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A REAL TURKEY DATA APPLICATION TO THE SEIR MODEL FOR TUBERCULOSIS DISEASE

Yıl 2025, Cilt: 11 Sayı: 2, 66 - 75, 31.12.2025
https://doi.org/10.22531/muglajsci.1705165

Öz

The SEIR mathematical model in the literature is applied to analyze the tuberculosis transmission dynamics in Turkey. It consists of four classes: susceptible (S), exposed (E), infected (I) and recovered (R). The equilibrium points and the basic reproduction number of the SEIR model are given. The structural identifiability analysis of the model is conducted in the Maple program and also with the differential algebra method. As a result of the structural identifiability analysis, all parameters in the model are found as locally structurally identifiable, but aren’t found to be globally identifiable. In addition, if an additional parameter is known, all other parameters are obtained to be globally identifiable. The number of tuberculosis cases in Turkey from 2005 to 2016 is used to solve the parameter estimation problem. The practical identifiability of the model is examined using Monte Carlo simulations. Sensitivity analysis of the model is also studied. Matlab program is used to estimate the model parameters and perform practical identifiability analysis. It is concluded that three parameters μ,β and γ, except for the parameter ε, are all practically identifiable. As a result, the basic reproduction number calculated with the obtained values shows that tuberculosis disease will continue in Turkey.

