Simple epidemiology model for a non-immune disease with ordinary and resistant carriers
Електронний науковий архів Науково-технічної бібліотеки Національного університету "Львівська політехніка"
Переглянути архів Інформація| Поле | Співвідношення | |
| Title |
Simple epidemiology model for a non-immune disease with ordinary and resistant carriers
Проста епідеміологічна модель для неімунного захворювання із звичайними та резистентними збудниками |
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| Creator |
Ільницький, Г.
Ільницький, Я. Ilnytskyi, H. Ilnytskyi, J. |
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| Contributor |
Львівський національний медичний університет ім. Данила Галицького
Інститут фізики конденсованих систем НАН України Національний унівеpситет «Львівська політехніка» Danylo Halytskyi Lviv National Medical University Institute for Condensed Matter Physics of the Nat. Acad. Sci. of Ukraine Lviv Polytechnic National University |
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| Subject |
епідеміологія
резистентні збудники epidemiology resistant carriers 004.942 [616.9-022-021484 616-036.22] |
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| Description |
Запропоновано модель неiмунного захворювання, яке переноситься як звичайним, так i резистентним збудниками. Ефективнiсть поширення iнфекцiї β вважається одна- ковою для обох типiв збудникiв, тодi як ефективностi лiкування γ та γ′ вiдповiдно iнфiкованих звичайним та резистентним збудником вiдрiзняється. Конверсiя звичай- ного збудника у резистентний вiдбувається iз ефективнiстю δ. Проаналiзовано ста- цiонарнi стани моделi та здiйснено переформулювання фiксованих точок у термiнах двох параметрiв, що є комбiнацiєю початкових чотирьох ефективностей. Здiйснено оцiнку нижньої та верхньої меж цих параметрiв та побудовано тривимiрний графiк фiксованих точок. We consider the compartmental model for the non-immune disease with both ordinary and resistant carriers. The same infecting rate β is assumed for both types of carriers, whereas the curing rates γ and γ′ for the ordinary and resistant carriers, respectively, are different. The conversion from an ordinary into resistant carrier takes place with the rate δ. The stationary states for the model are evaluated and rewritten in a compact form using two reduced parameters that are combinations of initial four rates. The lower and upper bounds are given for both these parameters and the 3D plot for the fixed points is presented. |
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| Date |
2018-06-05T14:12:27Z
2018-06-05T14:12:27Z 2017-06-15 2017-06-15 |
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| Type |
Article
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| Identifier |
Ilnytskyi H. Simple epidemiology model for a non-immune disease with ordinary and resistant carriers / H. Ilnytskyi, J. Ilnytskyi // Mathematical Modeling and Computing. — Lviv : Lviv Politechnic Publishing House, 2017. — Vol 4. — No 1. — P. 37–42.
2312-9794 http://ena.lp.edu.ua:8080/handle/ntb/41470 Ilnytskyi H. Simple epidemiology model for a non-immune disease with ordinary and resistant carriers / H. Ilnytskyi, J. Ilnytskyi // Mathematical Modeling and Computing. — Lviv : Lviv Politechnic Publishing House, 2017. — Vol 4. — No 1. — P. 37–42. |
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| Language |
en
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| Relation |
Mathematical Modeling and Computing, 1 (4), 2017
[1] SchwaberM. J., Navon-Venezia S., KayeK. S., Ben-AmiR., SchwartzD., CarmeliY. Clinical and Economic Impact of Bacteremia with Extended-Spectrum-Lactamase-Producing Enterobacteriaceae, Antimicrobial Agents and Chemotherapy. 50, n. 4, 1257–1262 (2006). [2] Zapalac J. S., BillingsK.R., SchwadeN.D., RolandP. S. Suppurative Complications of Acute Otitis Media in the Era of Antibiotic Resistance. Archives of Otolaryngology–Head & Neck Surgery, 128, n. 6, 660–663 (2002). [3] RobertsR.R., HotaB., Ahmad I., ScottR.D. I., Foster S.D., Abbasi F., Schabowski S., Kampe L.M., CiavarellaG.G., SupinoM., Naples J., CordellR., Levy S.B., WeinsteinR.A. Hospital and Societal Costs of Antimicrobial-Resistant Infections in a Chicago Teaching Hospital: Implications for Antibiotic Stewardship. Clinical Infectious Diseases. 49, n. 8, 1175–1184 (2009). [4] FeshchenkoY., DzyublikA., PertsevaT., Bratus E., DzyublikY., GladkaG., Morrissey I., TorumkuneyD. Results from the Survey of Antibiotic Resistance (SOAR) 2011–13 in Ukraine. Journal of Antimicrobial Chemotherapy. 71, n. suppl 1, i63–i69 (2016). [5] CohenT., DyeC., ColijnC., WilliamsB., MurrayM. Mathematical models of the epidemiology and control of drug-resistant TB. Expert Review of Respiratory Medicine. 3, n. 1, 67–79 (2009). [6] NieuwhofG., Conington J., Bishop S.C. A genetic epidemiological model to describe resistance to an endemic bacterial disease in livestock: application to footrot in sheep. Genetics Selection Evolution. 41, n. 1, 19 (2009). [7] ZwerlingA., Shrestha S., DowdyD.W. MathematicalModelling and Tuberculosis: Advances in Diagnostics and Novel Therapies. Advances in Medicine. 