Modeling Complex Infectious Disease Dynamics by Distinguishing Susceptible Populations

Predicting the dynamics of infectious diseases plays an important role in their prevention and control. However, traditional epidemic models, such as susceptible-infected-recovered (SIR), susceptible-exposed-infected-recovered (SEIR), and their variants, are often inadequate for accurately capturing behavior-driven and heterogeneous transmission at scale. In this study, we propose a nonlinear dynamic model-the susceptible-low-susceptible-exposed-infected-recovered-death (SLEIRD) model-that further extends the SEIR framework. SLEIRD explicitly differentiates susceptible populations by introducing a low-susceptible group adopting protective measures, models four infection channels from susceptible/low-susceptible to exposed/infectious groups, thereby providing a behavior-aware and structurally richer compartmentalization than classical models. To validate its effectiveness, we compare SLEIRD with other classical epidemic models using real-world epidemic data, predicting confirmed cases and deaths over five, ten, fifteen, twenty, and thirty days. Experimental results show that SLEIRD outperforms SIR, improving the root mean squared error (RMSE) for daily confirmed cases and deaths by 6.0%-46.3% and 31.1%-62.3%, respectively; compared with SEIR, SLEIRD reduces the average RMSE for daily confirmed cases and deaths by 6.8%-56.6% and 59.8%-79.6%, respectively. These findings indicate that SLEIRD more accurately simulates complex spread dynamics and offers a scalable, behavior-aware tool for data-driven epidemic forecasting with practical value for disease control and prevention.