Predictive algorithm for the regional spread of coronavirus infection across the Russian Federation
Abstract Background Outbreaks of infectious diseases are a complex phenomenon with many interacting factors. Regional health authorities need prognostic modeling of the epidemic process. Methods For these purposes, various mathematical algorithms can be used, which are a useful tool for studying the...
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| Format: | Article |
| Language: | English |
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BMC
2023-03-01
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| Series: | BMC Medical Informatics and Decision Making |
| Subjects: | |
| Online Access: | https://doi.org/10.1186/s12911-023-02135-1 |
| _version_ | 1797864529493753856 |
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| author | Andrey Reshetnikov Vitalii Berdutin Alexander Zaporozhtsev Sergey Romanov Olga Abaeva Nadezhda Prisyazhnaya Nadezhda Vyatkina |
| author_facet | Andrey Reshetnikov Vitalii Berdutin Alexander Zaporozhtsev Sergey Romanov Olga Abaeva Nadezhda Prisyazhnaya Nadezhda Vyatkina |
| author_sort | Andrey Reshetnikov |
| collection | DOAJ |
| description | Abstract Background Outbreaks of infectious diseases are a complex phenomenon with many interacting factors. Regional health authorities need prognostic modeling of the epidemic process. Methods For these purposes, various mathematical algorithms can be used, which are a useful tool for studying the infections spread dynamics. Epidemiological models act as evaluation and prognosis models. The authors outlined the experience of developing a short-term predictive algorithm for the spread of the COVID-19 in the region of the Russian Federation based on the SIR model: Susceptible (vulnerable), Infected (infected), Recovered (recovered). The article describes in detail the methodology of a short-term predictive algorithm, including an assessment of the possibility of building a predictive model and the mathematical aspects of creating such forecast algorithms. Results Findings show that the predicted results (the mean square of the relative error of the number of infected and those who had recovered) were in agreement with the real-life situation: σ(I) = 0.0129 and σ(R) = 0.0058, respectively. Conclusions The present study shows that despite a large number of sophisticated modifications, each of which finds its scope, it is advisable to use a simple SIR model to quickly predict the spread of coronavirus infection. Its lower accuracy is fully compensated by the adaptive calibration of parameters based on monitoring the current situation with updating indicators in real-time. |
| first_indexed | 2024-04-09T22:53:24Z |
| format | Article |
| id | doaj.art-e916b1d177cb4c939558374f3d6bc3a9 |
| institution | Directory Open Access Journal |
| issn | 1472-6947 |
| language | English |
| last_indexed | 2024-04-09T22:53:24Z |
| publishDate | 2023-03-01 |
| publisher | BMC |
| record_format | Article |
| series | BMC Medical Informatics and Decision Making |
| spelling | doaj.art-e916b1d177cb4c939558374f3d6bc3a92023-03-22T11:31:33ZengBMCBMC Medical Informatics and Decision Making1472-69472023-03-0123111910.1186/s12911-023-02135-1Predictive algorithm for the regional spread of coronavirus infection across the Russian FederationAndrey Reshetnikov0Vitalii Berdutin1Alexander Zaporozhtsev2Sergey Romanov3Olga Abaeva4Nadezhda Prisyazhnaya5Nadezhda Vyatkina6Institute of Social Sciences, Sechenov First Moscow State Medical UniversityContract Department, Federal Budgetary Institution of Healthcare “Volga District Medical Center of the Federal Medical and Biological Agency”Department of Theoretical and Applied Mechanics, Federal State Budgetary Educational Institution of Higher Education “Nizhny Novgorod State Technical University Named After R.E. Alekseev”Department of Sociology of Medicine, Health Economics, and Health Insurance, Sechenov First Moscow State Medical UniversityDepartment of Sociology of Medicine, Health Economics, and Health Insurance, Sechenov First Moscow State Medical UniversityInstitute of Social Sciences, Sechenov First Moscow State Medical UniversityInstitute of Social Sciences, Sechenov First Moscow State Medical UniversityAbstract Background Outbreaks of infectious diseases are a complex phenomenon with many interacting factors. Regional health authorities need prognostic modeling of the epidemic process. Methods For these purposes, various mathematical algorithms can be used, which are a useful tool for studying the infections spread dynamics. Epidemiological models act as evaluation and prognosis models. The authors outlined the experience of developing a short-term predictive algorithm for the spread of the COVID-19 in the region of the Russian Federation based on the SIR model: Susceptible (vulnerable), Infected (infected), Recovered (recovered). The article describes in detail the methodology of a short-term predictive algorithm, including an assessment of the possibility of building a predictive model and the mathematical aspects of creating such forecast algorithms. Results Findings show that the predicted results (the mean square of the relative error of the number of infected and those who had recovered) were in agreement with the real-life situation: σ(I) = 0.0129 and σ(R) = 0.0058, respectively. Conclusions The present study shows that despite a large number of sophisticated modifications, each of which finds its scope, it is advisable to use a simple SIR model to quickly predict the spread of coronavirus infection. Its lower accuracy is fully compensated by the adaptive calibration of parameters based on monitoring the current situation with updating indicators in real-time.https://doi.org/10.1186/s12911-023-02135-1COVID-19Dynamics of viral diseaseEpidemiological modelPredictive algorithm |
| spellingShingle | Andrey Reshetnikov Vitalii Berdutin Alexander Zaporozhtsev Sergey Romanov Olga Abaeva Nadezhda Prisyazhnaya Nadezhda Vyatkina Predictive algorithm for the regional spread of coronavirus infection across the Russian Federation BMC Medical Informatics and Decision Making COVID-19 Dynamics of viral disease Epidemiological model Predictive algorithm |
| title | Predictive algorithm for the regional spread of coronavirus infection across the Russian Federation |
| title_full | Predictive algorithm for the regional spread of coronavirus infection across the Russian Federation |
| title_fullStr | Predictive algorithm for the regional spread of coronavirus infection across the Russian Federation |
| title_full_unstemmed | Predictive algorithm for the regional spread of coronavirus infection across the Russian Federation |
| title_short | Predictive algorithm for the regional spread of coronavirus infection across the Russian Federation |
| title_sort | predictive algorithm for the regional spread of coronavirus infection across the russian federation |
| topic | COVID-19 Dynamics of viral disease Epidemiological model Predictive algorithm |
| url | https://doi.org/10.1186/s12911-023-02135-1 |
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