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

Kumar, Ashutosh; Asghar, Adil; Dwivedi, Prakhar; Kumar, Gopichand; Narayan, Ravi Kant; Jha, Rakesh Kumar; Parashar, Rakesh; Sahni, Chetan; Pandey, Sada N.

doi:10.2196/36860

Cited by 6 publications

(4 citation statements)

References 41 publications

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“…The steep rise in the new COVID-19 cases in South Africa seems to be driven by Omicron, whereas Delta variant–linked cases are seeing a decline ( Figure 4 ). The rapid rise in new COVID-19 cases connected with the emergence of a new SARS-CoV-2 variant strongly indicated the commencement of a new COVID-19 wave in South Africa [ 14 ].…”

Section: Discussionmentioning

confidence: 99%

“…Therefore, we aimed to resolve the existing ambiguity over the epidemiological properties of the Omicron variant using an integrated approach combining viral genomic sequence analysis and epidemiological data. Integrating viral genomic analysis with epidemiological data is a relatively novel approach; however, its success in predicting the epidemiological properties of SARS-CoV-2 variants and the future course of the COVID-19 pandemic has been validated in recent bioinformatic studies [ 14 , 15 ]. The findings of this study will thus provide concrete insights into the origin and epidemiological attributes of this variant to pave the way for the end of the pandemic.…”

Section: Introductionmentioning

confidence: 99%

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SARS-CoV-2 Omicron Variant Genomic Sequences and Their Epidemiological Correlates Regarding the End of the Pandemic: In Silico Analysis

Kumar¹,

Asghar²,

Singh³

et al. 2023

JMIR Bioinform Biotech

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Background Emergence of the new SARS-CoV-2 variant B.1.1.529 worried health policy makers worldwide due to a large number of mutations in its genomic sequence, especially in the spike protein region. The World Health Organization (WHO) designated this variant as a global variant of concern (VOC), which was named “Omicron.” Following Omicron’s emergence, a surge of new COVID-19 cases was reported globally, primarily in South Africa. Objective The aim of this study was to understand whether Omicron had an epidemiological advantage over existing variants. Methods We performed an in silico analysis of the complete genomic sequences of Omicron available on the Global Initiative on Sharing Avian Influenza Data (GISAID) database to analyze the functional impact of the mutations present in this variant on virus-host interactions in terms of viral transmissibility, virulence/lethality, and immune escape. In addition, we performed a correlation analysis of the relative proportion of the genomic sequences of specific SARS-CoV-2 variants (in the period from October 1 to November 29, 2021) with matched epidemiological data (new COVID-19 cases and deaths) from South Africa. Results Compared with the current list of global VOCs/variants of interest (VOIs), as per the WHO, Omicron bears more sequence variation, specifically in the spike protein and host receptor-binding motif (RBM). Omicron showed the closest nucleotide and protein sequence homology with the Alpha variant for the complete sequence and the RBM. The mutations were found to be primarily condensed in the spike region (n=28-48) of the virus. Further mutational analysis showed enrichment for the mutations decreasing binding affinity to angiotensin-converting enzyme 2 receptor and receptor-binding domain protein expression, and for increasing the propensity of immune escape. An inverse correlation of Omicron with the Delta variant was noted (r=–0.99, P<.001; 95% CI –0.99 to –0.97) in the sequences reported from South Africa postemergence of the new variant, subsequently showing a decrease. There was a steep rise in new COVID-19 cases in parallel with the increase in the proportion of Omicron isolates since the report of the first case (74%-100%). By contrast, the incidence of new deaths did not increase (r=–0.04, P>.05; 95% CI –0.52 to 0.58). Conclusions In silico analysis of viral genomic sequences suggests that the Omicron variant has more remarkable immune-escape ability than existing VOCs/VOIs, including Delta, but reduced virulence/lethality than other reported variants. The higher power for immune escape for Omicron was a likely reason for the resurgence in COVID-19 cases and its rapid rise as the globally dominant strain. Being more infectious but less lethal than the existing variants, Omicron could have plausibly led to widespread unnoticed new, repeated, and vaccine breakthrough infections, raising the population-level immunity barrier against the emergence of new lethal variants. The Omicron variant could have thus paved the way for the end of the pandemic.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

SARS-CoV-2 Omicron Variant Genomic Sequences and Their Epidemiological Correlates Regarding the End of the Pandemic: In Silico Analysis

Kumar¹,

Asghar²,

Singh³

et al. 2023

JMIR Bioinform Biotech

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“…The mutation fitness of SARS-CoV-2 genomic sequences sampled in Ontario was observed to increase in spurts over short time periods, likely coinciding with the introduction of novel SARS-CoV-2 variants with acquired genomic mutations that confer a fitness advantage [ 70 ]. Thus, future research predicting the functional impact of different sets of mutations on fitness could improve the surveillance of emergent SARS-CoV-2 variants for public health and inform the design of specific antiviral therapies [ 71 ].…”

Section: Discussionmentioning

confidence: 99%

Mutational Patterns Observed in SARS-CoV-2 Genomes Sampled From Successive Epochs Delimited by Major Public Health Events in Ontario, Canada: Genomic Surveillance Study