Kaynakça

  • Tüberküloz Tanı ve Tedavi Rehberi, T.C. Sağlık Bakanlığı, 862, Ankara, 2011. Access Date: 08/12/2023: https://toraks.org.tr/site/sf/documents/pre_migration/0843354699a1757b76dde91155e96a9f72d0604ec89c5ed967e4db07dc77ad02.pdf
  • Tüberküloz Tanı ve Tedavi Rehberi, T.C. Sağlık Bakanlığı, 1129, 2. Baskı Ankara, 2019. Access Date: 29/10/2022: https://hsgm.saglik.gov.tr/depo/birimler/tuberkuloz-db/Dokumanlar/Rehberler/Tuberkuloz_Tani_ve_Tedavi_Rehberi.pdf
  • White, P. J. ve Garnett, G. P., “Mathematical modelling of the epidemiology of tuberculosis”, Modelling parasite transmission and control, 127-140, 2010.
  • Kermack, W. O. ve McKendrick, A. G., “A contribution to the mathematical theory of epidemics” Proceedings of the royal society of London, Series A, Containing papers of a mathematical and physical character, Vol. 115(772), 700-721, 1927.
  • M'kendrick, A. G., “Applications of mathematics to medical problems”, Proceedings of the Edinburgh Mathematical Society, 44, 98-130, 1925.
  • Frost, W. H., “How much control of tuberculosis?” American Journal of Public Health and the Nations Health, Vol. 27(8), 759-766, 1937.
  • Side, S., “A susceptible-infected-recovered model and simulation for transmission of tuberculosis”, Advanced Science Letters, Vol.21(2), 137-139, 2015.
  • Kasereka Kabunga, S., Doungmo Goufo, E. F. ve Ho Tuong, V., “Analysis and simulation of a mathematical model of tuberculosis transmission in Democratic Republic of the Congo” Advances in Difference Equations, Vol. 2020(1), 642, 2020.
  • Mettle, F. O., Osei Affi, P. ve Twumasi, C. “Modelling the transmission dynamics of tuberculosis in the ashanti region of ghana”, Interdisciplinary Perspectives on Infectious Diseases, Vol. 2020(1), 4513854, 2020.
  • Das, K., Murthy, B. S. N., Samad, S. A. ve Biswas, M. H. A., “Mathematical transmission analysis of SEIR tuberculosis disease model”, Sensors International, Vol. 2, 100120, 2021.
  • Li, Y., Liu, X., Yuan, Y., Li, J. ve Wang, L., “Global analysis of tuberculosis dynamical model and optimal control strategies based on case data in the United States”, Applied Mathematics and Computation, Vol. 422, 126983, 2022.
  • Waaler, H. T. ve Piot, M. A., “The use of an epidemiological model for estimating the effectiveness of tuberculosis control measures: sensitivity of the effectiveness of tuberculosis control measures to the coverage of the population”, Bulletin of the World Health Organization, Vol. 41(1), 75-93, 1969.
  • Waaler, H. T. ve Piot, M. A., “Use of an epidemiological model for estimating the effectiveness of tuberculosis control measures: sensitivity of the effectiveness of tuberculosis control measures to the social time preference”, Bulletin of the World Health Organization, Vol. 43(1), 1-16, 1970.
  • Zhang, J., Li, Y. ve Zhang, X., “Mathematical modeling of tuberculosis data of China”, Journal of theoretical biology, Vol. 365, 159-163, 2015.
  • Tamhaji, N. H. ve Hamdan, N. I., “The Dynamics of Tuberculosis through BSEIR Model with Immigration in Malaysia”, Malaysian Journal of Fundamental and Applied Sciences, Vol. 19(6), 1176-1189, 2023.
  • Dontwi, I. K., Obeng-Denteh, W., Andam, E. A. ve Obiri-Apraku, L., “A mathematical model to predict the prevalence and transmission dynamics of tuberculosis in amansie west district, Ghana”, British Journal of Mathematics & Computer Science, Vol. 4(3), 402-425, 2014.
  • Ergen, K., Çilli, A. ve Yahnıoğlu, N., “Predicting epidemic diseases using mathematical modelling of SIR”, Acta Physica Polonica A, Vol. 128(2B), B273-B275, 2015.
  • Ucakan, Y., Gulen, S. ve Koklu, K., “Analysing of tuberculosis in Turkey through SIR, SEIR and BSEIR mathematical models”, Mathematical and Computer Modelling of Dynamical Systems, Vol. 27(1), 179-202, 2021.
  • Chowell, G., Dahal, S., Liyanage, Y. R., Tariq, A., ve Tuncer, N., “Structural identifiability analysis of epidemic models based on differential equations: a tutorial-based primer”, Journal of mathematical biology, Vol. 87(6), 79, 2023.
  • Wieland, F. G., Hauber, A. L., Rosenblatt, M., Tönsing, C. ve Timmer, J., “On structural and practical identifiability”, Current Opinion in Systems Biology, Vol. 25, 60-69, 2021.
  • Yılmaz A., COVID -19 bulaşıcı hastalığının Türkiye’deki yayılmasının matematiksel modellenmesi, Yüksek Lisans Tezi, Bilecik Şeyh Edebali Üniversitesi Fen Bilimleri Enstitüsü, Matematik Anabilim Dalı, Bilecik, 49s, 2022.
  • Martcheva, M., An introduction to mathematical epidemiology, Springer, New York, 2015.
  • Diekmann O., Heesterbeek J. A. P. ve Metz J. A. J., “On the definition and computation of the basic reproduction ratio ℛ0 in models for infectious diseases in heterogeneous populations”, J. Math. Biol. Vol. 28, 365-382, 1990.
  • Diekmann, O. ve Heesterbeek, J. A. P., Mathematical epidemiology of infectious diseases: model building, analysis and interpretation, John Wiley & Sons, 2000.
  • Hong, H., Ovchinnikov, A., Pogudin, G. ve Yap, C., “SIAN: software for structural identifiability analysis of ODE models”, Bioinformatics, Vol. 35(16), 2873-2874, 2019.
