Mathematical modeling of respiratory viral infection and applications to SARS‐CoV‐2 progression
Viral infection in cell culture and tissue is modeled with delay reaction‐diffusion equations. It is shown that progression of viral infection can be characterized by the viral replication number, time‐dependent viral load, and the speed of infection spreading. These three characteristics are determ...
Gespeichert in:
| Veröffentlicht in: | Mathematical methods in the applied sciences 2023-01, Vol.46 (2), p.1740-1751 |
|---|---|
| Hauptverfasser: | , , , |
| Format: | Artikel |
| Sprache: | eng |
| Schlagworte: | |
| Online-Zugang: | Volltext |
| Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
| container_end_page | 1751 |
|---|---|
| container_issue | 2 |
| container_start_page | 1740 |
| container_title | Mathematical methods in the applied sciences |
| container_volume | 46 |
| creator | Ait Mahiout, Latifa Bessonov, Nikolai Kazmierczak, Bogdan Volpert, Vitaly |
| description | Viral infection in cell culture and tissue is modeled with delay reaction‐diffusion equations. It is shown that progression of viral infection can be characterized by the viral replication number, time‐dependent viral load, and the speed of infection spreading. These three characteristics are determined through the original model parameters including the rates of cell infection and of virus production in the infected cells. The clinical manifestations of viral infection, depending on tissue damage, correlate with the speed of infection spreading, while the infectivity of a respiratory infection depends on the viral load in the upper respiratory tract. Parameter determination from the experiments on Delta and Omicron variants allows the estimation of the infection spreading speed and viral load. Different variants of the SARS‐CoV‐2 infection are compared confirming that Omicron is more infectious and has less severe symptoms than Delta variant. Within the same variant, spreading speed (symptoms) correlates with viral load allowing prognosis of disease progression. |
| doi_str_mv | 10.1002/mma.8606 |
| format | Article |
| fullrecord | <record><control><sourceid>proquest_pubme</sourceid><recordid>TN_cdi_pubmedcentral_primary_oai_pubmedcentral_nih_gov_9538414</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><sourcerecordid>2755460476</sourcerecordid><originalsourceid>FETCH-LOGICAL-c4726-17695ca124a6d6ff8ecd47cdc341b7f46d609306beb854e91a0ecb8f3b93ca853</originalsourceid><addsrcrecordid>eNp1kd9O2zAUxq0JNAqbtCdAlriBizAfx3GSG6Sq4s-kVpPWbbeW4zitURIHOy3qHY_AM_IkOGthA2k3ts85v_P52B9CX4CcAyH0a9PI84wT_gGNgOR5BCzle2hEICURo8AO0KH3t4SQDIB-RAcxpyylNBshNZP9UjeyN0rWuLGlrk27wLbCTvvOONlbt8HrcKixaSutemNbLNsSy66rQ9MQe9xbPB__mD89PE7s77BS3Dm7CBI-lD-h_UrWXn_e7Ufo19Xlz8lNNP1-_W0ynkYqDMMjSHmeKAmUSV7yqsq0KlmqShUzKNKKhSTJY8ILXWQJ0zlIolWRVXGRx0pmSXyELra63apodKl024exRedMI91GWGnE20prlmJh1yJP4owBCwJnW4Hlu7ab8VQMOcIg5imFNQT2dHeZs3cr7XvRGK90XctW25UXNKUJY5TDgJ68Q2_tyrXhKwKVJIyTYNdfQeWs905XrxMAEYPLIrgsBpcDevzvQ1_BF1sDEG2Be1PrzX-FxGw2_iP4DDpYs0Q</addsrcrecordid><sourcetype>Open Access Repository</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>2755460476</pqid></control><display><type>article</type><title>Mathematical modeling of respiratory viral infection and applications to SARS‐CoV‐2 progression</title><source>Wiley Online Library</source><creator>Ait Mahiout, Latifa ; Bessonov, Nikolai ; Kazmierczak, Bogdan ; Volpert, Vitaly</creator><creatorcontrib>Ait Mahiout, Latifa ; Bessonov, Nikolai ; Kazmierczak, Bogdan ; Volpert, Vitaly</creatorcontrib><description>Viral infection in cell culture and tissue is modeled with delay reaction‐diffusion equations. It is shown that progression of viral infection can be characterized by the viral replication number, time‐dependent viral load, and the speed of infection spreading. These three characteristics are