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Intelligent Data Analysis for Infection Spread Prediction

Author

Listed:
  • Alexey I. Borovkov

    (The World-Class Research Center “Advanced Digital Technologies”, Peter the Great St. Petersburg Polytechnic University, 195251 St. Petersburg, Russia)

  • Marina V. Bolsunovskaya

    (The World-Class Research Center “Advanced Digital Technologies”, Peter the Great St. Petersburg Polytechnic University, 195251 St. Petersburg, Russia)

  • Aleksei M. Gintciak

    (The World-Class Research Center “Advanced Digital Technologies”, Peter the Great St. Petersburg Polytechnic University, 195251 St. Petersburg, Russia)

Abstract

Intelligent data analysis based on artificial intelligence and Big Data tools is widely used by the scientific community to overcome global challenges. One of these challenges is the worldwide coronavirus pandemic, which began in early 2020. Data science not only provides an opportunity to assess the impact caused by a pandemic, but also to predict the infection spread. In addition, the model expansion by economic, social, and infrastructural factors makes it possible to predict changes in all spheres of human activity in competitive epidemiological conditions. This article is devoted to the use of anonymized and personal data in predicting the coronavirus infection spread. The basic “Susceptible–Exposed–Infected–Recovered” model was extended by including a set of demographic, administrative, and social factors. The developed model is more predictive and applicable in assessing future pandemic impact. After a series of simulation experiment results, we concluded that personal data use in high-level modeling of the infection spread is excessive.

Suggested Citation

  • Alexey I. Borovkov & Marina V. Bolsunovskaya & Aleksei M. Gintciak, 2022. "Intelligent Data Analysis for Infection Spread Prediction," Sustainability, MDPI, vol. 14(4), pages 1-11, February.
    • Handle: RePEc:gam:jsusta:v:14:y:2022:i:4:p:1995-:d:745945
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    References listed on IDEAS

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