Genomic epidemiology reveals multiple introductions and spread of SARS-CoV-2 in the Indian state of Karnataka

  1. Chitra Pattabiraman
  2. Farhat Habib
  3. Harsha P. K.
  4. Risha Rasheed
  5. Pramada Prasad
  6. Vijayalakshmi Reddy
  7. Prameela Dinesh
  8. Tina Damodar
  9. Kiran Hosallimath
  10. Anson K. George
  11. Nakka Vijay Kiran Reddy
  12. Banerjee John
  13. Amrita Pattanaik
  14. Narendra Kumar
  15. Reeta S. Mani
  16. Manjunatha M. Venkataswamy
  17. Shafeeq K. Shahul Hameed
  18. Prakash Kumar B. G.
  19. Anita Desai
  20. Ravi Vasanthapuram

Reviewed by

Read the full article See related articles

Listed in

Log in to save this article

Abstract

Karnataka, a state in south India, reported its first case of Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) infection on March 8, 2020, more than a month after the first case was reported in India. We used a combination of contact tracing and genomic epidemiology to trace the spread of SARS-CoV-2 in the state up until May 21, 2020 (1578 cases). We obtained 91 genomes of SARS-CoV-2 which clustered into seven lineages (Pangolin lineages—A, B, B.1, B.1.80, B.1.1, B.4, and B.6). The lineages in Karnataka were known to be circulating in China, Southeast Asia, Iran, Europe and other parts of India and are likely to have been imported into the state both by international and domestic travel. Our sequences grouped into 17 contact clusters and 24 cases with no known contacts. We found 14 of the 17 contact clusters had a single lineage of the virus, consistent with multiple introductions and most (12/17) were contained within a single district, reflecting local spread. In most of the 17 clusters, the index case (12/17) and spreaders (11/17) were symptomatic. Of the 91 sequences, 47 belonged to the B.6 lineage, including eleven of 24 cases with no known contact, indicating ongoing transmission of this lineage in the state. Genomic epidemiology of SARS-CoV-2 in Karnataka suggests multiple introductions of the virus followed by local transmission in parallel with ongoing viral evolution. This is the first study from India combining genomic data with epidemiological information emphasizing the need for an integrated approach to outbreak response.

Article activity feed

  1. Version published to 10.1371/journal.pone.0243412
  2. Version published to 10.1101/2020.07.10.20150045v4 on medRxiv
  3. Version published to 10.1101/2020.07.10.20150045v3 on medRxiv
  4. Version published to 10.1101/2020.07.10.20150045v2 on medRxiv
  5. ScreenIT

    SciScore for 10.1101/2020.07.10.20150045: (What is this?)

    Please note, not all rigor criteria are appropriate for all manuscripts.

    Table 1: Rigor

    Institutional Review Board StatementIRB: This study was approved by Institutional Ethics Committee (Basic and Neurosciences).
    Randomizationnot detected.
    Blindingnot detected.
    Power Analysisnot detected.
    Sex as a biological variablenot detected.

    Table 2: Resources

    Software and Algorithms
    SentencesResources
    The resulting DNA was cleaned up and added to FLO-MIN-106 flow cell and sequenced on the MinION.
    MinION
    suggested: (MinION, RRID:SCR_017985)
    Resulting reads were mapped to the RefSeq strain (NC_045512) using Minimap2 (v1.17) within Geneious Prime (Geneious Prime 2020.0.3).
    RefSeq
    suggested: (RefSeq, RRID:SCR_003496)
    Phylogenetic analysis, lineage assignment, detection of SNPs and amino acid replacements: Consensus sequences from the 47 genomes from this study were aligned with the reference genome using MUSCLE (v 3.8.425)19.
    MUSCLE
    suggested: (MUSCLE, RRID:SCR_011812)

    Results from OddPub: We did not detect open data. We also did not detect open code. Researchers are encouraged to share open data when possible (see Nature blog).

    Results from LimitationRecognizer: We detected the following sentences addressing limitations in the study:
    Our study had the following limitations – it is a single point analysis and some follow-up data is not available, for instance we do not know if individuals who were asymptomatic at testing later developed symptoms. Further, lineage assignments during an outbreak are dynamic and could change as more data is added and sequencing errors are accounted for. Notwithstanding these limitations, our analysis provides insights about introduction, spread, and establishment of SARS-CoV-2 in Karnataka. Further, we were able to capture both geographic diversity and obtain representation from the ten large contact clusters in the state. This was made possible by linking epidemiological information to genomic data. Integrating such an approach, in real time, into public health measures is essential for an effective outbreak response.

    Results from TrialIdentifier: No clinical trial numbers were referenced.

    Results from Barzooka: We did not find any issues relating to the usage of bar graphs.

    Results from JetFighter: We did not find any issues relating to colormaps.

    Results from rtransparent:
    • Thank you for including a conflict of interest statement. Authors are encouraged to include this statement when submitting to a journal.
    • Thank you for including a funding statement. Authors are encouraged to include this statement when submitting to a journal.
    • No protocol registration statement was detected.

    About SciScore

    SciScore is an automated tool that is designed to assist expert reviewers by finding and presenting formulaic information scattered throughout a paper in a standard, easy to digest format. SciScore checks for the presence and correctness of RRIDs (research resource identifiers), and for rigor criteria such as sex and investigator blinding. For details on the theoretical underpinning of rigor criteria and the tools shown here, including references cited, please follow this link.

  6. Version published to 10.1101/2020.07.10.20150045v1 on medRxiv