Short-term prediction of the development of the epidemic of a new coronavirus infection in different phases of the epidemic process
- Autores: Makhova V.V.1, Ploskireva A.A.2, Maletskaya O.V.1, Kovalchuk I.V.3, Kulichenko A.N.1
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Afiliações:
- Stavropol Research Anti-Plague Institute of Russian Federal Service for Supervision of Consumer Rights Protection and Human Well-Being
- Central Research Institute of Epidemiology of Russian Federal Service for Supervision of Consumer Rights Protection and Human Well-Being
- Department of Russian Federal Service for Supervision of Consumer Rights Protection and Human Well-Being for the Stavropol Territory
- Edição: Volume 13, Nº 4 (2023)
- Páginas: 7-13
- Seção: Epidemic Situation
- URL: https://journals.eco-vector.com/2226-6976/article/view/625508
- DOI: https://doi.org/10.18565/epidem.2023.13.4.7-13
- ID: 625508
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Resumo
Objective. Testing an original method for short-term prediction of the epidemiological situation for COVID-19 using the example of the Stavropol Territory.
Materials and methods. We used data from the Department of Russian Federal Service for Supervision of Consumer Rights Protection and Human Well-Being in the Stavropol Territory, the «Center for Hygiene and Epidemiology in the Stavropol Territory» on cases of COVID-19 from March 20, 2020 to August 1, 2022, as well as the results of molecular genetic monitoring of fragmentary and whole-genome sequencing of clinical material from patients COVID-19 in the Stavropol Territory, received at the Stavropol Anti-Plague Institute of Russian Federal Service for Supervision of Consumer Rights Protection and Human Well-Being. We used the original short-term prediction method proposed by A.A. Ploskireva.
Results. The results of a retrospective analysis of the epidemic situation regarding COVID-19 in the Stavropol Territory in four periods (21.09–04.10.2020; 08–21.04.2021; 28.09–11.10.2021 и 01–14.04.2022) justified the median prediction scenario, in one (10–23.02.2022) – optimistic prediction scenario (P > 0.05). However, during the period of change from the SARS-CoV-2 «India» strain Delta B.1.617.2 to Omicron B.1.1.529, against the background of an increase in the number of vaccinated people, none of the prediction scenarios came true – the incidence during this period was lower than the pessimistic scenario.
Conclusion. The predicting technique can be used not only to predict a pandemic of a new coronavirus infection, but also to control and assess the spread of diseases from the group of new infections at different stages of the epidemic process in the short term.
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Sobre autores
Valentina Makhova
Stavropol Research Anti-Plague Institute of Russian Federal Service for Supervision of Consumer Rights Protection and Human Well-Being
Autor responsável pela correspondência
Email: dr.makhova@yandex.ru
ORCID ID: 0000-0003-2988-3559
Junior Researcher, Laboratory of Epidemiology
Rússia, StavropolAntonina Ploskireva
Central Research Institute of Epidemiology of Russian Federal Service for Supervision of Consumer Rights Protection and Human Well-Being
Email: antoninna@mail.ru
ORCID ID: 0000-0002-3612-1889
MD, Professor, Deputy Director for Clinical Work
Rússia, MoscowOlga Maletskaya
Stavropol Research Anti-Plague Institute of Russian Federal Service for Supervision of Consumer Rights Protection and Human Well-Being
Email: maletskaya_ov@mail.ru
ORCID ID: 0000-0002-3003-4952
MD, Professor, Deputy Director for Scientific and Anti-epidemic Work