Chen¹,

Randhawa²,

Soltysiak³

et al. 2022

JMIR Bioinform Biotech

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Background The emergence of SARS-CoV-2 variants with mutations associated with increased transmissibility and virulence is a public health concern in Ontario, Canada. Characterizing how the mutational patterns of the SARS-CoV-2 genome have changed over time can shed light on the driving factors, including selection for increased fitness and host immune response, that may contribute to the emergence of novel variants. Moreover, the study of SARS-CoV-2 in the microcosm of Ontario, Canada can reveal how different province-specific public health policies over time may be associated with observed mutational patterns as a model system. Objective This study aimed to perform a comprehensive analysis of single base substitution (SBS) types, counts, and genomic locations observed in SARS-CoV-2 genomic sequences sampled in Ontario, Canada. Comparisons of mutational patterns were conducted between sequences sampled during 4 different epochs delimited by major public health events to track the evolution of the SARS-CoV-2 mutational landscape over 2 years. Methods In total, 24,244 SARS-CoV-2 genomic sequences and associated metadata sampled in Ontario, Canada from January 1, 2020, to December 31, 2021, were retrieved from the Global Initiative on Sharing All Influenza Data database. Sequences were assigned to 4 epochs delimited by major public health events based on the sampling date. SBSs from each SARS-CoV-2 sequence were identified relative to the MN996528.1 reference genome. Catalogues of SBS types and counts were generated to estimate the impact of selection in each open reading frame, and identify mutation clusters. The estimation of mutational fitness over time was performed using the Augur pipeline. Results The biases in SBS types and proportions observed support previous reports of host antiviral defense activity involving the SARS-CoV-2 genome. There was an increase in U>C substitutions associated with adenosine deaminase acting on RNA (ADAR) activity uniquely observed during Epoch 4. The burden of novel SBSs observed in SARS-CoV-2 genomic sequences was the greatest in Epoch 2 (median 5), followed by Epoch 3 (median 4). Clusters of SBSs were observed in the spike protein open reading frame, ORF1a, and ORF3a. The high proportion of nonsynonymous SBSs and increasing dN/dS metric (ratio of nonsynonymous to synonymous mutations in a given open reading frame) to above 1 in Epoch 4 indicate positive selection of the spike protein open reading frame. Conclusions Quantitative analysis of the mutational patterns of the SARS-CoV-2 genome in the microcosm of Ontario, Canada within early consecutive epochs of the pandemic tracked the mutational dynamics in the context of public health events that instigate significant shifts in selection and mutagenesis. Continued genomic surveillance of emergent variants will be useful for the design of public health policies in response to the evolving COVID-19 pandemic.

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“…Coronavirus disease 2019 (COVID- 19) is an infectious disease caused by the SARS-CoV-2 virus, a positivesense, single-stranded RNA virus. It can cause various symptoms ranging from common colds to severe acute respiratory syndrome (SARS) [1]. Severe acute respiratory syndrome (SARS) and Middle East respiratory syndrome (MERS) are also caused by coronaviruses and have been associated with neuropsychiatric manifestations such as delirium, depression, anxiety, and insomnia [2].…”

Section: Introductionmentioning

confidence: 99%

Psychiatric Symptoms and Fatigue in COVID-19 Survivors

Naphade,

Singh,

Rao

et al. 2023

Cureus

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Introduction: Psychiatric symptoms and fatigue are common after the coronavirus disease 2019 (COVID-19) illness. The cause of these symptoms is direct neuronal injury and indirect injury with immune-mediated inflammation. In addition, social factors also affect mental health. Objective: We aim to compare psychiatric symptoms and fatigue between COVID-19 survivors and healthy controls. Material and methods: We prospectively evaluated 100 COVID-19 survivors for anxiety, depression, positive affect, and behavior control using the Mental Health Inventory (MHI). Fatigue is assessed using the Modified Fatigue Impact Scale (MFIS) score. We compared them with 100 healthy controls. Results: There was a significant statistical difference between the MHI score and individual components of MHI. Overall, MHI scores in cases and controls were 79.41 and 93.31, respectively, with a P value of less than 0.0001. Computed scores for anxiety, depression, behavior control, and positive affect of COVID-19 survivors showed statistically significant differences as compared to healthy controls. There was a weak association between hospital stay duration and poor MHI scores. Fatigue was significantly worse in COVID-19 survivors, with a mean score of 6.93 in cases and 5.35 in controls, with a P value of 0.0001. This was a cross-sectional study evaluating psychiatric symptom scores, but not establishing the diagnosis. It is suggested that appropriate treatment and counseling for these symptoms should be done. Conclusions: Psychiatric symptoms and fatigue were significantly more common in COVID-19 patients after recovery from acute illness. It is a major contributing cause of morbidity other than organic complications of COVID-19 and requires attention in management.

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

Cited by 6 publications

References 41 publications

SARS-CoV-2 Omicron Variant Genomic Sequences and Their Epidemiological Correlates Regarding the End of the Pandemic: In Silico Analysis

SARS-CoV-2 Omicron Variant Genomic Sequences and Their Epidemiological Correlates Regarding the End of the Pandemic: In Silico Analysis

Mutational Patterns Observed in SARS-CoV-2 Genomes Sampled From Successive Epochs Delimited by Major Public Health Events in Ontario, Canada: Genomic Surveillance Study

Psychiatric Symptoms and Fatigue in COVID-19 Survivors

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