  • Muñoz-Tamayo, R. ve Tedeschi, L. O., “ASAS-NANP symposium: mathematical modeling in animal nutrition: the power of identifiability analysis for dynamic modeling in animal science: a practitioner approach”, Journal of Animal Science, Vol. 101, skad320, 2023.
  • Tuncer, N., Marctheva, M., LaBarre, B. ve Payoute, S., “Structural and practical identifiability analysis of Zika epidemiological models”, Bulletin of mathematical biology, Vol. 80, 2209-2241, 2018.
  • Miao, H., Xia, X., Perelson, A. S. ve Wu, H., “On identifiability of nonlinear ODE models and applications in viral dynamics”, SIAM review, Vol. 53(1), 3-39, 2011.
  • Chis, O. T., Banga, J. R. ve Balsa-Canto, E., “Structural identifiability of systems biology models: a critical comparison of methods”, PloS one, Vol. 6(11), e27755, 2011.
  • Sedoglavic, A., “A probabilistic algorithm to test local algebraic observability in polynomial time” In Proceedings of the 2001 international symposium on Symbolic and algebraic computation, 2001, 309-317.
  • Karlsson, J., Nyberg, M., Saccomani, M.P. ve Jirstrand, M., “An efficient method for structural identifiability analysis of large dynamic systems”, IFAC proceedings volumes, Vol.45(16),941-946, 2012.
  • Villaverde, A.F., Barreiro, A. ve Papachristodoulou A., “Structural identifiability of dynamic systems biology models”, PLoS Comput Biol., Vol.12:10, e1005153, 2016.
  • Thomaseth, K. ve Saccomani, M. P., “Local identifiability analysis of nonlinear ODE models: how to determine all candidate solutions”, IFAC-Papers OnLine, Vol.51(2), 529-534, 2018.
  • Norton, J. P., “An investigation of the sources of nonuniqueness in deterministic identifiability” Mathematical Biosciences, Vol. 60(1), 89-108, 1982.
  • Bellu, G., Saccomani, M. P., Audoly, S. ve D'Angiò, L., “DAISY: A new software tool to test global identifiability of biological and physiological systems”, Computer methods and programs in biomedicine, Vol. 88(1), 52-61, 2007.
  • Meshkat, N., Kuo, C. E. Z. ve DiStefano III, J., “On finding and using identifiable parameter combinations in nonlinear dynamic systems biology models and COMBOS: a novel web implementation”, PloS one, Vol. 9(10), e110261, 2014.
  • Ligon, T. S., Fröhlich, F., Chiş, O. T., Banga, J. R., Balsa-Canto, E. ve Hasenauer, J., “GenSSI 2.0: multi-experiment structural identifiability analysis of SBML models”, Bioinformatics, Vol. 34(8), 1421-1423, 2018.
  • Mufutau, R. A. ve Akinpelu, F., “Sensitivity Analysis of Mathematical Modelling of Tuberculosis Disease With Resistance to Drug Treatments”, International Journal of Mathematical Sciences and Optimization: Theory and Applications, Vol. 6(2), 940-955, 2020.
  • Kara, F., Kabasakal, E., Yıldırım, A., Mutlu, S.M. ve Baykal. F., “Türkiye’de Verem Savaşı 2018 Raporu”, HSGM Tüberküloz Dairesi Başkanlığı, 1109, Ankara-2018. Access Date: 22/12/2021: https://hsgm.saglik.gov.tr/depo/birimler/tuberkuloz-db/Dokumanlar/Raporlar/Tu_rkiye_de_Verem_Savas_2018_Raporu_kapakl_.pdf
  • Banks, H. T., Hu, S. ve Thompson, W. C., Modeling and inverse problems in the presence of uncertainty. CRC Press, USA, 2014.
  • Türkiye İstatistik Kurumu web sitesi, Hayat Tabloları. Access Date: 27/09/2024: https://data.tuik.gov.tr/Bulten/Index?p=Hayat-Tablolari-2021-2023-53678
  • Isik, O. R., Tuncer, N. ve Martcheva, M., “Mathematical model of measles in Turkey”, Journal of Biological Systems, Vol. 32(03), 941-970, 2024.
  • Isik, O. R., Tuncer, N. ve Martcheva, M., “A mathematical model for the role of vaccination and treatment in measles transmission in Turkey”, Journal of Computational and Applied Mathematics, Vol. 457, 116308, 2025.
  • Marceddu, G., Kalluci, E., Noka, E., Gordani, O., Macchia, A., Bertelli, M. ve Merkaj, Z., “The application of next generation matrix in the calculation of basic reproduction number for COVID-19”, La Clinica Terapeutica, 174(6), 2023.
  • Chitnis, N., Hyman, J. M. ve Cushing, J. M., “Determining important parameters in the spread of malaria through the sensitivity analysis of a mathematical model”, Bulletin of mathematical biology, 70, 1272-1296, 2008.
  • Hines, K. E., Middendorf, T. R. ve Aldrich, R. W., “Determination of parameter identifiability in nonlinear biophysical models: A Bayesian approach”, Journal of General Physiology, 143(3), 401-416, 2014.
  • Saccomani, M. P. ve Thomaseth, K., “The union between structural and practical identifiability makes strength in reducing oncological model complexity: a case study”. Complexity, Vol. 2018(1), 2380650, 2018.
  • Lam, N.N., Docherty, P.D. ve Murray, R., “Practical identifiability of parametrised models: A review of benefits and limitations of various approaches”, Math Comput Simul, Vol. 199, 202-16, 2022.
  • Tuncer, N., Gulbudak, H., Cannataro, V.L. ve Martcheva, M., “Structural and practical identifiability issues of immuno-epidemiological vector--host models with application to rift valley fever”, Bull Math Biol., Vol. 78,1796-827, 2016.

