Work

Lightweight Models for Influenza and COVID-19 Prediction in Heterogeneous Populations: A Trade-Off Between Performance and Level of Detail

Year: 2025

Type: article

Abstract: In this work, we employ two modeling approaches—a mean-field model and a network model—for the purpose of modeling respiratory infection outbreaks in Russia. The presented approaches and their software implementation combine heterogeneity and structural simplicity and, in this sense, they close the gap between the compartmental SEIR models and complex detailed solutions based on agent-based approaches—the two most common modeling techniques for influenza and COVID-19 dynamics. The mathematical description of the approaches is presented, with SEIR compartmental model serving as a baseline for comparison. The experiments demonstrate the similarity of the modeling output of the presented approaches, which allows their interchangeable usage in replicating real outbreak dynamics in Russian cities. The ability of the discussed approaches to mimic data from Russian epidemic surveillance is shown by fitting a mean-field model to data from an influenza outbreak in Saint Petersburg in 2014–2015. The comparison of model complexity and their performance is made using synthetic scenarios. Following the results of numerical experiments, the comparative advantages and drawbacks of the approaches in the application to respiratory infection outbreaks are discussed. The presented modeling techniques, in addition to classical SEIR models and agent-based models as a part of epidemic surveillance, allow one to select the best modeling option for any particular task in outbreak surveillance and control, based on the computational resources at hand, data availability, and data quality.

Source: Mathematics

DOI: 10.3390/math13091385

Authors: Andrey Korzin, Vasiliy Leonenko

Institution: ITMO University

Language: English

Topic: Mathematical and Theoretical Epidemiology and Ecology Models

Subfield: Public Health, Environmental and Occupational Health

Field: Medicine

Domain: Health Sciences

SDG: Good health and well-being

Open Access status: gold

Estimated APC paid: $3,137