Multicentral Agent-Based Model of Six Epidemic Waves of COVID-19 in the Nizhny Novgorod Region of Russian Federation

Abstract

Relevance. To investigate the characteristics 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 actions or characteristics of disease itself on COVID-19 spread dynamics. Employing agent-based models can be attractive because they take into consideration different population characteristics (e.g., age distribution and social activity) and restrictive measures, laboratory testing, etc., as well as random factors that are usually omitted in traditional modifications of the SIR-like dynamic models. Aim. Improvement of the previously proposed agent-based model [23,24] for modeling the spread of COVID-19 in various regions of the Russian Federation. At this stage, six waves of the spread of COVID-19 have been modeled in the Nizhny Novgorod region as a whole region, as well as in its individual cities, taking into account restrictive measures and vaccination of the population. Materials and Methods. In this paper we extend a recently proposed agent-based model for Monte Carlo-based numerical simulation of the spread of COVID-19 with consideration of testing and vaccination strategies. Analysis is performed in MATLAB/ GNU Octave. Results. Developed multicentral model allows for more accurate simulation of the epidemic dynamics within one region, when a patient zero usually arrives at a regional center, after which the distribution chains capture the periphery of the region due to pendulum migration. Furthermore, we demonstrate the application of the developed model to analyze the epidemic spread in the Nizhny Novgorod region of Russian Federation. The simulated dynamics of the daily newly detected cases and COVID-19-related deaths was in good agreement with the official statistical data both for the region as whole and different periphery cities. Conclusions. The results obtained with developed model suggest that the actual number of COVID-19 cases might be 1.5–3.0 times higher than the number of reported cases. The developed model also took into account the effect of vaccination. It is shown that with the same modeling parameters, but without vaccination, the third and fourth waves of the epidemic would be united into one characterized by a huge rise in the morbidity rates and the occurrence of natural individual immunity with the absence of further pandemic waves. Nonetheless, the number of deaths would exceed the real one by about 9–10 times.

Keywords

• dynamics
• forecasting
• agent-based modeling
• simulation modeling
• covid-19
• epidemiology
• surveillance
• methodology