CSS) for early prediction of mortality in severe COVID-19 patients. The CSS was developed with scores ranging from 0 to 9. A higher score indicates higher risk with good discrimination quality presented by Mann Whitney U test and area under receiver operating characteristic curve (AUROC). RESULTS: Patient age of ≥60 y, cough, breathlessness, diabetes and any other comorbidity (with or without diabetes) are significant and independent risk factors for non-survival among COVID-19 patients. The CSS showed good sensitivity and specificity (i.e. 74.1% and 78.5% at CSS≥5, respectively), with an overall diagnostic accuracy of 82.8%, which was close to the diagnostic accuracy detected in the validation cohort (81.9%). In the validation cohort, high (8-9), medium (5-7) and low (0-4) CSS groups had 54.80%, 28.60% and 6.5% observed mortality, respectively, which was very close to the predicted mortality (62.40%, 27.60% and 5.2%, respectively, by scoring cohort). CONCLUSIONS: The CSS shows a positive relationship between a higher score and proportion of mortality and, as its validation showed, it is useful for the prediction of risk of mortality in COVID-19 patients at an early stage, so that referral for triage and admission can be predetermined even before admission to hospital.

6.
Wellcome Open Res ; 5: 184, 2020.
Article in English | MEDLINE | ID: covidwho-808195

ABSTRACT

Background: India first detected SARS-CoV-2, causal agent of COVID-19 in late January 2020, imported from Wuhan, China. From March 2020 onwards, the importation of cases from countries in the rest of the world followed by seeding of local transmission triggered further outbreaks in India. Methods: We used ARTIC protocol-based tiling amplicon sequencing of SARS-CoV-2 (n=104) from different states of India using a combination of MinION and MinIT sequencing from Oxford Nanopore Technology to understand how introduction and local transmission occurred. Results: The analyses revealed multiple introductions of SARS-CoV-2 genomes, including the A2a cluster from Europe and the USA, A3 cluster from Middle East and A4 cluster (haplotype redefined) from Southeast Asia (Indonesia, Thailand and Malaysia) and Central Asia (Kyrgyzstan). The local transmission and persistence of genomes A4, A2a and A3 was also observed in the studied locations. The most prevalent genomes with patterns of variance (confined in a cluster) remain unclassified, and are here proposed as A4-clade based on its divergence within the A cluster. Conclusions: The viral haplotypes may link their persistence to geo-climatic conditions and host response. Multipronged strategies including molecular surveillance based on real-time viral genomic data is of paramount importance for a timely management of the pandemic.

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