Detection of COVID-19 Outbreaks with Whole Genome Sequencing by Oxford Nanopore Technology in a Tertiary Centre, Malaysia

Introduction: The SARS-CoV-2 virus genome surveillance is important to monitor and track emerging variants. In a hospital setting, this will help identify both community and inter and intrahospital transmission and, if performed in real-time, will reduce the rate of the disease spread by immediately implementing infection control measures. The study aims to describe the COVID-19 outbreaks from the SARS-CoV-2 virus genomic surveillance by whole genome sequencing (WGS). Methods: 101 laboratory-confirmed clinical isolates belonging to patients with COVID-19 from June 2021 to June 2022 were subjected to WGS. The sequences were assembled using Bioinformatic tools EPI2ME software by the Oxford Nanopore technology (ONT). Based on the Q10 quality score, 86 isolates were subjected to phylogenetic tree analyses using the Maximum Likelihood method in MEGA 11 software. The sequencing process reached 100% coverage within 16 hours. The sociodemographic and clinical data were retrieved from both the hospital information systems (UNIMEDS) and patients’ medical records. Patients’ clinical presentations were categorized into mild and severe. Results: Phylogenetic analysis revealed 7 clusters of COVID-19 outbreaks with 24 patients showing only mild symptoms. The first three clusters (Cluster I, II and III) were found to have circulated from June to July 2021 and belonged to Clade GK (Delta variant). Cluster I showed transmission from a father to his 2 sons. Cluster II involves transmission between the 2 siblings from different families. Cluster III suggested an intrahospital transmission between medical staff and medical students. Meanwhile, from February to April 2022, four clusters were detected within Clade GRA (Omicron Variant). Cluster IV involved 3 medical students who stayed in the same residential college. Cluster V involved a group of medical staff with 1 university student. Cluster VI also involved a group of 3 university students, indicating possible transmission within their residential area. Cluster VII involved infection between family members from mother to her child. Discussion: In our study, we were able to track 1 intrahospital transmission, 1 inter-hospital and 5 community COVID-19 transmissions based on the phylogenetic tree. No new viral variants were found. The viral evolutions were of a similar pattern to what is being described globally. This WGS technique has been shown to give a faster turnaround time. Conclusion: The WGS has enabled laboratory scientists and epidemiologists to detect the occurrence of COVID-19 outbreaks as well as genomic surveillance. Due to its robustness and rapid sequencing results, this technique could be used in real-time in the hospital setting to prevent further transmission of the infection.

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Publication Details

Journal
International Journal of Infectious Diseases
Published
2025-02-28
DOI
https://doi.org/10.1016/j.ijid.2024.107452
Primary Topic
COVID-19 diagnosis using AI
Type
article
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article

Detection of COVID-19 Outbreaks with Whole Genome Sequencing by Oxford Nanopore Technology in a Tertiary Centre, Malaysia

Associate Professor Seok Mui Wang, Norazimah Tajudin, Abdullah Mohamad, Dr Siti Farah Nawi
International Journal of Infectious Diseases
COVID-19 diagnosis using AI
article

Detection of COVID-19 Outbreaks with Whole Genome Sequencing by Oxford Nanopore Technology in a Tertiary Centre, Malaysia

Associate Professor Seok Mui Wang, Norazimah Tajudin, Abdullah Mohamad, Dr Siti Farah Nawi
article en

Abstract

Introduction: The SARS-CoV-2 virus genome surveillance is important to monitor and track emerging variants. In a hospital setting, this will help identify both community and inter and intrahospital transmission and, if performed in real-time, will reduce the rate of the disease spread by immediately implementing infection control measures. The study aims to describe the COVID-19 outbreaks from the SARS-CoV-2 virus genomic surveillance by whole genome sequencing (WGS). Methods: 101 laboratory-confirmed clinical isolates belonging to patients with COVID-19 from June 2021 to June 2022 were subjected to WGS. The sequences were assembled using Bioinformatic tools EPI2ME software by the Oxford Nanopore technology (ONT). Based on the Q10 quality score, 86 isolates were subjected to phylogenetic tree analyses using the Maximum Likelihood method in MEGA 11 software. The sequencing process reached 100% coverage within 16 hours. The sociodemographic and clinical data were retrieved from both the hospital information systems (UNIMEDS) and patients’ medical records. Patients’ clinical presentations were categorized into mild and severe. Results: Phylogenetic analysis revealed 7 clusters of COVID-19 outbreaks with 24 patients showing only mild symptoms. The first three clusters (Cluster I, II and III) were found to have circulated from June to July 2021 and belonged to Clade GK (Delta variant). Cluster I showed transmission from a father to his 2 sons. Cluster II involves transmission between the 2 siblings from different families. Cluster III suggested an intrahospital transmission between medical staff and medical students. Meanwhile, from February to April 2022, four clusters were detected within Clade GRA (Omicron Variant). Cluster IV involved 3 medical students who stayed in the same residential college. Cluster V involved a group of medical staff with 1 university student. Cluster VI also involved a group of 3 university students, indicating possible transmission within their residential area. Cluster VII involved infection between family members from mother to her child. Discussion: In our study, we were able to track 1 intrahospital transmission, 1 inter-hospital and 5 community COVID-19 transmissions based on the phylogenetic tree. No new viral variants were found. The viral evolutions were of a similar pattern to what is being described globally. This WGS technique has been shown to give a faster turnaround time. Conclusion: The WGS has enabled laboratory scientists and epidemiologists to detect the occurrence of COVID-19 outbreaks as well as genomic surveillance. Due to its robustness and rapid sequencing results, this technique could be used in real-time in the hospital setting to prevent further transmission of the infection.

International Journal of Infectious DiseasesVol. 152
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Openalex Percentile: Top 94%
COVID-19 diagnosis using AI
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