determined through the original model parameters including the rates of cell infection and of virus production in the infected cells. The clinical manifestations of viral infection, depending on tissue damage, correlate with the speed of infection spreading, while the infectivity of a respiratory infection depends on the viral load in the upper respiratory tract. Parameter determination from the experiments on Delta and Omicron variants allows the estimation of the infection spreading speed and viral load. Different variants of the SARS‐CoV‐2 infection are compared confirming that Omicron is more infectious and has less severe symptoms than Delta variant. Within the same variant, spreading speed (symptoms) correlates with viral load allowing prognosis of disease progression.</description><identifier>ISSN: 0170-4214</identifier><identifier>EISSN: 1099-1476</identifier><identifier>DOI: 10.1002/mma.8606</identifier><identifier>PMID: 36247228</identifier><language>eng</language><publisher>Germany: Wiley Subscription Services, Inc</publisher><subject>Analysis of PDEs ; Emerging diseases ; Human health and pathology ; Infections ; Infectious diseases ; Life Sciences ; Mathematical models ; Mathematics ; Microbiology and Parasitology ; Parameters ; reaction‐diffusion equations ; SARS‐CoV‐2 variants ; Severe acute respiratory syndrome coronavirus 2 ; Signs and symptoms ; spreading speed ; Viral diseases ; viral infection ; Viral infections ; viral load ; Virology</subject><ispartof>Mathematical methods in the applied sciences, 2023-01, Vol.46 (2), p.1740-1751</ispartof><rights>2022 John Wiley & Sons, Ltd.</rights><rights>2023 John Wiley & Sons, Ltd.</rights><rights>Distributed under a Creative Commons Attribution 4.0 International License</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c4726-17695ca124a6d6ff8ecd47cdc341b7f46d609306beb854e91a0ecb8f3b93ca853</citedby><cites>FETCH-LOGICAL-c4726-17695ca124a6d6ff8ecd47cdc341b7f46d609306beb854e91a0ecb8f3b93ca853</cites><orcidid>0000-0002-5323-9934</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://onlinelibrary.wiley.com/doi/pdf/10.1002%2Fmma.8606$$EPDF$$P50$$Gwiley$$H</linktopdf><linktohtml>$$Uhttps://onlinelibrary.wiley.com/doi/full/10.1002%2Fmma.8606$$EHTML$$P50$$Gwiley$$H</linktohtml><link.rule.ids>230,314,780,784,885,1417,27924,27925,45574,45575</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/36247228$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink><backlink>$$Uhttps://hal.science/hal-04136721$$DView record in HAL$$Hfree_for_read</backlink></links><search><creatorcontrib>Ait Mahiout, Latifa</creatorcontrib><creatorcontrib>Bessonov, Nikolai</creatorcontrib><creatorcontrib>Kazmierczak, Bogdan</creatorcontrib><creatorcontrib>Volpert, Vitaly</creatorcontrib><title>Mathematical modeling of respiratory viral infection and applications to SARS‐CoV‐2 progression</title><title>Mathematical methods in the applied sciences</title><addtitle>Math Methods Appl Sci</addtitle><description>Viral infection in cell culture and tissue is modeled with delay reaction‐diffusion equations. It is shown that progression of viral infection can be characterized by the viral replication number, time‐dependent viral load, and the speed of infection spreading. These three characteristics are determined through the original model parameters including the rates of cell infection and of virus production in the infected cells. The clinical manifestations of viral infection, depending on tissue damage, correlate with the speed of infection spreading, while the infectivity of a respiratory infection depends on the viral load in the upper respiratory tract. Parameter determination from the experiments on Delta and Omicron variants allows the estimation of the infection spreading speed and viral load. Different variants of the SARS‐CoV‐2 infection are compared confirming that Omicron is more infectious and has less severe symptoms than Delta variant. Within the same variant, spreading speed (symptoms) correlates with viral load allowing prognosis of disease progression.