SIR+A mathematical model for evaluating and predicting 2016–2017 ARVI-influenza incidence by using on the Moscow territory

Influenza is a major challenge to global healthcare due to its high transmissivity and ability to cause major epidemics. Influenza epidemics and pandemics are associated with changes in the society structure that contribute to the spread of new viral strains in certain environmental and social setti...

Descripción completa

Guardado en:

Detalles Bibliográficos
Autores principales:	N. A. Kontarov, G. V. Arkharova, Yu. B. Grishunina, S. A. Grishunina, N. V. Yuminova
Formato:	article
Lenguaje:	RU
Publicado:	Sankt-Peterburg : NIIÈM imeni Pastera 2019
Materias:	assessment prediction disease arvi influenza mathematical model sir+а Infectious and parasitic diseases RC109-216
Acceso en línea:	https://doaj.org/article/0395ba27bda141b7852ff05315cd9e0b
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

id	oai:doaj.org-article:0395ba27bda141b7852ff05315cd9e0b
record_format	dspace
spelling	oai:doaj.org-article:0395ba27bda141b7852ff05315cd9e0b2021-11-22T07:09:53ZSIR+A mathematical model for evaluating and predicting 2016–2017 ARVI-influenza incidence by using on the Moscow territory2220-76192313-739810.15789/2220-7619-2019-3-4-583-588https://doaj.org/article/0395ba27bda141b7852ff05315cd9e0b2019-11-01T00:00:00Zhttps://www.iimmun.ru/iimm/article/view/1159https://doaj.org/toc/2220-7619https://doaj.org/toc/2313-7398Influenza is a major challenge to global healthcare due to its high transmissivity and ability to cause major epidemics. Influenza epidemics and pandemics are associated with changes in the society structure that contribute to the spread of new viral strains in certain environmental and social settings. Currently, influenza is one of the most common global diseases that results in annual epidemics or even pandemics, often leading to lethal outcome. Influenza viruses are uniquely prone to variability via point mutations, recombination and gene reassortment accompanied with changes in their biological properties considered as the main cause of uncontrolled infection spread. Hence, examining cohorts of predisposed individuals by using probability models provides not only additional information about viral outbreaks, but also allows monitoring dynamics of viral epidemics in controlled areas. Understanding influenza epidemiology is crucial for restructuring healthcare resources. Public healthcare service mainly relies on influenza vaccination. However, there are vulnerable cohorts such as elderly and immunocompromised individuals, which usually contain no protective antiinfluenza virus antibody level. Despite advances in the developing vaccines and chemotherapy, large-scale influenza epidemics still continue to emerge. Upon that, no reliable methods for disease prognosis based on rate of ongoing epidemic situation are currently available. Monitoring and predicting emerging epidemics is complicated due to discrepancy between dynamics of influenza epidemics that might be evaluated by using surveillance data as well as platform for tracking influenza incidence rate. However, it may be profoundly exacerbated by mutations found in the influenza virus genome by altering genuine morbidity dynamics. Use of probabilistic models for assessing parameters of stochastic epidemics would contribute to more accurately predicted changes in morbidity rate. Here, an SIR+A probabilistic model considering a relationship between infected, susceptible and protected individuals as well as the aggressiveness of external risks for predicting changes in influenza morbidity rate that allowed to evaluate and predict the 2016 ARVI influenza incidence rate in Moscow area. Moreover, introducing an intensity of infection parameter allows to conduct a reliable analysis of incidence rate and predict its changes.N. A. KontarovG. V. ArkharovaYu. B. GrishuninaS. A. GrishuninaN. V. YuminovaSankt-Peterburg : NIIÈM imeni Pasteraarticleassessmentpredictiondiseasearviinfluenzamathematical modelsir+аInfectious and parasitic diseasesRC109-216RUInfekciâ i Immunitet, Vol 9, Iss 3-4, Pp 583-588 (2019)
institution	DOAJ
collection	DOAJ
language	RU
topic	assessment prediction disease arvi influenza mathematical model sir+а Infectious and parasitic diseases RC109-216
spellingShingle	assessment prediction disease arvi influenza mathematical model sir+а Infectious and parasitic diseases RC109-216 N. A. Kontarov G. V. Arkharova Yu. B. Grishunina S. A. Grishunina N. V. Yuminova SIR+A mathematical model for evaluating and predicting 2016–2017 ARVI-influenza incidence by using on the Moscow territory
