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  <doi_batch_id>aspg-41-3371-1792223297</doi_batch_id>
  <timestamp>20261017074817</timestamp>
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  <journal>
   <journal_metadata language="en">
    <full_title>Metaheuristic Optimization Review</full_title>
    <abbrev_title>MOR</abbrev_title>
    <issn media_type="electronic">3066-280X</issn>
   </journal_metadata>
   <journal_issue>
    <publication_date media_type="online">
     <year>2024</year>
    </publication_date>
    <journal_volume>
     <volume>1</volume>
    </journal_volume>
    <issue>2</issue>
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   <journal_article publication_type="full_text">
    <titles>
     <title>Optimizing AI Models for COVID-19 Detection and Forecasting: A Comprehensive Study</title>
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     <person_name sequence="first" contributor_role="author">
      <given_name>Abdelhameed</given_name>
      <surname>Ibrahim</surname>
      <affiliations>
       <institution>
        <institution_name>School of ICT, Faculty of Engineering, Design and Information &amp; Communications Technology (EDICT), Bahrain Polytechnic, PO Box 33349, Isa Town, Bahrain</institution_name>
       </institution>
      </affiliations>
     </person_name>
    </contributors>
    <jats:abstract>
     <jats:p>This systematic review explores the use of artificial intelligence (AI) and machine learning (ML) during the COVID-19 disease outbreak. AI/ML models may interpret medical images, auditory input, and patient records to diagnose early enough, thus enhancing the likelihood of positive patient outcomes. Coupled with optimization algorithms, deep learning methods have predicted COVID-19 from chest X-rays and CT scans with unprecedented high accuracy. This review, therefore, synthesizes the existing literature and looks at the significant emphases, gaps, and potential trends of applying AI in diagnosing COVID-19 and forecasting outbreaks. Further, the advancement of AI and ML in this domain needs to be known to enhance global preventive diagnostic techniques for future pandemics.</jats:p>
    </jats:abstract>
    <publication_date media_type="online">
     <year>2024</year>
    </publication_date>
    <pages>
     <first_page>18</first_page>
     <last_page>22</last_page>
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     <item_number item_number_type="article-number">3371</item_number>
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     <doi>10.54216/MOR.010204</doi>
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