ABSTRACT
To study the characteristics of the spread of the COVID-19 pandemic and introduce timely and effective measures, there is a need for models that can predict the impact of various restrictive factors on COVID-19 disease dynamics. In this regard, it seems expedient to employ agent-based models that can take into account various characteristics of the population (for example, age distribution and social activity) and restrictive measures, testing, etc., as well as random factors that are usually omitted in traditionally used modifications of Susceptible-Infected-Recovered (SIR) type models. This paper presents the development of the previously proposed agent model for numerical simulation of the spread of COVID-19, namely, the transition from a single-center model, in which all agents interact within one common pool, to a multi-center model, in which the agents under consideration are distributed over several centers of interactions, and are also redistributed over time to other pools. This model allows us to more accurately simulate the epidemic dynamic within one region, when the patient zero usually arrives at the regional center, after which the distribution chains capture the periphery of the region due to pendulum migration. This paper demonstrates the application of the developed model to analyze the epidemic spread in the Nizhny Novgorod region of Russian Federation. Simulated dynamics of the daily number of newly detected cases and COVID-19-associated deaths is in good agreement with official statistics. Modeling results suggest that the actual number of COVID-19 cases is 1.5–3 times higher than the number of reported cases. The developed model also takes into account the process of vaccination. It is shown that with the same modeling parameters, but without vaccination, the third and fourth waves of the pandemic would be characterized by a significant increase in the incidence and the formation of natural immunity, but the number of deaths would exceed the real one by about 9 times. © 2023 Journal of Biomedical Photonics & Engineering.
ABSTRACT
COVID-19 was declared as a pandemic by the World Health Organization on March 11, 2020. Here, the dynamics of this epidemic is studied by using a generalized logistic function model and extended compartmental models with and without delays. For a chosen population, it is shown as to how forecasting may be done on the spreading of the infection by using a generalized logistic function model, which can be interpreted as a basic compartmental model. In an extended compartmental model, which is a modified form of the SEIQR model, the population is divided into susceptible, exposed, infectious, quarantined, and removed (recovered or dead) compartments, and a set of delay integral equations is used to describe the system dynamics. Time-varying infection rates are allowed in the model to capture the responses to control measures taken, and distributed delay distributions are used to capture variability in individual responses to an infection. The constructed extended compartmental model is a nonlinear dynamical system with distributed delays and time-varying parameters. The critical role of data is elucidated, and it is discussed as to how the compartmental model can be used to capture responses to various measures including quarantining. Data for different parts of the world are considered, and comparisons are also made in terms of the reproductive number. The obtained results can be useful for furthering the understanding of disease dynamics as well as for planning purposes.