| Autor: |
Uvaliyeva, Indira, Belginova, Saule, Sovetbekov, Sanzhar |
| Předmět: |
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| Zdroj: |
Scientific Journal of Astana IT University; Dec2022, Vol. 12, p89-100, 12p |
| Abstrakt: |
The COVID-19 epidemic has gone down in history as an emergency of international importance. Currently, the number of people infected with coronavirus around the world continues to grow, and modeling such a complex system as the spread of infection is one of the most pressing problems. Various models are used to understand the progress of the COVID-19 coronavirus epidemic and to plan effective control strategies. Such models require the use of advanced computing, such as artificial intelligence, machine learning, cloud computing, and edge computing. This article uses the SIR mathematical model, which is often used and simple to model the prevalence of COVID-19 infection. The SIR model can provide a theoretical basis for studying the prevalence of the COVID-19 virus in a specific population and an understanding of the temporal evolution of the virus. One of the main advantages of this model is the ease of adjusting the sampling parameters as the study scale increases and the most appropriate graphs between the data and the resulting assumptions. Computer models based on the mathematical SIR model of the spread of the COVID-19 epidemic make it possible to estimate the number of possible deaths in the future. In addition, on the basis of the proposed models, it will be possible to assess the effectiveness of measures taken to prevent infection by comparing published data with forecasts. Computer models in Python are created on the basis of the proposed mathematical apparatus of SIR. The following libraries were added in the Python high-level programming language for the numerical solution of the system of differential equations for the SIR model: NumPy, Matplotlib PyPlot and the Integrate module from the SciPy library. [ABSTRACT FROM AUTHOR] |
| Databáze: |
Complementary Index |
| Externí odkaz: |
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