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  <doi_batch_id>aspg-33-4453-1792223598</doi_batch_id>
  <timestamp>20261017075318</timestamp>
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  <journal>
   <journal_metadata language="en">
    <full_title>Galoitica: Journal of Mathematical Structures and Applications</full_title>
    <abbrev_title>GJMSA</abbrev_title>
    <issn media_type="electronic">2834-5568</issn>
   </journal_metadata>
   <journal_issue>
    <publication_date media_type="online">
     <year>2026</year>
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    <journal_volume>
     <volume>13</volume>
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    <issue>2</issue>
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    <titles>
     <title>Using Mathematical Modeling to Predict the Spread of Epidemics</title>
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    <contributors>
     <person_name sequence="first" contributor_role="author">
      <given_name>Nabaa</given_name>
      <surname>Fawze</surname>
     </person_name>
    </contributors>
    <jats:abstract>
     <jats:p>In this paper, we give a brief overview of the advances in mathematical modelling applied to epidemiology and the achievements that have been made in epidemiological research using mathematical models. Since the early 1900s when the well-known susceptible–infected–recovered model was proposed, mathematical treatments have played an important role in epidemiology. We introduce the deterministic SIR model and explain the value of computational tools, mathematical modelling and statistical analysis for the understanding of the development of disease and the size of emerging outbreaks. These are issues that are of major concern in everyday life and public health. With the emergence of the novel coronavirus in recent times, it has become evident that mathematical modeling along with computational and statistical tools are extremely relevant. Hence, this study explains the mathematical modelling in Epidemiology and demonstrates the advancement of the discipline over the years.</jats:p>
    </jats:abstract>
    <publication_date media_type="online">
     <year>2026</year>
    </publication_date>
    <pages>
     <first_page>06</first_page>
     <last_page>09</last_page>
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     <item_number item_number_type="article-number">4453</item_number>
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     <doi>10.54216/GJMSA.130202</doi>
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