A network epidemic model: From the mathematical analysis to machine learning experiments
Year: 2024
Type: article
Abstract: We consider a generic Susceptible-Infected-Recovered-Hospitalised-Deceased model for the spread of infectious diseases over contact networks. After properly reducing it to a nonlinear susceptible-infected-recovered (SIR) model on a graph, we systematically investigate its invariant sets, its stability, convergence, monotonic- ity and equilibrium properties. Given the significant influence of network structure on infection dynamics, the basic reproduction number is insufficient for predicting disease evolution. We introduce more suitable threshold conditions for the model. However, due to the inherent high dimensionality of networks, modeling transmission through them necessitates numerical methods. This work provides numerical demonstrations of epidemic spread, validating the derived threshold conditions. Additionally, we highlight the limitations of social distancing measures in the presence of stable equilibria with a persistent infected population using especially machine learning tools.
Language: English
Field: Medicine
Domain: Health Sciences
Open Access status: hybrid
Estimated APC paid: $3,600
OpenAlex ID: W4404851536