TÜBERKÜLOZ HASTALIĞI İÇİN SEIR MODELİNE GERÇEK TÜRKİYE VERİ UYGULAMASI

Yıl 2025, Cilt: 11 Sayı: 2, 66 - 75, 31.12.2025
https://doi.org/10.22531/muglajsci.1705165

Öz

Türkiye'de tüberküloz iletim dinamiklerini belirlemek için literatürdeki SEIR matematiksel model uygulanmıştır. SEIR matematiksel model, duyarlı (S), maruz kalmış (E), enfekte (I) ve iyileşmiş (R) olmak üzere dört sınıftan oluşmaktadır. SEIR matematiksel modelin denge noktaları ve temel üreme sayısı verilmiştir. Modelin yapısal tanımlanabilirlik analizi Maple programında ve ayrıca diferansiyel cebir yöntemi ile belirlenmiştir. Yapısal tanımlanabilirlik analizi sonucunda, modeldeki tüm parametreler local yapısal tanımlanabilir olarak bulunmuş, fakat, global tanımlanabilir bulunmamıştır. Ek olarak, ek bir parametrenin bilinmesi durumunda tüm diğer parametreler global tanımlanabilir olarak elde edilmiştir. Parametre tahmin probleminin çözümü için Türkiye’de 2005’den 2016 yılına kadar tüberküloz vaka sayıları kullanılmıştır. Modelin pratik tanımlanabilirliği Monte Carlo simülasyonları kullanılarak incelenmiştir. Modelin duyarlılık analizi de incelenmiştir. Modelin parametre tahmini ve pratik tanımlanabilirlik analizi için Matlab programı kullanılmıştır. ε parametresi hariç μ, β ve γ parametrelerinin hepsi pratik tanımlanabilir olduğu sonucuna ulaşılmıştır. Sonuç olarak, elde edilen değerlerle hesaplanan temel üreme sayısı Türkiye'de tüberküloz hastalığının devam edeceğini göstermektedir.