2015, 1–10 (2015). [8] FofanaM.O., Shrestha S., KnightG.M., CohenT., WhiteR.G., Cobelens F., DowdyD.W. A Multistrain Mathematical Model To Investigate the Role of Pyrazinamide in the Emergence of Extensively Drug- Resistant Tuberculosis. Antimicrobial Agents and Chemotherapy. 61, n. 3, e00498–16 (2016). [9] Spicknall I.H., FoxmanB., MarrsC. F., Eisenberg J.N. S. A Modeling Framework for the Evolution and Spread of Antibiotic Resistance: Literature Review and Model Categorization. American Journal of Epidemiology. 178, n. 4, 508–520 (2013). [10] Feng Z. Applications of Epidemiological Models to Public Health Policymaking: The Role of Heterogeneity in Model Predictions. World Scientific Publishing Company (2014). [11] Ilnytskyi J., HolovatchY., KozitskyY., IlnytskyiH. Computer simulations of a stochastic model for the non-immune disease spread. Bulletin of the National University “Lviv Polytechnic”, 800, 176–184 (2014). [12] Ilnytskyi J., HaiduchokO., IlnytskyiH. Modelling of diaseses dissemination with multi-resistant pathogens. Computer technologies of Printing. 34, n. 2, 72–79 (2015), (in Ukrainian). [13] IlnytskyiH., Ilnytskyi J. Modelling of dynamics and clusterisation for the spread of the diseases with multidrug resistant carriers. Scientifically Capacitant Technologies. 28, n. 4, 296–3009 (2015), (in Ukrainian). [14] Ilnytskyi J., KozitskyY., IlnytskyiH., HaiduchokO. Stationary states and spatial patterning in anSISepidemiology model with implicit mobility. Physica A: Statistical Mechanics and its Applications. 461, 36–45 (2016). [1] SchwaberM. J., Navon-Venezia S., KayeK. S., Ben-AmiR., SchwartzD., CarmeliY. Clinical and Economic Impact of Bacteremia with Extended-Spectrum-Lactamase-Producing Enterobacteriaceae, Antimicrobial Agents and Chemotherapy. 50, n. 4, 1257–1262 (2006). [2] Zapalac J. S., BillingsK.R., SchwadeN.D., RolandP. S. Suppurative Complications of Acute Otitis Media in the Era of Antibiotic Resistance. Archives of Otolaryngology–Head & Neck Surgery, 128, n. 6, 660–663 (2002). [3] RobertsR.R., HotaB., Ahmad I., ScottR.D. I., Foster S.D., Abbasi F., Schabowski S., Kampe L.M., CiavarellaG.G., SupinoM., Naples J., CordellR., Levy S.B., WeinsteinR.A. Hospital and Societal Costs of Antimicrobial-Resistant Infections in a Chicago Teaching Hospital: Implications for Antibiotic Stewardship. Clinical Infectious Diseases. 49, n. 8, 1175–1184 (2009). [4] FeshchenkoY., DzyublikA., PertsevaT., Bratus E., DzyublikY., GladkaG., Morrissey I., TorumkuneyD. Results from the Survey of Antibiotic Resistance (SOAR) 2011–13 in Ukraine. Journal of Antimicrobial Chemotherapy. 71, n. suppl 1, i63–i69 (2016). [5] CohenT., DyeC., ColijnC., WilliamsB., MurrayM. Mathematical models of the epidemiology and control of drug-resistant TB. Expert Review of Respiratory Medicine. 3, n. 1, 67–79 (2009). [6] NieuwhofG., Conington J., Bishop S.C. A genetic epidemiological model to describe resistance to an endemic bacterial disease in livestock: application to footrot in sheep. Genetics Selection Evolution. 41, n. 1, 19 (2009). [7] ZwerlingA., Shrestha S., DowdyD.W. MathematicalModelling and Tuberculosis: Advances in Diagnostics and Novel Therapies. Advances in Medicine. 2015, 1–10 (2015). [8] FofanaM.O., Shrestha S., KnightG.M., CohenT., WhiteR.G., Cobelens F., DowdyD.W. A Multistrain Mathematical Model To Investigate the Role of Pyrazinamide in the Emergence of Extensively Drug- Resistant Tuberculosis. Antimicrobial Agents and Chemotherapy. 61, n. 3, e00498–16 (2016). [9] Spicknall I.H., FoxmanB., MarrsC. F., Eisenberg J.N. S. A Modeling Framework for the Evolution and Spread of Antibiotic Resistance: Literature Review and Model Categorization. American Journal of Epidemiology. 178, n. 4, 508–520 (2013). [10] Feng Z. Applications of Epidemiological Models to Public Health Policymaking: The Role of Heterogeneity in Model Predictions. World Scientific Publishing Company (2014). [11] Ilnytskyi J., HolovatchY., KozitskyY., IlnytskyiH. Computer simulations of a stochastic model for the non-immune disease spread. Bulletin of the National University "Lviv Polytechnic", 800, 176–184 (2014). [12] Ilnytskyi J., HaiduchokO., IlnytskyiH. Modelling of diaseses dissemination with multi-resistant pathogens. Computer technologies of Printing. 34, n. 2, 72–79 (2015), (in Ukrainian). [13] IlnytskyiH., Ilnytskyi J. Modelling of dynamics and clusterisation for the spread of the diseases with multidrug resistant carriers. Scientifically Capacitant Technologies. 28, n. 4, 296–3009 (2015), (in Ukrainian). [14] Ilnytskyi J., KozitskyY., IlnytskyiH., HaiduchokO. Stationary states and spatial patterning in anSISepidemiology model with implicit mobility. Physica A: Statistical Mechanics and its Applications. 461, 36–45 (2016). |
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| Rights |
© 2017 Lviv Polytechnic National University CMM IAPMM NASU
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| Format |
37-42
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| Coverage |
Lviv
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| Publisher |
Lviv Politechnic Publishing House
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