Analyzing a class of stochastic SIRS models under imperfect vaccination
This work is devoted to a class of stochastic infectious disease models, namely, stochastic SIRS models under imperfect vaccination. Because of the inclusion of the vaccination, the underlying models are more difficult to analyze than that of the previously considered in the literature. Our main eff...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Hieu, Nguyen T. [verfasserIn] Nguyen, Dang H. [verfasserIn] Nguyen, Nhu N. [verfasserIn] Yin, George [verfasserIn] |
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| Format: |
E-Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2023 |
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| Schlagwörter: |
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| Übergeordnetes Werk: |
Enthalten in: Journal of the Franklin Institute - Franklin Institute$gPhiladelphia, Pa. ; ID: gnd/38821-X, Amsterdam [u.a.] : Elsevier Science, 1829, 361, Seite 1284-1302 |
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| Übergeordnetes Werk: |
volume:361 ; pages:1284-1302 |
| DOI / URN: |
10.1016/j.jfranklin.2023.12.053 |
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| Katalog-ID: |
ELV06696945X |
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| 245 | 1 | 0 | |a Analyzing a class of stochastic SIRS models under imperfect vaccination |
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| 520 | |a This work is devoted to a class of stochastic infectious disease models, namely, stochastic SIRS models under imperfect vaccination. Because of the inclusion of the vaccination, the underlying models are more difficult to analyze than that of the previously considered in the literature. Our main effort is devoted to overcoming the difficulties and deriving a complete characterization of longtime behavior of systems. Numerical examples are provided to present computational evidence. These examples also provide insight that the discrete event process (the random switching) can reverse persistence to extinction, and vice versa. | ||
| 650 | 4 | |a SIRS model | |
| 650 | 4 | |a Vaccination | |
| 650 | 4 | |a Persistence | |
| 650 | 4 | |a Extinction | |
| 650 | 4 | |a Regime-switching system | |
| 700 | 1 | |a Nguyen, Dang H. |e verfasserin |4 aut | |
| 700 | 1 | |a Nguyen, Nhu N. |e verfasserin |4 aut | |
| 700 | 1 | |a Yin, George |e verfasserin |0 (orcid)0000-0002-2951-0704 |4 aut | |
| 773 | 0 | 8 | |i Enthalten in |a Franklin Institute$gPhiladelphia, Pa. ; ID: gnd/38821-X |t Journal of the Franklin Institute |d Amsterdam [u.a.] : Elsevier Science, 1829 |g 361, Seite 1284-1302 |h Online-Ressource |w (DE-627)320464954 |w (DE-600)2007754-3 |w (DE-576)259484628 |x 1879-2693 |7 nnns |
| 773 | 1 | 8 | |g volume:361 |g pages:1284-1302 |
| 912 | |a GBV_USEFLAG_U | ||
| 912 | |a GBV_ELV | ||
| 912 | |a SYSFLAG_U | ||
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| 912 | |a GBV_ILN_22 | ||
| 912 | |a GBV_ILN_23 | ||
| 912 | |a GBV_ILN_24 | ||
| 912 | |a GBV_ILN_31 | ||
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| 912 | |a GBV_ILN_90 | ||
| 912 | |a GBV_ILN_95 | ||
| 912 | |a GBV_ILN_100 | ||
| 912 | |a GBV_ILN_105 | ||
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| 912 | |a GBV_ILN_213 | ||
| 912 | |a GBV_ILN_224 | ||
| 912 | |a GBV_ILN_230 | ||
| 912 | |a GBV_ILN_370 | ||
| 912 | |a GBV_ILN_602 | ||
| 912 | |a GBV_ILN_702 | ||
| 912 | |a GBV_ILN_2001 | ||
| 912 | |a GBV_ILN_2003 | ||
| 912 | |a GBV_ILN_2004 | ||
| 912 | |a GBV_ILN_2005 | ||
| 912 | |a GBV_ILN_2007 | ||
| 912 | |a GBV_ILN_2009 | ||
| 912 | |a GBV_ILN_2010 | ||
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| 912 | |a GBV_ILN_2014 | ||
| 912 | |a GBV_ILN_2015 | ||
| 912 | |a GBV_ILN_2020 | ||
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| 912 | |a GBV_ILN_2027 | ||
| 912 | |a GBV_ILN_2034 | ||
| 912 | |a GBV_ILN_2044 | ||
| 912 | |a GBV_ILN_2048 | ||
| 912 | |a GBV_ILN_2049 | ||
| 912 | |a GBV_ILN_2050 | ||
| 912 | |a GBV_ILN_2055 | ||
| 912 | |a GBV_ILN_2056 | ||
| 912 | |a GBV_ILN_2059 | ||
| 912 | |a GBV_ILN_2061 | ||
| 912 | |a GBV_ILN_2064 | ||
| 912 | |a GBV_ILN_2106 | ||
| 912 | |a GBV_ILN_2110 | ||
| 912 | |a GBV_ILN_2111 | ||
| 912 | |a GBV_ILN_2112 | ||
| 912 | |a GBV_ILN_2122 | ||
| 912 | |a GBV_ILN_2129 | ||
| 912 | |a GBV_ILN_2143 | ||
| 912 | |a GBV_ILN_2152 | ||
| 912 | |a GBV_ILN_2153 | ||
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| 912 | |a GBV_ILN_2507 | ||
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| 912 | |a GBV_ILN_4249 | ||
| 912 | |a GBV_ILN_4251 | ||
| 912 | |a GBV_ILN_4305 | ||
| 912 | |a GBV_ILN_4306 | ||
| 912 | |a GBV_ILN_4307 | ||
| 912 | |a GBV_ILN_4313 | ||