description	Influenza is a major challenge to global healthcare due to its high transmissivity and ability to cause major epidemics. Influenza epidemics and pandemics are associated with changes in the society structure that contribute to the spread of new viral strains in certain environmental and social settings. Currently, influenza is one of the most common global diseases that results in annual epidemics or even pandemics, often leading to lethal outcome. Influenza viruses are uniquely prone to variability via point mutations, recombination and gene reassortment accompanied with changes in their biological properties considered as the main cause of uncontrolled infection spread. Hence, examining cohorts of predisposed individuals by using probability models provides not only additional information about viral outbreaks, but also allows monitoring dynamics of viral epidemics in controlled areas. Understanding influenza epidemiology is crucial for restructuring healthcare resources. Public healthcare service mainly relies on influenza vaccination. However, there are vulnerable cohorts such as elderly and immunocompromised individuals, which usually contain no protective antiinfluenza virus antibody level. Despite advances in the developing vaccines and chemotherapy, large-scale influenza epidemics still continue to emerge. Upon that, no reliable methods for disease prognosis based on rate of ongoing epidemic situation are currently available. Monitoring and predicting emerging epidemics is complicated due to discrepancy between dynamics of influenza epidemics that might be evaluated by using surveillance data as well as platform for tracking influenza incidence rate. However, it may be profoundly exacerbated by mutations found in the influenza virus genome by altering genuine morbidity dynamics. Use of probabilistic models for assessing parameters of stochastic epidemics would contribute to more accurately predicted changes in morbidity rate. Here, an SIR+A probabilistic model considering a relationship between infected, susceptible and protected individuals as well as the aggressiveness of external risks for predicting changes in influenza morbidity rate that allowed to evaluate and predict the 2016 ARVI influenza incidence rate in Moscow area. Moreover, introducing an intensity of infection parameter allows to conduct a reliable analysis of incidence rate and predict its changes.
format	article
author	N. A. Kontarov G. V. Arkharova Yu. B. Grishunina S. A. Grishunina N. V. Yuminova
author_facet	N. A. Kontarov G. V. Arkharova Yu. B. Grishunina S. A. Grishunina N. V. Yuminova
author_sort	N. A. Kontarov
title	SIR+A mathematical model for evaluating and predicting 2016–2017 ARVI-influenza incidence by using on the Moscow territory
title_short	SIR+A mathematical model for evaluating and predicting 2016–2017 ARVI-influenza incidence by using on the Moscow territory
title_full	SIR+A mathematical model for evaluating and predicting 2016–2017 ARVI-influenza incidence by using on the Moscow territory
title_fullStr	SIR+A mathematical model for evaluating and predicting 2016–2017 ARVI-influenza incidence by using on the Moscow territory
title_full_unstemmed	SIR+A mathematical model for evaluating and predicting 2016–2017 ARVI-influenza incidence by using on the Moscow territory
title_sort	sir+a mathematical model for evaluating and predicting 2016–2017 arvi-influenza incidence by using on the moscow territory
publisher	Sankt-Peterburg : NIIÈM imeni Pastera
publishDate	2019
url	https://doaj.org/article/0395ba27bda141b7852ff05315cd9e0b
work_keys_str_mv	AT nakontarov siramathematicalmodelforevaluatingandpredicting20162017arviinfluenzaincidencebyusingonthemoscowterritory AT gvarkharova siramathematicalmodelforevaluatingandpredicting20162017arviinfluenzaincidencebyusingonthemoscowterritory AT yubgrishunina siramathematicalmodelforevaluatingandpredicting20162017arviinfluenzaincidencebyusingonthemoscowterritory AT sagrishunina siramathematicalmodelforevaluatingandpredicting20162017arviinfluenzaincidencebyusingonthemoscowterritory AT nvyuminova siramathematicalmodelforevaluatingandpredicting20162017arviinfluenzaincidencebyusingonthemoscowterritory
_version_	1718417895586791424

SIR+A mathematical model for evaluating and predicting 2016–2017 ARVI-influenza incidence by using on the Moscow territory

Ejemplares similares