Kaynakça

  • Tüberküloz Tanı ve Tedavi Rehberi, T.C. Sağlık Bakanlığı, 862, Ankara, 2011. Access Date: 08/12/2023: https://toraks.org.tr/site/sf/documents/pre_migration/0843354699a1757b76dde91155e96a9f72d0604ec89c5ed967e4db07dc77ad02.pdf
  • Tüberküloz Tanı ve Tedavi Rehberi, T.C. Sağlık Bakanlığı, 1129, 2. Baskı Ankara, 2019. Access Date: 29/10/2022: https://hsgm.saglik.gov.tr/depo/birimler/tuberkuloz-db/Dokumanlar/Rehberler/Tuberkuloz_Tani_ve_Tedavi_Rehberi.pdf
  • White, P. J. ve Garnett, G. P., “Mathematical modelling of the epidemiology of tuberculosis”, Modelling parasite transmission and control, 127-140, 2010.
  • Kermack, W. O. ve McKendrick, A. G., “A contribution to the mathematical theory of epidemics” Proceedings of the royal society of London, Series A, Containing papers of a mathematical and physical character, Vol. 115(772), 700-721, 1927.
  • M'kendrick, A. G., “Applications of mathematics to medical problems”, Proceedings of the Edinburgh Mathematical Society, 44, 98-130, 1925.
  • Frost, W. H., “How much control of tuberculosis?” American Journal of Public Health and the Nations Health, Vol. 27(8), 759-766, 1937.
  • Side, S., “A susceptible-infected-recovered model and simulation for transmission of tuberculosis”, Advanced Science Letters, Vol.21(2), 137-139, 2015.
  • Kasereka Kabunga, S., Doungmo Goufo, E. F. ve Ho Tuong, V., “Analysis and simulation of a mathematical model of tuberculosis transmission in Democratic Republic of the Congo” Advances in Difference Equations, Vol. 2020(1), 642, 2020.
  • Mettle, F. O., Osei Affi, P. ve Twumasi, C. “Modelling the transmission dynamics of tuberculosis in the ashanti region of ghana”, Interdisciplinary Perspectives on Infectious Diseases, Vol. 2020(1), 4513854, 2020.
  • Das, K., Murthy, B. S. N., Samad, S. A. ve Biswas, M. H. A., “Mathematical transmission analysis of SEIR tuberculosis disease model”, Sensors International, Vol. 2, 100120, 2021.
  • Li, Y., Liu, X., Yuan, Y., Li, J. ve Wang, L., “Global analysis of tuberculosis dynamical model and optimal control strategies based on case data in the United States”, Applied Mathematics and Computation, Vol. 422, 126983, 2022.
  • Waaler, H. T. ve Piot, M. A., “The use of an epidemiological model for estimating the effectiveness of tuberculosis control measures: sensitivity of the effectiveness of tuberculosis control measures to the coverage of the population”, Bulletin of the World Health Organization, Vol. 41(1), 75-93, 1969.
  • Waaler, H. T. ve Piot, M. A., “Use of an epidemiological model for estimating the effectiveness of tuberculosis control measures: sensitivity of the effectiveness of tuberculosis control measures to the social time preference”, Bulletin of the World Health Organization, Vol. 43(1), 1-16, 1970.
  • Zhang, J., Li, Y. ve Zhang, X., “Mathematical modeling of tuberculosis data of China”, Journal of theoretical biology, Vol. 365, 159-163, 2015.
  • Tamhaji, N. H. ve Hamdan, N. I., “The Dynamics of Tuberculosis through BSEIR Model with Immigration in Malaysia”, Malaysian Journal of Fundamental and Applied Sciences, Vol. 19(6), 1176-1189, 2023.
  • Dontwi, I. K., Obeng-Denteh, W., Andam, E. A. ve Obiri-Apraku, L., “A mathematical model to predict the prevalence and transmission dynamics of tuberculosis in amansie west district, Ghana”, British Journal of Mathematics & Computer Science, Vol. 4(3), 402-425, 2014.
  • Ergen, K., Çilli, A. ve Yahnıoğlu, N., “Predicting epidemic diseases using mathematical modelling of SIR”, Acta Physica Polonica A, Vol. 128(2B), B273-B275, 2015.
  • Ucakan, Y., Gulen, S. ve Koklu, K., “Analysing of tuberculosis in Turkey through SIR, SEIR and BSEIR mathematical models”, Mathematical and Computer Modelling of Dynamical Systems, Vol. 27(1), 179-202, 2021.
  • Chowell, G., Dahal, S., Liyanage, Y. R., Tariq, A., ve Tuncer, N., “Structural identifiability analysis of epidemic models based on differential equations: a tutorial-based primer”, Journal of mathematical biology, Vol. 87(6), 79, 2023.
  • Wieland, F. G., Hauber, A. L., Rosenblatt, M., Tönsing, C. ve Timmer, J., “On structural and practical identifiability”, Current Opinion in Systems Biology, Vol. 25, 60-69, 2021.
  • Yılmaz A., COVID -19 bulaşıcı hastalığının Türkiye’deki yayılmasının matematiksel modellenmesi, Yüksek Lisans Tezi, Bilecik Şeyh Edebali Üniversitesi Fen Bilimleri Enstitüsü, Matematik Anabilim Dalı, Bilecik, 49s, 2022.
  • Martcheva, M., An introduction to mathematical epidemiology, Springer, New York, 2015.
  • Diekmann O., Heesterbeek J. A. P. ve Metz J. A. J., “On the definition and computation of the basic reproduction ratio ℛ0 in models for infectious diseases in heterogeneous populations”, J. Math. Biol. Vol. 28, 365-382, 1990.
  • Diekmann, O. ve Heesterbeek, J. A. P., Mathematical epidemiology of infectious diseases: model building, analysis and interpretation, John Wiley & Sons, 2000.
  • Hong, H., Ovchinnikov, A., Pogudin, G. ve Yap, C., “SIAN: software for structural identifiability analysis of ODE models”, Bioinformatics, Vol. 35(16), 2873-2874, 2019.