| 912 | |a GBV_ILN_4322 | ||
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| 912 | |a GBV_ILN_4324 | ||
| 912 | |a GBV_ILN_4326 | ||
| 912 | |a GBV_ILN_4333 | ||
| 912 | |a GBV_ILN_4334 | ||
| 912 | |a GBV_ILN_4338 | ||
| 912 | |a GBV_ILN_4393 | ||
| 912 | |a GBV_ILN_4700 | ||
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10.1016/j.jfranklin.2023.12.053 doi (DE-627)ELV06696945X (ELSEVIER)S0016-0032(23)00828-1 DE-627 ger DE-627 rda eng 510 VZ 31.80 bkl Hieu, Nguyen T. verfasserin aut Analyzing a class of stochastic SIRS models under imperfect vaccination 2023 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier This work is devoted to a class of stochastic infectious disease models, namely, stochastic SIRS models under imperfect vaccination. Because of the inclusion of the vaccination, the underlying models are more difficult to analyze than that of the previously considered in the literature. Our main effort is devoted to overcoming the difficulties and deriving a complete characterization of longtime behavior of systems. Numerical examples are provided to present computational evidence. These examples also provide insight that the discrete event process (the random switching) can reverse persistence to extinction, and vice versa. SIRS model Vaccination Persistence Extinction Regime-switching system Nguyen, Dang H. verfasserin aut Nguyen, Nhu N. verfasserin aut Yin, George verfasserin (orcid)0000-0002-2951-0704 aut Enthalten in Franklin Institute$gPhiladelphia, Pa. ; ID: gnd/38821-X Journal of the Franklin Institute Amsterdam [u.a.] : Elsevier Science, 1829 361, Seite 1284-1302 Online-Ressource (DE-627)320464954 (DE-600)2007754-3 (DE-576)259484628 1879-2693 nnns volume:361 pages:1284-1302 GBV_USEFLAG_U GBV_ELV SYSFLAG_U SSG-OPC-MAT GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2007 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2106 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 31.80 Angewandte Mathematik VZ AR 361 1284-1302 |
| spelling |
10.1016/j.jfranklin.2023.12.053 doi (DE-627)ELV06696945X (ELSEVIER)S0016-0032(23)00828-1 DE-627 ger DE-627 rda eng 510 VZ 31.80 bkl Hieu, Nguyen T. verfasserin aut Analyzing a class of stochastic SIRS models under imperfect vaccination 2023 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier This work is devoted to a class of stochastic infectious disease models, namely, stochastic SIRS models under imperfect vaccination. Because of the inclusion of the vaccination, the underlying models are more difficult to analyze than that of the previously considered in the literature. Our main effort is devoted to overcoming the difficulties and deriving a complete characterization of longtime behavior of systems. Numerical examples are provided to present computational evidence. These examples also provide insight that the discrete event process (the random switching) can reverse persistence to extinction, and vice versa. SIRS model Vaccination Persistence Extinction Regime-switching system Nguyen, Dang H. verfasserin aut Nguyen, Nhu N. verfasserin aut Yin, George verfasserin (orcid)0000-0002-2951-0704 aut Enthalten in Franklin Institute$gPhiladelphia, Pa. ; ID: gnd/38821-X Journal of the Franklin Institute Amsterdam [u.a.] : Elsevier Science, 1829 361, Seite 1284-1302 Online-Ressource (DE-627)320464954 (DE-600)2007754-3 (DE-576)259484628 1879-2693 nnns volume:361 pages:1284-1302 GBV_USEFLAG_U GBV_ELV SYSFLAG_U SSG-OPC-MAT GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2007 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2106 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 31.80 Angewandte Mathematik VZ AR 361 1284-1302 |
| allfields_unstemmed |
10.1016/j.jfranklin.2023.12.053 doi (DE-627)ELV06696945X (ELSEVIER)S0016-0032(23)00828-1 DE-627 ger DE-627 rda eng 510 VZ 31.80 bkl Hieu, Nguyen T. verfasserin aut Analyzing a class of stochastic SIRS models under imperfect vaccination 2023 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier This work is devoted to a class of stochastic infectious disease models, namely, stochastic SIRS models under imperfect vaccination. Because of the inclusion of the vaccination, the underlying models are more difficult to analyze than that of the previously considered in the literature. Our main effort is devoted to overcoming the difficulties and deriving a complete characterization of longtime behavior of systems. Numerical examples are provided to present computational evidence. These examples also provide insight that the discrete event process (the random switching) can reverse persistence to extinction, and vice versa. SIRS model Vaccination Persistence Extinction Regime-switching system Nguyen, Dang H. verfasserin aut Nguyen, Nhu N. verfasserin aut Yin, George verfasserin (orcid)0000-0002-2951-0704 aut Enthalten in Franklin Institute$gPhiladelphia, Pa. ; ID: gnd/38821-X Journal of the Franklin Institute Amsterdam [u.a.] : Elsevier Science, 1829 361, Seite 1284-1302 Online-Ressource (DE-627)320464954 (DE-600)2007754-3 (DE-576)259484628 1879-2693 nnns volume:361 pages:1284-1302 GBV_USEFLAG_U GBV_ELV SYSFLAG_U SSG-OPC-MAT GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2007 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2106 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 31.80 Angewandte Mathematik VZ AR 361 1284-1302 |
