Emergence of Novel SARS-CoV-2 variants in India: second wave

Adiga, Rama; Nayak, Varun R

doi:10.3855/jidc.15484

Cited by 8 publications

(8 citation statements)

References 12 publications

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“…The authors analyzed viral genomic sequences retrospectively and observed a similar pattern in the rise of the B.1.617 lineage, mainly the B.1.617.2 variant, in Delhi before the second wave [ 10 ]. A B.1.617.2-driven second wave was also reflected in the analysis of viral genomic sequences performed by Adiga and Nayak in 2021 [ 43 ]. We recently used our prediction model prospectively during the initial rise of cases caused by the Omicron strain in South Africa, which indicated an upcoming wave with very high transmissibility but limited lethality [ 4 ].…”

Section: Discussionmentioning

confidence: 95%

A Bioinformatics Tool for Predicting Future COVID-19 Waves Based on a Retrospective Analysis of the Second Wave in India: Model Development Study

Kumar¹,

Asghar²,

Dwivedi³

et al. 2022

JMIR Bioinform Biotech

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Background Since the start of the COVID-19 pandemic, health policymakers globally have been attempting to predict an impending wave of COVID-19. India experienced a devastating second wave of COVID-19 in the late first week of May 2021. We retrospectively analyzed the viral genomic sequences and epidemiological data reflecting the emergence and spread of the second wave of COVID-19 in India to construct a prediction model. Objective We aimed to develop a bioinformatics tool that can predict an impending COVID-19 wave. Methods We analyzed the time series distribution of genomic sequence data for SARS-CoV-2 and correlated it with epidemiological data for new cases and deaths for the corresponding period of the second wave. In addition, we analyzed the phylodynamics of circulating SARS-CoV-2 variants in the Indian population during the study period. Results Our prediction analysis showed that the first signs of the arrival of the second wave could be seen by the end of January 2021, about 2 months before its peak in May 2021. By the end of March 2021, it was distinct. B.1.617 lineage variants powered the wave, most notably B.1.617.2 (Delta variant). Conclusions Based on the observations of this study, we propose that genomic surveillance of SARS-CoV-2 variants, complemented with epidemiological data, can be a promising tool to predict impending COVID-19 waves.

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Section: Discussionmentioning

confidence: 95%

A Bioinformatics Tool for Predicting Future COVID-19 Waves Based on a Retrospective Analysis of the Second Wave in India: Model Development Study

Kumar¹,

Asghar²,

Dwivedi³

et al. 2022

JMIR Bioinform Biotech

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“…This may be explicable because position 478 is within the receptor binding domain (RBD), and it had a high level of mutations in summer of 2020 [ 69 ] continuing through later in 2021 when the T478K mutation emerged as a characteristic mutation of the Delta lineage [ 66 ]. Similarly E484Q has been a characteristic mutation in the Delta lineage, but E484K has been an aspect of other pre-Delta, such as Beta and some Alpha sublineages [ 70 , 71 , 72 ]. Similarly, P681H was found in the highly prevalent Alpha lineage [ 73 ], and P681R was found in the Kappa and then later Delta lineage [ 74 ].…”

Section: Resultsmentioning

confidence: 99%

“…By contrast, another site with frequent mutations, site 484, where E484K, E484Q, and E484A mutations associated with antibody escape have been found [ 71 ], does not have high attention. The low attention at position 484 in the disease severity model may suggest that the site does not have a significant role in determining virulence rather than antibody evasion [ 26 , 70 , 72 , 73 , 74 ].…”

Section: Resultsmentioning

confidence: 99%

Interpretable and Predictive Deep Neural Network Modeling of the SARS-CoV-2 Spike Protein Sequence to Predict COVID-19 Disease Severity

Sokhansanj

Zhao

Rosen

2022

Biology

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Through the COVID-19 pandemic, SARS-CoV-2 has gained and lost multiple mutations in novel or unexpected combinations. Predicting how complex mutations affect COVID-19 disease severity is critical in planning public health responses as the virus continues to evolve. This paper presents a novel computational framework to complement conventional lineage classification and applies it to predict the severe disease potential of viral genetic variation. The transformer-based neural network model architecture has additional layers that provide sample embeddings and sequence-wide attention for interpretation and visualization. First, training a model to predict SARS-CoV-2 taxonomy validates the architecture’s interpretability. Second, an interpretable predictive model of disease severity is trained on spike protein sequence and patient metadata from GISAID. Confounding effects of changing patient demographics, increasing vaccination rates, and improving treatment over time are addressed by including demographics and case date as independent input to the neural network model. The resulting model can be interpreted to identify potentially significant virus mutations and proves to be a robust predctive tool. Although trained on sequence data obtained entirely before the availability of empirical data for Omicron, the model can predict the Omicron’s reduced risk of severe disease, in accord with epidemiological and experimental data.

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“…[122] Similarly E484Q has been a characteristic mutation in the Delta lineage, but E484K has been an aspect of other pre-Delta, such as Beta and some Alpha sublineages. [126]–[128] Similarly, P681H was found in the highly prevalent Alpha lineage [129], and P681R was found in the Kappa and then later Delta lineage. [130] Accordingly, it may be the case that a consistently high attention suggests that the model pays attention to the site because it was not a point of variation at early time points.…”

Section: Resultsmentioning

confidence: 99%

Interpretable and Predictive Deep Modeling of the SARS-CoV-2 Spike Protein Sequence

Sokhansanj

Zhao

Rosen

2021

Preprint

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As the COVID-19 pandemic continues, the SARS-CoV-2 virus continues to rapidly mutate and change in ways that impact virulence, transmissibility, and immune evasion. Genome sequencing is a critical tool, as other biological techniques can be more costly, time-consuming, and difficult. However, the rapid and complex evolution of SARS-CoV-2 challenges conventional sequence analysis methods like phylogenetic analysis. The virus picks up and loses mutations independently in multiple subclades, often in novel or unexpected combinations, and, as for the newly emerged Omicron variant, sometimes with long explained branches. We propose interpretable deep sequence models trained by machine learning to complement conventional methods. We apply Transformer-based neural network models developed for natural language processing to analyze protein sequences. We add network layers to generate sample embeddings and sequence-wide attention to interpret models and visualize multiscale patterns. We demonstrate and validate our framework by modeling SARS-CoV-2 and coronavirus taxonomy. We then develop an interpretable predictive model of disease severity that integrates SARS-CoV-2 spike protein sequence and patient demographic variables, using publicly available data from the GISAID database. We also apply our model to Omicron. Based on knowledge prior to the availability of empirical data for Omicron, we predict: 1) reduced neutralization antibody activity (15-50 fold) greater than any previously characterized variant, varying between Omicron sublineages, and 2) reduced risk of severe disease (by 35-40%) relative to Delta. Both predictions are in accord with recent epidemiological and experimental data.

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Emergence of Novel SARS-CoV-2 variants in India: second wave

Cited by 8 publications

References 12 publications

A Bioinformatics Tool for Predicting Future COVID-19 Waves Based on a Retrospective Analysis of the Second Wave in India: Model Development Study

A Bioinformatics Tool for Predicting Future COVID-19 Waves Based on a Retrospective Analysis of the Second Wave in India: Model Development Study

Interpretable and Predictive Deep Neural Network Modeling of the SARS-CoV-2 Spike Protein Sequence to Predict COVID-19 Disease Severity

Interpretable and Predictive Deep Modeling of the SARS-CoV-2 Spike Protein Sequence

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