  • Muñoz-Tamayo, R. ve Tedeschi, L. O., “ASAS-NANP symposium: mathematical modeling in animal nutrition: the power of identifiability analysis for dynamic modeling in animal science: a practitioner approach”, Journal of Animal Science, Vol. 101, skad320, 2023.
  • Tuncer, N., Marctheva, M., LaBarre, B. ve Payoute, S., “Structural and practical identifiability analysis of Zika epidemiological models”, Bulletin of mathematical biology, Vol. 80, 2209-2241, 2018.
  • Miao, H., Xia, X., Perelson, A. S. ve Wu, H., “On identifiability of nonlinear ODE models and applications in viral dynamics”, SIAM review, Vol. 53(1), 3-39, 2011.
  • Chis, O. T., Banga, J. R. ve Balsa-Canto, E., “Structural identifiability of systems biology models: a critical comparison of methods”, PloS one, Vol. 6(11), e27755, 2011.
  • Sedoglavic, A., “A probabilistic algorithm to test local algebraic observability in polynomial time” In Proceedings of the 2001 international symposium on Symbolic and algebraic computation, 2001, 309-317.
  • Karlsson, J., Nyberg, M., Saccomani, M.P. ve Jirstrand, M., “An efficient method for structural identifiability analysis of large dynamic systems”, IFAC proceedings volumes, Vol.45(16),941-946, 2012.
  • Villaverde, A.F., Barreiro, A. ve Papachristodoulou A., “Structural identifiability of dynamic systems biology models”, PLoS Comput Biol., Vol.12:10, e1005153, 2016.
  • Thomaseth, K. ve Saccomani, M. P., “Local identifiability analysis of nonlinear ODE models: how to determine all candidate solutions”, IFAC-Papers OnLine, Vol.51(2), 529-534, 2018.
  • Norton, J. P., “An investigation of the sources of nonuniqueness in deterministic identifiability” Mathematical Biosciences, Vol. 60(1), 89-108, 1982.
  • Bellu, G., Saccomani, M. P., Audoly, S. ve D'Angiò, L., “DAISY: A new software tool to test global identifiability of biological and physiological systems”, Computer methods and programs in biomedicine, Vol. 88(1), 52-61, 2007.
  • Meshkat, N., Kuo, C. E. Z. ve DiStefano III, J., “On finding and using identifiable parameter combinations in nonlinear dynamic systems biology models and COMBOS: a novel web implementation”, PloS one, Vol. 9(10), e110261, 2014.
  • Ligon, T. S., Fröhlich, F., Chiş, O. T., Banga, J. R., Balsa-Canto, E. ve Hasenauer, J., “GenSSI 2.0: multi-experiment structural identifiability analysis of SBML models”, Bioinformatics, Vol. 34(8), 1421-1423, 2018.
  • Mufutau, R. A. ve Akinpelu, F., “Sensitivity Analysis of Mathematical Modelling of Tuberculosis Disease With Resistance to Drug Treatments”, International Journal of Mathematical Sciences and Optimization: Theory and Applications, Vol. 6(2), 940-955, 2020.
  • Kara, F., Kabasakal, E., Yıldırım, A., Mutlu, S.M. ve Baykal. F., “Türkiye’de Verem Savaşı 2018 Raporu”, HSGM Tüberküloz Dairesi Başkanlığı, 1109, Ankara-2018. Access Date: 22/12/2021: https://hsgm.saglik.gov.tr/depo/birimler/tuberkuloz-db/Dokumanlar/Raporlar/Tu_rkiye_de_Verem_Savas_2018_Raporu_kapakl_.pdf
  • Banks, H. T., Hu, S. ve Thompson, W. C., Modeling and inverse problems in the presence of uncertainty. CRC Press, USA, 2014.
  • Türkiye İstatistik Kurumu web sitesi, Hayat Tabloları. Access Date: 27/09/2024: https://data.tuik.gov.tr/Bulten/Index?p=Hayat-Tablolari-2021-2023-53678
  • Isik, O. R., Tuncer, N. ve Martcheva, M., “Mathematical model of measles in Turkey”, Journal of Biological Systems, Vol. 32(03), 941-970, 2024.
  • Isik, O. R., Tuncer, N. ve Martcheva, M., “A mathematical model for the role of vaccination and treatment in measles transmission in Turkey”, Journal of Computational and Applied Mathematics, Vol. 457, 116308, 2025.
  • Marceddu, G., Kalluci, E., Noka, E., Gordani, O., Macchia, A., Bertelli, M. ve Merkaj, Z., “The application of next generation matrix in the calculation of basic reproduction number for COVID-19”, La Clinica Terapeutica, 174(6), 2023.
  • Chitnis, N., Hyman, J. M. ve Cushing, J. M., “Determining important parameters in the spread of malaria through the sensitivity analysis of a mathematical model”, Bulletin of mathematical biology, 70, 1272-1296, 2008.
  • Hines, K. E., Middendorf, T. R. ve Aldrich, R. W., “Determination of parameter identifiability in nonlinear biophysical models: A Bayesian approach”, Journal of General Physiology, 143(3), 401-416, 2014.
  • Saccomani, M. P. ve Thomaseth, K., “The union between structural and practical identifiability makes strength in reducing oncological model complexity: a case study”. Complexity, Vol. 2018(1), 2380650, 2018.
  • Lam, N.N., Docherty, P.D. ve Murray, R., “Practical identifiability of parametrised models: A review of benefits and limitations of various approaches”, Math Comput Simul, Vol. 199, 202-16, 2022.
  • Tuncer, N., Gulbudak, H., Cannataro, V.L. ve Martcheva, M., “Structural and practical identifiability issues of immuno-epidemiological vector--host models with application to rift valley fever”, Bull Math Biol., Vol. 78,1796-827, 2016.
Toplam 49 adet kaynakça vardır.