| allfieldsGer |
10.1016/j.jfranklin.2023.12.053 doi (DE-627)ELV06696945X (ELSEVIER)S0016-0032(23)00828-1 DE-627 ger DE-627 rda eng 510 VZ 31.80 bkl Hieu, Nguyen T. verfasserin aut Analyzing a class of stochastic SIRS models under imperfect vaccination 2023 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier This work is devoted to a class of stochastic infectious disease models, namely, stochastic SIRS models under imperfect vaccination. Because of the inclusion of the vaccination, the underlying models are more difficult to analyze than that of the previously considered in the literature. Our main effort is devoted to overcoming the difficulties and deriving a complete characterization of longtime behavior of systems. Numerical examples are provided to present computational evidence. These examples also provide insight that the discrete event process (the random switching) can reverse persistence to extinction, and vice versa. SIRS model Vaccination Persistence Extinction Regime-switching system Nguyen, Dang H. verfasserin aut Nguyen, Nhu N. verfasserin aut Yin, George verfasserin (orcid)0000-0002-2951-0704 aut Enthalten in Franklin Institute$gPhiladelphia, Pa. ; ID: gnd/38821-X Journal of the Franklin Institute Amsterdam [u.a.] : Elsevier Science, 1829 361, Seite 1284-1302 Online-Ressource (DE-627)320464954 (DE-600)2007754-3 (DE-576)259484628 1879-2693 nnns volume:361 pages:1284-1302 GBV_USEFLAG_U GBV_ELV SYSFLAG_U SSG-OPC-MAT GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2007 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2106 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 31.80 Angewandte Mathematik VZ AR 361 1284-1302 |
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analyzing a class of stochastic sirs models under imperfect vaccination |
| title_auth |
Analyzing a class of stochastic SIRS models under imperfect vaccination |
| abstract |
This work is devoted to a class of stochastic infectious disease models, namely, stochastic SIRS models under imperfect vaccination. Because of the inclusion of the vaccination, the underlying models are more difficult to analyze than that of the previously considered in the literature. Our main effort is devoted to overcoming the difficulties and deriving a complete characterization of longtime behavior of systems. Numerical examples are provided to present computational evidence. These examples also provide insight that the discrete event process (the random switching) can reverse persistence to extinction, and vice versa. |
| abstractGer |
This work is devoted to a class of stochastic infectious disease models, namely, stochastic SIRS models under imperfect vaccination. Because of the inclusion of the vaccination, the underlying models are more difficult to analyze than that of the previously considered in the literature. Our main effort is devoted to overcoming the difficulties and deriving a complete characterization of longtime behavior of systems. Numerical examples are provided to present computational evidence. These examples also provide insight that the discrete event process (the random switching) can reverse persistence to extinction, and vice versa. |
| abstract_unstemmed |
This work is devoted to a class of stochastic infectious disease models, namely, stochastic SIRS models under imperfect vaccination. Because of the inclusion of the vaccination, the underlying models are more difficult to analyze than that of the previously considered in the literature. Our main effort is devoted to overcoming the difficulties and deriving a complete characterization of longtime behavior of systems. Numerical examples are provided to present computational evidence. These examples also provide insight that the discrete event process (the random switching) can reverse persistence to extinction, and vice versa. |
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| title_short |
Analyzing a class of stochastic SIRS models under imperfect vaccination |
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