</description><subject>Analysis of PDEs</subject><subject>Emerging diseases</subject><subject>Human health and pathology</subject><subject>Infections</subject><subject>Infectious diseases</subject><subject>Life Sciences</subject><subject>Mathematical models</subject><subject>Mathematics</subject><subject>Microbiology and Parasitology</subject><subject>Parameters</subject><subject>reaction‐diffusion equations</subject><subject>SARS‐CoV‐2 variants</subject><subject>Severe acute respiratory syndrome coronavirus 2</subject><subject>Signs and symptoms</subject><subject>spreading speed</subject><subject>Viral diseases</subject><subject>viral infection</subject><subject>Viral infections</subject><subject>viral load</subject><subject>Virology</subject><issn>0170-4214</issn><issn>1099-1476</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2023</creationdate><recordtype>article</recordtype><recordid>eNp1kd9O2zAUxq0JNAqbtCdAlriBizAfx3GSG6Sq4s-kVpPWbbeW4zitURIHOy3qHY_AM_IkOGthA2k3ts85v_P52B9CX4CcAyH0a9PI84wT_gGNgOR5BCzle2hEICURo8AO0KH3t4SQDIB-RAcxpyylNBshNZP9UjeyN0rWuLGlrk27wLbCTvvOONlbt8HrcKixaSutemNbLNsSy66rQ9MQe9xbPB__mD89PE7s77BS3Dm7CBI-lD-h_UrWXn_e7Ufo19Xlz8lNNP1-_W0ynkYqDMMjSHmeKAmUSV7yqsq0KlmqShUzKNKKhSTJY8ILXWQJ0zlIolWRVXGRx0pmSXyELra63apodKl024exRedMI91GWGnE20prlmJh1yJP4owBCwJnW4Hlu7ab8VQMOcIg5imFNQT2dHeZs3cr7XvRGK90XctW25UXNKUJY5TDgJ68Q2_tyrXhKwKVJIyTYNdfQeWs905XrxMAEYPLIrgsBpcDevzvQ1_BF1sDEG2Be1PrzX-FxGw2_iP4DDpYs0Q</recordid><startdate>20230130</startdate><enddate>20230130</enddate><creator>Ait Mahiout, Latifa</creator><creator>Bessonov, Nikolai</creator><creator>Kazmierczak, Bogdan</creator><creator>Volpert, Vitaly</creator><general>Wiley Subscription Services, Inc</general><general>Wiley</general><general>John Wiley and Sons Inc</general><scope>NPM</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>7TB</scope><scope>8FD</scope><scope>FR3</scope><scope>JQ2</scope><scope>KR7</scope><scope>7X8</scope><scope>1XC</scope><scope>5PM</scope><orcidid>https://orcid.org/0000-0002-5323-9934</orcidid></search><sort><creationdate>20230130</creationdate><title>Mathematical modeling of respiratory viral infection and applications to SARS‐CoV‐2 progression</title><author>Ait Mahiout, Latifa ; Bessonov, Nikolai ; Kazmierczak, Bogdan ; Volpert, Vitaly</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c4726-17695ca124a6d6ff8ecd47cdc341b7f46d609306beb854e91a0ecb8f3b93ca853</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2023</creationdate><topic>Analysis of PDEs</topic><topic>Emerging diseases</topic><topic>Human health and pathology</topic><topic>Infections</topic><topic>Infectious diseases</topic><topic>Life Sciences</topic><topic>Mathematical models</topic><topic>Mathematics</topic><topic>Microbiology and Parasitology</topic><topic>Parameters</topic><topic>reaction‐diffusion equations</topic><topic>SARS‐CoV‐2 variants</topic><topic>Severe acute respiratory syndrome coronavirus 2</topic><topic>Signs and symptoms</topic><topic>spreading speed</topic><topic>Viral diseases</topic><topic>viral infection</topic><topic>Viral infections</topic><topic>viral load</topic><topic>Virology</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Ait Mahiout, Latifa</creatorcontrib><creatorcontrib>Bessonov, Nikolai</creatorcontrib><creatorcontrib>Kazmierczak, Bogdan</creatorcontrib><creatorcontrib>Volpert, Vitaly</creatorcontrib><collection>PubMed</collection><collection>CrossRef</collection><collection>Mechanical & Transportation Engineering Abstracts</collection><collection>Technology Research Database</collection><collection>Engineering Research Database</collection><collection>ProQuest Computer Science