Modeling the dynamics of population immunity to influenza in Russian cities

Vasiliy N Leonenko,Daria M Danilenko,V Volpert,F Syomin

doi:10.1051/itmconf/20203103001

Vasiliy N Leonenko, Daria M Danilenko + Show 2 more

Open Access

https://doi.org/10.1051/itmconf/20203103001

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Abstract

Prediction models which will explicitly include the immunity levels of the population are required to plan effective measures for the containment of seasonal epidemics of influenza. The aim of the current work is to develop an approach to herd immunity dynamics modeling, with the long–term goal of employing it as a part of multicomponent model of influenza incidence dynamics. Based on serological studies performed for 52 Russian cities and 2 virus strains (A(H1N1)pdm09, A(H3N2)) in 11 years period, we propose statistical models which allow to analyze and predict the strain–specific immunity dynamics.

Highlights

Epidemic outbreaks of influenza, along with the corresponding increase in the incidence of acute respiratory infections (ARI), cause 3-5 million cases of severe illness and 250-500 thousand deaths annually, according to WHO estimates [1]
In vast majority of works devoted to the modeling of influenza epidemics, the dynamics of the population immunity levels from season to season is either added as an abstract parameter of the model, without its direct assessment from serological studies, — sometimes due to the lack of the corresponding data, — or it is not considered at all
The aim of the presented research is to develop an approach to modeling and forecasting of collective immunity dynamics in urban populations based on the data obtained from biological samples

Summary

Introduction

Along with the corresponding increase in the incidence of acute respiratory infections (ARI), cause 3-5 million cases of severe illness and 250-500 thousand deaths annually, according to WHO estimates [1]. In vast majority of works devoted to the modeling of influenza epidemics, the dynamics of the population immunity levels from season to season is either added as an abstract parameter of the model, without its direct assessment from serological studies, — sometimes due to the lack of the corresponding data, — or it is not considered at all.

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