Current approaches to modeling of epidemic process of non-polio Enterovirus infections
Aim. To study mathematical models for predicting the incidence of non-polio enterovirus infections (NPEVI) in the Kemerovo Region.Materials and Methods. Here we conducted a retrospective epidemiological study of NPEVI incidence in the Kemerovo region from 2006 to 2021 (n = 2152 cases). Epidemic proc...
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Kemerovo State Medical University
2023-04-01
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| Series: | Фундаментальная и клиническая медицина |
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| Online Access: | https://fcm.kemsmu.ru/jour/article/view/675 |
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| author | M. V. Novoselova N. Yu. Potseluev E. B. Brusina |
| author_facet | M. V. Novoselova N. Yu. Potseluev E. B. Brusina |
| author_sort | M. V. Novoselova |
| collection | DOAJ |
| description | Aim. To study mathematical models for predicting the incidence of non-polio enterovirus infections (NPEVI) in the Kemerovo Region.Materials and Methods. Here we conducted a retrospective epidemiological study of NPEVI incidence in the Kemerovo region from 2006 to 2021 (n = 2152 cases). Epidemic process was studied using autocorrelation analysis, Fourier analysis, and neural networks using STATISTICA Automated Neural Networks (SANN) tool and StatTech v. 3.0.5.Results. The incidence rates of NPEVI were 9,39 per 100,000 population (2009), 15,78 per 100,000 population (2015) and 8,41 per 100,000 population (2019), exceeding the average median value (2006- 2021) by a factor of 2.4, 4.1, and 2.2, respectively. NPEVI incidence was largely determined by enteroviral meningitis. The majority of cases (89.94%) were registered in children. Notably, standard mathematical models failed to provide an objective analysis of the incidence trend. Autocorrelation analysis found the summer-autumn seasonality (August-October) by evaluating the ratio of actual data to 12-month rolling averages. Modeling of the epidemic process of NPEVI using neural networks highly likely predicted its incidence up to 52 months.Conclusion. The epidemic process of NPEVI in Kemerovo region has been characterized by a low intensity and summer-autumn seasonality. Neural networks are suggested as a promising tool to forecast the incidence of NPEVI. |
| format | Article |
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| institution | Matheson Library |
| issn | 2500-0764 2542-0941 |
| language | Russian |
| publishDate | 2023-04-01 |
| publisher | Kemerovo State Medical University |
| record_format | Article |
| series | Фундаментальная и клиническая медицина |
| spelling | doaj-art-a673b7f85c8b4ee6bcccfe3d03fe3bc42025-08-03T12:59:25ZrusKemerovo State Medical UniversityФундаментальная и клиническая медицина2500-07642542-09412023-04-0181435310.23946/2500-0764-2023-8-1-43-53352Current approaches to modeling of epidemic process of non-polio Enterovirus infectionsM. V. Novoselova0N. Yu. Potseluev1E. B. Brusina2Kemerovo State Medical UniversityAltai State Medical UniversityKemerovo State Medical UniversityAim. To study mathematical models for predicting the incidence of non-polio enterovirus infections (NPEVI) in the Kemerovo Region.Materials and Methods. Here we conducted a retrospective epidemiological study of NPEVI incidence in the Kemerovo region from 2006 to 2021 (n = 2152 cases). Epidemic process was studied using autocorrelation analysis, Fourier analysis, and neural networks using STATISTICA Automated Neural Networks (SANN) tool and StatTech v. 3.0.5.Results. The incidence rates of NPEVI were 9,39 per 100,000 population (2009), 15,78 per 100,000 population (2015) and 8,41 per 100,000 population (2019), exceeding the average median value (2006- 2021) by a factor of 2.4, 4.1, and 2.2, respectively. NPEVI incidence was largely determined by enteroviral meningitis. The majority of cases (89.94%) were registered in children. Notably, standard mathematical models failed to provide an objective analysis of the incidence trend. Autocorrelation analysis found the summer-autumn seasonality (August-October) by evaluating the ratio of actual data to 12-month rolling averages. Modeling of the epidemic process of NPEVI using neural networks highly likely predicted its incidence up to 52 months.Conclusion. The epidemic process of NPEVI in Kemerovo region has been characterized by a low intensity and summer-autumn seasonality. Neural networks are suggested as a promising tool to forecast the incidence of NPEVI.https://fcm.kemsmu.ru/jour/article/view/675non-polio enterovirus infectionsincidencemathematical modelingcyclicityseasonalityforecasting |
| spellingShingle | M. V. Novoselova N. Yu. Potseluev E. B. Brusina Current approaches to modeling of epidemic process of non-polio Enterovirus infections Фундаментальная и клиническая медицина non-polio enterovirus infections incidence mathematical modeling cyclicity seasonality forecasting |
| title | Current approaches to modeling of epidemic process of non-polio Enterovirus infections |
| title_full | Current approaches to modeling of epidemic process of non-polio Enterovirus infections |
| title_fullStr | Current approaches to modeling of epidemic process of non-polio Enterovirus infections |
| title_full_unstemmed | Current approaches to modeling of epidemic process of non-polio Enterovirus infections |
| title_short | Current approaches to modeling of epidemic process of non-polio Enterovirus infections |
| title_sort | current approaches to modeling of epidemic process of non polio enterovirus infections |
| topic | non-polio enterovirus infections incidence mathematical modeling cyclicity seasonality forecasting |
| url | https://fcm.kemsmu.ru/jour/article/view/675 |
| work_keys_str_mv | AT mvnovoselova currentapproachestomodelingofepidemicprocessofnonpolioenterovirusinfections AT nyupotseluev currentapproachestomodelingofepidemicprocessofnonpolioenterovirusinfections AT ebbrusina currentapproachestomodelingofepidemicprocessofnonpolioenterovirusinfections |