Rússia, StavropolIrina Kovalchuk
Department of Russian Federal Service for Supervision of Consumer Rights Protection and Human Well-Being for the Stavropol Territory
Email: kovalchuk_iv@26.rospotrebnadzor.ru
Cand. Med. Sci., Deputy Head
Rússia, StavropolAleksandr Kulichenko
Stavropol Research Anti-Plague Institute of Russian Federal Service for Supervision of Consumer Rights Protection and Human Well-Being
Email: kulichenko_an@list.ru
ORCID ID: 0000-0002-9362-3949
MD, Professor, Director
Rússia, StavropolBibliografia
- Кутырев В.В., Попова А.Ю., Смоленский В.Ю., Ежлова Е.Б., Демина Ю.В., Сафронов В.А. и др. Эпидемиологические особенности новой коронавирусной инфекции (COVID-19). Сообщение 1. Модели реализации профилактических и противоэпидемических мероприятий. Проблемы особо опасных инфекций 2020; (1): 6–13. doi: 10.21055/0370- 1069-2020-1-6-13. Kutyrev V.V., Popova A.Yu., Smolensky V.Yu., Ezhlova E.B., Demina Yu.V., Safronov V.A. et al. [Epidemiological features of the new coronavirus infection (COVID-19). Message 1. Models for the implementation of preventive and anti-epidemic measures]. Problems of Particularly Dangerous Infections 2020; (1): 6–13. (In Russ.). doi: 10.21055/0370-1069-2020-1-6-13
- WHO Coronavirus (COVID-19) Dashboard. https://covid19.who.int/
- Временные методические рекомендации «Профилактика, диагностика и лечение новой коронавирусной инфекции (COVID 19)». Версия 11. https://base.garant.ru/400738625/ [Interim guidelines on Prevention «Prevention, Diagnosis and Treatment of Novel Coronavirus Infection (COVID-19)». Version 11]. (In Russ.). https:// base.garant.ru/400738625/
- Huang C., Wang Y., Li X., Ren L., Zhao J., Hu Y. et al. Clinical features of patients infected with 2019 novel coronavirus in Wuhan, China. Lancet 2020; 395(10223): 497–506. https://doi.org/10.1016/S0140-6736(20)30183-5
- Onder G., Rezza G., Brusaferro S. Case-fatality rate and characteristics of patients dying in relation to COVID-19 in Italy. JAMA 2020; 323(18): 1775–6. https://doi.org/10.1001/jama.2020.4683
- Mehta P., McAuley D.F., Brown M., Sanchez E., Tattersall R., Manson J.J. et al. COVID-19: consider cytokine storm syndromes and immunosuppression. Lancet 2020; 395(10229): 1033–4. https://doi.org/10.1016/S0140-6736(20)30628-0
- Hollander J.E., Carr B.G. Virtually perfect? Telemedicine for COVID-19. N. Engl. J. Med. 2020; 382(18): 1679–81. https://doi.org/10.1056/NEJMp 2003539.
- Гаврилов Д.В., Абрамов Р.В., Кирилкина А.В., Ившин А.А., Новицкий Р.Э. Модель прогнозирования пандемии COVID-19 на основе машинного обучения в отдельных регионах Российской Федерации. Фармакоэкономика. Современная фармакоэкономика и фармакоэпидемиология 2021; 14(3): 342–56. https://doi.org/10. 17749/2070-4909/farmakoekonomika.2021.108. Gavrilov D.V., Abramov R.V., Kirilkina A.V., Ivshin A.A., Novitsky R.E. [A model for predicting the COVID-19 pandemic based on machine learning in certain regions of the Russian Federation]. Pharmacoeconomics. Modern pharmacoeconomics and pharmacoepidemiology 2021; 14(3): 342–56. (In Russ.). https://doi.org/10.17749/2070-4909/farmakoekonomika. 2021.1081
- Антонец Д.В., Плоскирева А.А., Поспелов М.В., Смоленский В.Ю. Комплексный анализ и прогноз развития эпидемиологической ситуации по COVID-19 в Российской Федерации (2020-08-31). doi: 10.21055/preprints-3111803. Antonets D.V., Ploskireva A.A., Pospelov M.V., Smolensky V. Yu. [Comprehensive analysis and forecast of the development of the epidemiological situation on COVID-19 in the Russian Federation (2020-08-31)]. (In Russ.). doi: 10.21055/preprints-3111803.
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