Dataset Information

The Emergence and Spread of Novel SARS-CoV-2 Variants.

ABSTRACT: Since severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) began to spread in late 2019, laboratories around the world have widely used whole genome sequencing (WGS) to continuously monitor the changes in the viral genes and discovered multiple subtypes or branches evolved from SARS-CoV-2. Recently, several novel SARS-CoV-2 variants have been found to be more transmissible. They may affect the immune response caused by vaccines and natural infections and reduce the sensitivity to neutralizing antibodies. We analyze the distribution characteristics of prevalent SARS-CoV-2 variants and the frequency of mutant sites based on the data available from GISAID and PANGO by R 4.0.2 and ArcGIS 10.2. Our analysis suggests that B.1.1.7, B.1.351, and P.1 are more easily spreading than other variants, and the key mutations of S protein, including N501Y, E484K, and K417N/T, have high mutant frequencies, which may have become the main genotypes for the spread of SARS-CoV-2.

SUBMITTER: Yi H

PROVIDER: S-EPMC8364952 | biostudies-literature | 2021

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

The Emergence and Spread of Novel SARS-CoV-2 Variants.

Yi Huaimin H Wang Jin J Wang Jiong J Lu Yuying Y Zhang Yali Y Peng Ruihao R Lu Jiahai J Chen Zeliang Z

Frontiers in public health 20210802

Since severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) began to spread in late 2019, laboratories around the world have widely used whole genome sequencing (WGS) to continuously monitor the changes in the viral genes and discovered multiple subtypes or branches evolved from SARS-CoV-2. Recently, several novel SARS-CoV-2 variants have been found to be more transmissible. They may affect the immune response caused by vaccines and natural infections and reduce the sensitivity to neutral ...[more]

PMID: 34409009

Similar Datasets

Project description:Coronavirus disease 2019 (COVID-19) has emerged in December 2019 when the first case was reported in Wuhan, China and turned into a pandemic with 27 million (September 9th) cases. Currently, there are over 95,000 complete genome sequences of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the virus causing COVID-19, in public databases, accompanying a growing number of studies. Nevertheless, there is still much to learn about the viral population variation when the virus is evolving as it continues to spread. We have analyzed SARS-CoV-2 genomes to identify the most variant sites, as well as the stable, conserved ones in samples collected in the Netherlands until June 2020. We identified the most frequent mutations in different geographies. We also performed a phylogenetic study focused on the Netherlands to detect novel variants emerging in the late stages of the pandemic and forming local clusters. We investigated the S and N proteins on SARS-CoV-2 genomes in the Netherlands and found the most variant and stable sites to guide development of diagnostics assays and vaccines. We observed that while the SARS-CoV-2 genome has accumulated mutations, diverging from reference sequence, the variation landscape is dominated by four mutations globally, suggesting the current reference does not represent the virus samples circulating currently. In addition, we detected novel variants of SARS-CoV-2 almost unique to the Netherlands that form localized clusters and region-specific sub-populations indicating community spread. We explored SARS-CoV-2 variants in the Netherlands until June 2020 within a global context; our results provide insight into the viral population diversity for localized efforts in tracking the transmission of COVID-19, as well as sequenced-based approaches in diagnostics and therapeutics. We emphasize that little diversity is observed globally in recent samples despite the increased number of mutations relative to the established reference sequence. We suggest sequence-based analyses should opt for a consensus representation to adequately cover the genomic variation observed to speed up diagnostics and vaccine design.

Project description:The successive emergence of SARS-CoV-2 mutations has led to an unprecedented increase in COVID-19 incidence worldwide. Currently, vaccination is considered to be the best available solution to control the ongoing COVID-19 pandemic. However, public opposition to vaccination persists in many countries, which can lead to increased COVID-19 caseloads and hence greater opportunities for vaccine-evasive mutant strains to arise. To determine the extent that public opinion regarding vaccination can induce or hamper the emergence of new variants, we develop a model that couples a compartmental disease transmission framework featuring two strains of SARS-CoV-2 with game theoretical dynamics on whether or not to vaccinate. We combine semi-stochastic and deterministic simulations to explore the effect of mutation probability, perceived cost of receiving vaccines, and perceived risks of infection on the emergence and spread of mutant SARS-CoV-2 strains. We find that decreasing the perceived costs of being vaccinated and increasing the perceived risks of infection (that is, decreasing vaccine hesitation) will decrease the possibility of vaccine-resistant mutant strains becoming established by about fourfold for intermediate mutation rates. Conversely, we find increasing vaccine hesitation to cause both higher probability of mutant strains emerging and more wild-type cases after the mutant strain has appeared. We also find that once a new variant has emerged, perceived risk of being infected by the original variant plays a much larger role than perceptions of the new variant in determining future outbreak characteristics. Furthermore, we find that rapid vaccination under non-pharmaceutical interventions is a highly effective strategy for preventing new variant emergence, due to interaction effects between non-pharmaceutical interventions and public support for vaccination. Our findings indicate that policies that combine combating vaccine-related misinformation with non-pharmaceutical interventions (such as reducing social contact) will be the most effective for avoiding the establishment of harmful new variants.

Dataset Information

The Emergence and Spread of Novel SARS-CoV-2 Variants.

Publications

The Emergence and Spread of Novel SARS-CoV-2 Variants.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets