<?xml version="1.0" encoding="UTF-8"?>
<mods xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xmlns="http://www.loc.gov/mods/v3" version="3.1" xsi:schemaLocation="http://www.loc.gov/mods/v3 http://www.loc.gov/standards/mods/v3/mods-3-1.xsd">
  <titleInfo>
    <title>Genomic epidemiology of Sars-Cov-2  in Malaysia</title>
  </titleInfo>
  <name type="personal">
    <namePart>Chew, Sian Hui</namePart>
    <role>
      <roleTerm authority="marcrelator" type="text">creator</roleTerm>
    </role>
    <role>
      <roleTerm type="text">author.</roleTerm>
    </role>
  </name>
  <typeOfResource>text</typeOfResource>
  <originInfo>
    <place>
      <placeTerm type="code" authority="marccountry">my</placeTerm>
    </place>
    <dateIssued encoding="marc">2025</dateIssued>
    <copyrightDate encoding="marc">2025</copyrightDate>
    <issuance>monographic</issuance>
  </originInfo>
  <language>
    <languageTerm authority="iso639-2b" type="code">eng</languageTerm>
  </language>
  <physicalDescription>
    <form authority="marcform">electronic</form>
    <extent>xiii, 108 pages : illustrations ;</extent>
  </physicalDescription>
  <abstract>The COVID-19 pandemic, caused by the novel coronavirus SARS-CoV-2, has  posed unprecedented challenges to global health systems. This research project aims to  elucidate the genomic epidemiology of SARS-CoV-2 in Malaysia by analysing whole  genome sequences of SARS-CoV-2 sourced from the GISAID EpiCoV databases.  Bioinformatics tools were used to construct phylogenetic tree and investigate the  evolutionary relationships among the virus strains circulating in Malaysia from the  beginning of the pandemic until 30 November 2024. The research began with the  collection of genomic epidemiological data of SARS-CoV-2 from KKMNOW and  Ministry of Health Malaysia (MOH) websites, including daily confirmed cases, death  cases, hospitalizations, and daily testing performed. The genomic epidemiological data  collected was compiled in Excel files and visualised into choropleth maps using Tableau  Public to depict the spatial distribution of key indicators across different states in  Malaysia. The dataset of VOIs and VOCs collected from the GISAID EpiCoV databases  were visualised into choropleth map to track the circulation of variants within Malaysia.  The analysis of SARS-CoV-2 sequences was conducted by retrieving the data from  GISAID EpiCoV databases. The phylogenetic tree was constructed using Nextstrain – Nextclade tool and visualised using Auspice.us to understand the evolution of SARSCoV-2 variants. The findings suggested that nine distinct waves of COVID-19 were  identified, with the initial case reported on 25 January 2020. Despite record-high cases  during the fifth wave, health conditions stabilized, leading to varying trends in subsequent  waves. A decline in COVID-19 outbreaks was observed starting from the sixth wave of  the pandemic. In 2024, Malaysia transitioned into an endemic phase due to the relatively  low of reported confirmed cases. Selangor consistently reported high numbers of cases, deaths, hospitalizations, and diagnostic tests, linked to the population density. The  genomic surveillance revealed that AY.59 as the predominant variant circulating in  Malaysia, followed by AY.79. AY.23 was dominated in Sarawak, accounting for 61.34%  (2063 high coverage sequences submitted). AY.76 was predominated in Sabah and  Labuan, accounting for 38.38% (137 sequences) and 40% (16 sequences), respectively.  BA.2 emerged as the most prevalent variant in Selangor, accounting for 40.24% (957  sequences). The top 5 SARS-CoV-2 lineages emerged during 2024 were JN.1, KP.2.3,  XDY, KP.1.1.3, and KP.3. The phylogenetic analysis showed the ubiquity of Omicron  variants with diverse sub-lineages such as ‘21L’ and ‘23I’, along with novel recombinant  lineages like XBB and variant JN.1. This study provided the comprehension knowledge  of the genomic epidemiology of SARS-CoV-2 in Malaysia with valuable insights into  the evolution and transmission patterns of the virus. Genetic data and geographical  dispersion maps are essential assets for policymakers and health professionals in  effectively managing ongoing pandemic challenges.</abstract>
  <targetAudience authority="marctarget">specialized</targetAudience>
  <note type="statement of responsibility">Chew Sian Hui</note>
  <note>Faculty of Industrial Sciences and Technology</note>
  <note>Final Year Report (Bachelor of Applied Science in Industrial Biotechnology) -- Universiti Malaysia Pahang Al-Sultan Abdullah - 2025</note>
  <note>Include bibliographical reference</note>
  <subject authority="lcsh">
    <name type="corporate">
      <namePart>Faculty of Industrial Sciences and Technology</namePart>
    </name>
    <topic>Dissertations</topic>
  </subject>
  <subject authority="lcsh">
    <topic>Universities and colleges</topic>
    <topic>Dissertations</topic>
  </subject>
  <subject authority="lcsh">
    <topic>Final Year Report</topic>
    <topic>Dissertations</topic>
  </subject>
  <identifier type="isbn">THE0010291 (Local)</identifier>
  <identifier type="uri">http://umpir.ump.edu.my/id/eprint/45600</identifier>
  <location>
    <url>http://umpir.ump.edu.my/id/eprint/45600</url>
  </location>
  <recordInfo>
    <recordContentSource authority="marcorg">UMPSA</recordContentSource>
    <recordCreationDate encoding="marc">260120</recordCreationDate>
    <recordChangeDate encoding="iso8601">20260120180337.0</recordChangeDate>
    <languageOfCataloging>
      <languageTerm authority="iso639-2b" type="code">eng</languageTerm>
    </languageOfCataloging>
  </recordInfo>
</mods>
