https://doi.org/10.1140/epjp/s13360-025-06481-z
Regular Article
Network-based SEITR epidemiological model with contact heterogeneity: comparison with homogeneous models for random, scale-free and small-world networks
Tandy School of Computer Science, The University of Tulsa, 800 South Tucker Drive, Tulsa, OK, USA
Received:
14
March
2025
Accepted:
26
May
2025
Published online:
18
June
2025
The spread of infectious diseases can be accurately modeled using differential equations, but these models assume the contacts between individuals are homogeneous. Network methods, on the other hand, can account for variation of contacts between individuals. We introduce a new network-based epidemiological modeling algorithm for a susceptible-exposed-infected-treated-recovered (SEITR) model. The epidemiological parameters of the network model map to the homogeneous ODE model parameter for comparison. We use a range of simulated rate constants and random network distributions to directly compare the heterogeneous network-based approach and the homogeneous ODE model. We simulate SEITR models on Erdős–Renyi, Barabási–Albert, and Watts–Strogatz networks with various sizes and connectivities. Among these networks, Watts–Strogatz networks exhibit the largest deviations from the homogeneous model, highlighting the importance of this network structure in influencing disease dynamics. We use the same parametric values in the network-based algorithm as in the compartmental ODE model, and find notable differences for the infectious compartment due to network effects. We demonstrate the utility of this approach on a realistic model of lumpy skin disease (LSD) transmission dynamics, which can be extended to other infectious diseases. The heterogeneous network-based SEITR model provides novel insights into LSD transmission, and the network method provides a valuable tool for researchers to model deviation from the homogeneous assumptions for other viruses.
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© The Author(s), under exclusive licence to Società Italiana di Fisica and Springer-Verlag GmbH Germany, part of Springer Nature 2025
Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