Collection</collection><collection>Civil Engineering Abstracts</collection><collection>MEDLINE - Academic</collection><collection>Hyper Article en Ligne (HAL)</collection><collection>PubMed Central (Full Participant titles)</collection><jtitle>Mathematical methods in the applied sciences</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Ait Mahiout, Latifa</au><au>Bessonov, Nikolai</au><au>Kazmierczak, Bogdan</au><au>Volpert, Vitaly</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Mathematical modeling of respiratory viral infection and applications to SARS‐CoV‐2 progression</atitle><jtitle>Mathematical methods in the applied sciences</jtitle><addtitle>Math Methods Appl Sci</addtitle><date>2023-01-30</date><risdate>2023</risdate><volume>46</volume><issue>2</issue><spage>1740</spage><epage>1751</epage><pages>1740-1751</pages><issn>0170-4214</issn><eissn>1099-1476</eissn><abstract>Viral infection in cell culture and tissue is modeled with delay reaction‐diffusion equations. It is shown that progression of viral infection can be characterized by the viral replication number, time‐dependent viral load, and the speed of infection spreading. These three characteristics are determined through the original model parameters including the rates of cell infection and of virus production in the infected cells. The clinical manifestations of viral infection, depending on tissue damage, correlate with the speed of infection spreading, while the infectivity of a respiratory infection depends on the viral load in the upper respiratory tract. Parameter determination from the experiments on Delta and Omicron variants allows the estimation of the infection spreading speed and viral load. Different variants of the SARS‐CoV‐2 infection are compared confirming that Omicron is more infectious and has less severe symptoms than Delta variant. Within the same variant, spreading speed (symptoms) correlates with viral load allowing prognosis of disease progression.</abstract><cop>Germany</cop><pub>Wiley Subscription Services, Inc</pub><pmid>36247228</pmid><doi>10.1002/mma.8606</doi><tpages>12</tpages><orcidid>https://orcid.org/0000-0002-5323-9934</orcidid><oa>free_for_read</oa></addata></record> |
| fulltext | fulltext |
| identifier | ISSN: 0170-4214 |
| ispartof | Mathematical methods in the applied sciences, 2023-01, Vol.46 (2), p.1740-1751 |
| issn | 0170-4214 1099-1476 |
| language | eng |
| recordid | cdi_pubmedcentral_primary_oai_pubmedcentral_nih_gov_9538414 |
| source | Wiley Online Library |
| subjects | Analysis of PDEs Emerging diseases Human health and pathology Infections Infectious diseases Life Sciences Mathematical models Mathematics Microbiology and Parasitology Parameters reaction‐diffusion equations SARS‐CoV‐2 variants Severe acute respiratory syndrome coronavirus 2 Signs and symptoms spreading speed Viral diseases viral infection Viral infections viral load Virology |
| title | Mathematical modeling of respiratory viral infection and applications to SARS‐CoV‐2 progression |
| url | https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2025-01-08T01%3A26%3A12IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-proquest_pubme&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.genre=article&rft.atitle=Mathematical%20modeling%20of%20respiratory%20viral%20infection%20and%20applications%20to%20SARS%E2%80%90CoV%E2%80%902%20progression&rft.jtitle=Mathematical%20methods%20in%20the%20applied%20sciences&rft.au=Ait%20Mahiout,%20Latifa&rft.date=2023-01-30&rft.volume=46&rft.issue=2&rft.spage=1740&rft.epage=1751&rft.pages=1740-1751&rft.issn=0170-4214&rft.eissn=1099-1476&rft_id=info:doi/10.1002/mma.8606&rft_dat=%3Cproquest_pubme%3E2755460476%3C/proquest_pubme%3E%3Curl%3E%3C/url%3E&disable_directlink=true&sfx.directlink=off&sfx.report_link=0&rft_id=info:oai/&rft_pqid=2755460476&rft_id=info:pmid/36247228&rfr_iscdi=true |