Competition of SARS-CoV-2 variants on the pandemic transmission dynamics

Chen, Jiaqi; Gu, Changgui; Ruan, Zhongyuan; Tang, Ming

doi:10.1016/j.chaos.2023.113193

Cited by 14 publications

(8 citation statements)

References 60 publications

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“…SARS-CoV-2 lineages with D614G mutation, replaced the index SARS-CoV-2 lineage globally by July 2020. Later, the B.1.351 lineage (Beta variant) became dominant to be soon replaced by the B.1.617.2 lineage (Delta variant), which was in turn replaced by a more transmissible and immune evasive lineage, the B.1.1.529 lineage (Omicron variant) [2]. This global trend was also seen in India, where three major COVID-19 waves were caused by the index SARS-CoV-2 lineage, the Delta variant, and the Omicron variant [3].…”

Section: Article 1 Introductionmentioning

confidence: 99%

“…Thus, lineages with a competitive advantage dominated and influenced the transmission dynamics of the SARS-CoV-2 pandemic [2]. Now, during the current period of Omicron variant domination, with varying range of host immunity following earlier infections or vaccinations, an Omicron lineage soup has emerged with 923 nonrecombinant Omicron lineages, with periodic surges by different Omicron lineages predominating in distinct geographical locations {the entire list #115 Designated SARS-CoV-2 Non-Recombinant (Sub-)Lineages, Lineage only; Maintained by Andrew Urquhart is available at https://cov-spectrum.org/collections/115 [covSPECTRUM (cov-spectrum.org)]} [4].…”

Section: Article 1 Introductionmentioning

confidence: 99%

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Chasing SARS-CoV-2 XBB.1.16 Recombinant Lineage in India and the Clinical Profile of XBB.1.16 cases in Maharashtra, India

Karyakarte

Das

Rajmane

et al. 2023

Preprint

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Background: SARS-CoV-2 has evolved rapidly, resulting in emergence of lineages with competitive advantage over one another. Co-infections with different SARS-CoV-2 lineages can give rise to recombinant lineages. To date, XBB lineage is the most widespread recombinant lineage worldwide, with the recently named XBB.1.16 lineage causing a surge in the number of COVID-19 cases in India. Methodology: The present study involved retrieval of SARS-CoV-2 genome sequences from India (between 1st December 2022 and 8th April 2023) through GISAID; sequences were curated, followed by lineage and phylogenetic analysis. Demographic and clinical data from Maharashtra, India were collected telephonically, recorded in Microsoft ® Excel, and analysed using IBM ® SPSS statistics, version 29.0.0.0 (241). Results: A total of 2,944 sequences were downloaded from the GISAID database, of which 2,856 were included in the study following data curation. The sequences from India were dominated by the XBB.1.16* lineage (36.17%) followed by XBB.2.3* (12.11%) and XBB.1.5* (10.36%). Of the 2,856 cases, 693 were from Maharashtra; 386 of these were included in the clinical study. The clinical features of COVID-19 cases with XBB.1.16* infection (XBB.1.16* cases, 276 in number) showed that 92% of those had a symptomatic disease, with fever (67%), cough (42%), rhinorrhoea (33.7%), body ache (14.5%) and fatigue (14.1%) being the most common symptoms. Presence of comorbidity was found in 17.7% of the XBB.1.16* cases. Among the XBB.1.16* cases, 91.7% were vaccinated with at least one dose of vaccine against COVID-19. While 74.3% of XBB.1.16* cases were home-isolated; 25.7% needed hospitalization/institutional quarantine, of these, 33.8% needed oxygen therapy. Out of 276 XBB.1.16* cases, seven (2.5%) cases succumbed to the disease. Majority of XBB.1.16* cases who died belonged to an elderly age group (60 years and above), had underlying comorbid condition/s, and needed supplemental oxygen therapy. The clinical features of COVID-19 cases infected with other co-circulating Omicron variants were similar to XBB.1.16* cases. Conclusion: The study reveals that XBB.1.16* lineage has become the most predominant SARS-CoV-2 lineage in India. The study also shows that the clinical features and outcome of XBB.1.16* cases were similar to those of other co-circulating Omicron lineage infected cases in Maharashtra, India. Keywords: XBB.1.16, XBB.1.16.1, XBB.1.16*, XBB, Omicron variant, COVID-19, SARS-CoV-2, Clinical features

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Section: Article 1 Introductionmentioning

confidence: 99%

Section: Article 1 Introductionmentioning

confidence: 99%

Chasing SARS-CoV-2 XBB.1.16 Recombinant Lineage in India and the Clinical Profile of XBB.1.16 cases in Maharashtra, India

Karyakarte

Das

Rajmane

et al. 2023

Preprint

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“…Different studies proposed mathematic models to understand this phenomenon. The first model was built considering took cross-immunity and immune escape [20] . The report analysed the competitive relationship between Omicron and non-Omicron strains [20] .…”

mentioning

confidence: 99%

“…The first model was built considering took cross-immunity and immune escape [20] . The report analysed the competitive relationship between Omicron and non-Omicron strains [20] . The Authors hypothesised that competition between strains may influence not only the final size and replacement time of variants, but also the possibility of emergence of new variants [20] .…”

mentioning

confidence: 99%

“…The report analysed the competitive relationship between Omicron and non-Omicron strains [20] . The Authors hypothesised that competition between strains may influence not only the final size and replacement time of variants, but also the possibility of emergence of new variants [20] . However, this model has some limitations since it does not take into adequate account population heterogeneity and the effects of non-pharmaceutical measures [20] .…”

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confidence: 99%

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