Ayrıntılar

Birincil Dil İngilizce
Konular Biyolojik Matematik
Bölüm Araştırma Makalesi
Yazarlar

Sevim Dolaşır 0000-0002-4970-7571

Osman Raşit Işık 0000-0003-1401-4553

Gönderilme Tarihi 23 Mayıs 2025
Kabul Tarihi 4 Kasım 2025
Yayımlanma Tarihi 31 Aralık 2025
Yayımlandığı Sayı Yıl 2025 Cilt: 11 Sayı: 2

Kaynak Göster

APA Dolaşır, S., & Işık, O. R. (2025). A REAL TURKEY DATA APPLICATION TO THE SEIR MODEL FOR TUBERCULOSIS DISEASE. Mugla Journal of Science and Technology, 11(2), 66-75. https://doi.org/10.22531/muglajsci.1705165
AMA 1.Dolaşır S, Işık OR. A REAL TURKEY DATA APPLICATION TO THE SEIR MODEL FOR TUBERCULOSIS DISEASE. MJST. 2025;11(2):66-75. doi:10.22531/muglajsci.1705165
Chicago Dolaşır, Sevim, ve Osman Raşit Işık. 2025. “A REAL TURKEY DATA APPLICATION TO THE SEIR MODEL FOR TUBERCULOSIS DISEASE”. Mugla Journal of Science and Technology 11 (2): 66-75. https://doi.org/10.22531/muglajsci.1705165.
EndNote Dolaşır S, Işık OR (01 Aralık 2025) A REAL TURKEY DATA APPLICATION TO THE SEIR MODEL FOR TUBERCULOSIS DISEASE. Mugla Journal of Science and Technology 11 2 66–75.
IEEE [1]S. Dolaşır ve O. R. Işık, “A REAL TURKEY DATA APPLICATION TO THE SEIR MODEL FOR TUBERCULOSIS DISEASE”, MJST, c. 11, sy 2, ss. 66–75, Ara. 2025, doi: 10.22531/muglajsci.1705165.
ISNAD Dolaşır, Sevim - Işık, Osman Raşit. “A REAL TURKEY DATA APPLICATION TO THE SEIR MODEL FOR TUBERCULOSIS DISEASE”. Mugla Journal of Science and Technology 11/2 (01 Aralık 2025): 66-75. https://doi.org/10.22531/muglajsci.1705165.
JAMA 1.Dolaşır S, Işık OR. A REAL TURKEY DATA APPLICATION TO THE SEIR MODEL FOR TUBERCULOSIS DISEASE. MJST. 2025;11:66–75.
MLA Dolaşır, Sevim, ve Osman Raşit Işık. “A REAL TURKEY DATA APPLICATION TO THE SEIR MODEL FOR TUBERCULOSIS DISEASE”. Mugla Journal of Science and Technology, c. 11, sy 2, Aralık 2025, ss. 66-75, doi:10.22531/muglajsci.1705165.
Vancouver 1.Dolaşır S, Işık OR. A REAL TURKEY DATA APPLICATION TO THE SEIR MODEL FOR TUBERCULOSIS DISEASE. MJST [Internet]. 01 Aralık 2025;11(2):66-75. Erişim adresi: https://izlik.org/JA33LT99XN

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