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<ArticleSet>
<Article>
<Journal>
				<PublisherName>University of Guilan</PublisherName>
				<JournalTitle>Journal of Mathematical Modeling</JournalTitle>
				<Issn>2345-394X</Issn>
				<Volume>14</Volume>
				<Issue>2</Issue>
				<PubDate PubStatus="epublish">
					<Year>2026</Year>
					<Month>05</Month>
					<Day>01</Day>
				</PubDate>
			</Journal>
<ArticleTitle>Full and partial controllability of the Kermack-Mckendrick system with time- varying incidence rates</ArticleTitle>
<VernacularTitle></VernacularTitle>
			<FirstPage>673</FirstPage>
			<LastPage>697</LastPage>
			<ELocationID EIdType="pii">9295</ELocationID>
			
<ELocationID EIdType="doi">10.22124/jmm.2025.31390.2818</ELocationID>
			
			<Language>EN</Language>
<AuthorList>
<Author>
					<FirstName>Hamza</FirstName>
					<LastName>El Mahjour</LastName>
<Affiliation>MASI research Team
Department of Information Systems and Communication
ENSAT, Abdelmalek Esaadi University, Morocco</Affiliation>

</Author>
<Author>
					<FirstName>Aadil</FirstName>
					<LastName>Lahrouz</LastName>
<Affiliation>LAM research laboratory
Department of Mathematics
FSTT, Abdelmalek Essaadi University</Affiliation>

</Author>
<Author>
					<FirstName>Omar</FirstName>
					<LastName>Zakary</LastName>
<Affiliation>Statistics and Modelling research team
Department of Mathematics
FSBM, University of Hassan II</Affiliation>

</Author>
<Author>
					<FirstName>Mariam</FirstName>
					<LastName>Redouane</LastName>
<Affiliation>LAM research laboratory
Department of Mathematics
FSTT, Abdelmalek Essaadi University</Affiliation>

</Author>
</AuthorList>
				<PublicationType>Journal Article</PublicationType>
			<History>
				<PubDate PubStatus="received">
					<Year>2025</Year>
					<Month>08</Month>
					<Day>12</Day>
				</PubDate>
			</History>
		<Abstract>This study contributes to epidemic control literature by introducing a time-varying inci-&lt;br /&gt;dence rate and establishing global controllability of the nonlinear SIR system, offering a&lt;br /&gt;practical framework for adaptive control strategies. We derive explicit solutions for partial&lt;br /&gt;controllability, demonstrating the feasibility of controlling the infected population, pro-&lt;br /&gt;viding guidance for outbreak management. Numerical methods exploiting an algorithmic&lt;br /&gt;approach achieve full control, targeting a desired state (Sd, Id). A novel hybrid method&lt;br /&gt;integrates analytical solutions with algorithmic optimization, leveraging explicit expres-&lt;br /&gt;sions for I(t) and S(t) to enhance precision and efficiency of epidemic control strategies,&lt;br /&gt;advancing adaptive management approaches</Abstract>
		<ObjectList>
			<Object Type="keyword">
			<Param Name="value">Epidemic model</Param>
			</Object>
			<Object Type="keyword">
			<Param Name="value">Varying infection rate</Param>
			</Object>
			<Object Type="keyword">
			<Param Name="value">Full Control</Param>
			</Object>
			<Object Type="keyword">
			<Param Name="value">Partial Control</Param>
			</Object>
			<Object Type="keyword">
			<Param Name="value">Hybrid Method</Param>
			</Object>
		</ObjectList>
<ArchiveCopySource DocType="pdf">https://jmm.guilan.ac.ir/article_9295_fd6f0670afa9641d22cff7f55adf61dd.pdf</ArchiveCopySource>
</Article>
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