350 rub
Journal Biomedical Radioelectronics №6 for 2019 г.
Article in number:
Uncertainty of initial conditions in a SEIR-model with vaccination
Type of article: scientific article
DOI: 10.18127/j15604136-201906-07
UDC: 575+616+51
Authors:

V.V. Kotin – Ph.D. (Phys.-Math.), Associate Professor, Department “Medical and Technical Information Technology” (BMT-2),  Bauman Moscow State Technical University 

E-mail v.kotin@gmail.com

N.M. Chervyakov – Graduate Student, Department “Medical and Technical Information Technology” (BMT-2), Bauman Moscow State Technical University 

E-mail: nm.chervyakov@yandex.ru

Abstract:

Mathematical models of the morbidity dynamics (often called “epidemiological models”) are traditionally considered extremely important for solving the problems of predicting and controlling human infectious diseases.  The current urgency of this range of problems is due to such factors as large-scale migration of the population, the emergence of resistant pathogen strains and obviously growing need for an economic analysis of anti-epidemic procedures.

This paper analyzes the SEIR model of morbidity dynamics, taking into account migration and morbidity control effects (vaccination).  Feasible sets and an integral funnel* for the SEIR system are found to evaluate extremely accessible control possibilities. The influence of the model input data (initial conditions) uncertainty is considered to determine the effectiveness of vaccination in the presence of initial conditions and migration flows uncertainty. The results obtained form the basis for choosing the most effective way to use limited resources during vaccination and other anti-epidemic measures such as isolation, quarantine and preventive treatment.

Pages: 40-47
References
  1. https://www.who.int/csr/disease/ru/
  2. https://www.who.int/ru/news-room/fact-sheets/detail/measles 
  3. Anderson R., Mej R. Infekcionnye bolezni cheloveka. Dinamika i kontrol'. M.: Mir. Nauchnyj mir. 2004.784 s.
  4. Romanyuha A.A. Matematicheskie modeli v immunologii i epidemiologii infekcionnyh zabolevanij. M.: Binom. Laboratoriya znanij. 2012. 293 s.
  5. Martcheva M. An introduction to mathematical epidemiology // Springer science, Texts in applied mathematics. V. 61. New York. 2015. 
  6. Temime L. The rising impact of mathematical modelling in epidemiology: antibiotic resistance research as a case study // Epidemiology and infection. 2007. V. 136(3). P. 289–298.
  7. Castelli F., Sulis G. Migration and infectious diseases // Clinical Microbiology and Infection. 2017. V. 23. I. 5. P. 283–289.
  8. Gershovitz M., Hammer J. The economical control of infectious diseases // Public Service Delivery Development Research Group. 2001. № 11. 1–48.
  9. Perrings C., Castillo-Chavez C., Chowell G. et al. Merging economics and epidemiology to improve the prediction and managment of infectious disease // EcoHealth. 2014. V. 11. P. 464. https://doi.org/10.1007/s10393-014-0963-6.
  10. Liu X., Stechlinski P. Inectious Disease Modeling. A hybrid system approach // Springer Nature, Nonlinear systems and complexity. V.19. Cham. 2017.
  11. Udoy S. Basak, Bimal Kumar Datta, Prodip Kumar Ghose. Mathematical Analysis of an HIV/AIDS Epidemic Model // American Journal of Mathematics and Statistics. 2015. V. 5. № 5. P. 253–258. 
  12. Aron J.L. Multiple attractors in the response to a vaccination program // Theoretical Population Biology. 1990. V. 38. P. 58–67.
  13. CHernous'ko F.L. Ocenivanie fazovogo sostoyaniya dinamicheskih sistem. M.: Nauka. 1988. 
  14. Atkins T. Using modeling and simulation to evaluate disease control measures, [dissertation] / University of science at the university of central Florida publishing. Orlando. 2010.
  15. Rachah A., Torres D. F.M. Dynamics and Optimal Control of Ebola Transmission // Springer international publishing, Mathematics in computer science. Cham. 2016. P. 331–342.
  16. Křivan, V., Colombo, G. A non-stochastic approach for modeling uncertainty in population dynamics Bulletin of Mathematical Biology. July 1998. V. 60. Is. 4. P. 721–751.
  17. Desyatkov B.M., Lapteva N.A., SHabanov A.N. Opredelenie tochnosti matematicheskogo modelirovaniya harakteristik epidemii grippa // Epidemiologiya i vakcinoprofilaktika. 2013. № 2 (69). S. 33–39.
  18. Moneim I.A. Efficiency of different vaccination strategies for childhood diseases: A simulation study // Advances in Bioscience and Biotechnology. 2013. № 4. P. 193–205.
  19. Kotin V.V., Litun E.I., Litun S.I. Optimizaciya posledovatel'nogo rezhima vakcinacii i ocenka oblastej dostizhimosti // Biomedicinskaya radioelektronika. 2017. № 7. S. 29–34.
  20. Kotin V., Chervyakov N. Feasible sets for SEIR-model with control. Dvadcat' shestaya mezhdunarodnaya konferenciya «Matematika. Komp'yuter. Obrazovanie». Pushchino 28 yanvarya – 2 fevralya 2019 g. Tezisy // Pod red. G.YU. Riznichenko i A.B. Rubina. M.–Izhevsk: Institut komp'yuternyh issledovanij. S. 195.
  21. SHaripova E.V., Babchenko I.V. Klinicheskie rekomendacii (protokol lecheniya) okazaniya medicinskoj pomoshchi detyam, bol'nym kor'yu // FGBU NIIDI FMBA Rossii. 2015.
  22. Agur L. Cojocaru G. Mazor R.M. Anderson, Danon Y.L. Pulse mass measles vaccination across age cohorts // Proc. Natl. Acad. Sci. USA. December 1993. V. 90. P. 11698–11702.
  23. Shulgin B., Stone L., Agur Theoretical examination of pulse vaccination policy in the SIR epidemic model // Mathl. Comput. Modelling. 2000. V. 31 (4/5). P. 207–215.
  24. Wu Jianyong, Dhingra Radhika, Gambhir Manoj, Remais Justin V. Sensitivity analysis of infectious disease models: methods, advances and their appli-cation // J. R. Soc. Interface. 10. 20121018. http://doi.org/10.1098/rsif.2012.1018
Date of receipt: 10 октября 2019 г.