TY - JOUR

T1 - Delay-differential SEIR modeling for improved modelling of infection dynamics

AU - Киселев, Илья

AU - Акбердин, Илья Ринатович

AU - Колпаков, Федор

N1 - The study was supported by the RFBR (#20-04-60355 and by the Ministry of Science and Higher Education of the Russian Federation (Agreement 075-10-2021-093, Project CMB-RND-2123). © 2023. Springer Nature Limited.

PY - 2023/8/18

Y1 - 2023/8/18

N2 - SEIR (Susceptible–Exposed–Infected–Recovered) approach is a classic modeling method that is frequently used to study infectious diseases. However, in the vast majority of such models transitions from one population group to another are described using the mass-action law. That causes inability to reproduce observable dynamics of an infection such as the incubation period or progression of the disease's symptoms. In this paper, we propose a new approach to simulate the epidemic dynamics based on a system of differential equations with time delays and instant transitions to approximate durations of transition processes more correctly and make model parameters more clear. The suggested approach can be applied not only to Covid-19 but also to the study of other infectious diseases. We utilized it in the development of the delay-based model of the COVID-19 pandemic in Germany and France. The model takes into account testing of different population groups, symptoms progression from mild to critical, vaccination, duration of protective immunity and new virus strains. The stringency index was used as a generalized characteristic of the non-pharmaceutical government interventions in corresponding countries to contain the virus spread. The parameter identifiability analysis demonstrated that the presented modeling approach enables to significantly reduce the number of parameters and make them more identifiable. Both models are publicly available.

AB - SEIR (Susceptible–Exposed–Infected–Recovered) approach is a classic modeling method that is frequently used to study infectious diseases. However, in the vast majority of such models transitions from one population group to another are described using the mass-action law. That causes inability to reproduce observable dynamics of an infection such as the incubation period or progression of the disease's symptoms. In this paper, we propose a new approach to simulate the epidemic dynamics based on a system of differential equations with time delays and instant transitions to approximate durations of transition processes more correctly and make model parameters more clear. The suggested approach can be applied not only to Covid-19 but also to the study of other infectious diseases. We utilized it in the development of the delay-based model of the COVID-19 pandemic in Germany and France. The model takes into account testing of different population groups, symptoms progression from mild to critical, vaccination, duration of protective immunity and new virus strains. The stringency index was used as a generalized characteristic of the non-pharmaceutical government interventions in corresponding countries to contain the virus spread. The parameter identifiability analysis demonstrated that the presented modeling approach enables to significantly reduce the number of parameters and make them more identifiable. Both models are publicly available.

KW - Humans

KW - COVID-19/epidemiology

KW - Pandemics/prevention & control

KW - France

KW - Germany

KW - Communicable Diseases/epidemiology

UR - https://www.scopus.com/record/display.uri?eid=2-s2.0-85168327479&origin=inward&txGid=82d6b7cfbd51c379a8f7763067e061b2

UR - https://www.mendeley.com/catalogue/8d04316d-3c1e-3bfb-8336-8b80855fb359/

U2 - 10.1038/s41598-023-40008-9

DO - 10.1038/s41598-023-40008-9

M3 - Article

C2 - 37596296

VL - 13

JO - Scientific Reports

JF - Scientific Reports

SN - 2045-2322

IS - 1

M1 - 13439

ER -