Creation of a computer model of influenza epidemics development on the basis of the statistical data laws analysis
DOI №______
Abstract
The success of medicine depends on the progress of information technology, in particular decision support systems that use computer models to predict the effects of medical management decisions. The purpose of the study is to identify the main dependencies of the development of influenza epidemics in Ukraine according to statistical data. The waves of the epidemic in the country as a whole are identified as the sum of individual waves in different cities. The qualitative similarity of the pattern of development of epidemics in different years was revealed. The physiological, weather and other factors of influence are analyzed.
The most appropriate S-shaped logistic model for the development of the epidemic is identified. The logistic dependence of the degree of adaptation of persons to new weather conditions is established. The Stype dependence of the growth of the anti-epidemic effect has been determined. Numerical simulation allows you to predict the level of the epidemic, depending on the value of the parameters. It is important to timely identify the moment of the beginning of the different stages of the epidemic and to quickly determine the peculiarities of the dynamics of the current epidemic in order to construct an optimal counteraction strategy. The simulation is done in the MatLab software environment. The transition to logistic ordinary differential equations in finite increments and the replacement of finite sum integration were performed. Modeling results are consistent with statistical data. The main practical benefit of simulation is the bell-like dependence of the number of infected persons. By the amplitude of this dependence determine the level of danger of the epidemic. The necessary and sufficient conditions for the emergence of the epidemic are identified.
Key words: model, forecast, management decision, epidemic, information technologies in medicine.
References (MLA)
1. Martcheva M. An Introduction to Mathematical Epidemiology. - New York: Springer Science + Business Media New York, 2015. www.springer.com/978-1-4899-7611-6. Web.
2. Maria do Rosario de Pinho, Philip Nunes Nogueira “On the Application of Optimal Control to SEIR Normalized Models: PROS and CONS.” Mathematical Biosciences and Engineering. V.14. ‒ No.1. (2017): 11-126. Print.
3. Boyev B.V. “Computer simulation in the assessment of the consequences of an act of biological terrorism.” Proceeding. 1-st Russian Symposium on Biological Safety. Moscow: Institute of Epidemiology and Microbiology named by N.F. Gamaleya RAMS. (2017). www.bio.su. Web.
4. Nofe Al-Asouad, Meir Shillor “Modeling, Analysis and Simulation of MERS Outbreak in Saudi Arabia.” Biomath. 7 1802277 (2017): 1-18. http://dx.doi.org/10.1145/j.biomath.2018.02.277.
5. Official website of the Ministry of Health of Ukraine. (2018) http://www.moz.gov.ua/ua/portal/op_flu_100525_0.html. Web.
6. Shevchenko V. L. Optimization Modeling in Strategic Planning. – Kyiv: CVSD NUOU, 2011. 283. Print.
7. Boyev B. V., Makarov V. V. "Geoinformation Systems and Epidemics of Influenza." Bulletin of the Russian University of Friendship of Peoples. Series: Agricultural Sciences. Animal husbandry. № 12 (2005): 6-15. Print.
8. Shevchenko A. V., Gepko A. L. "Mathematical Model of Forecasting of Epidemic Dynamics." Preventive medicine, 3(15) (2011): 3-6. Print.
9. Shevchenko V., Shevchenko A. “The Epidemiological Approach to Information Security Incident Forecasting for Decision Making Systems.” 13-th International Conference on Perspective Technologies and Methods in MEMS Design (MEMSTECH). Proceeding. - Polyana. (2017): 174177. Print.
10. Shevchenko V., Shcheblanin Ju., Shevchenko A. "The Epidemiological Approach to Prognosis and Management of Information Incidents." Science and Technology of the Air Forces of the Armed Forces of Ukraine. 4(29) (2017): 145-150. Print.