  This study investigates the utilization of various mathematical models for
comprehending and managing outbreaks of infectious diseases, with a specific
focus on how different distributions of incubation times influence predictions
regarding epidemics. Two methodologies are examined: a compartmental SEnIR ODE
model, which represents an enhanced version of the mean-field SEIR model, and a
stochastic agent-based complex network model. Our findings demonstrate that the
selection of diverse incubation time distributions can result in noteworthy
discrepancies in critical epidemic forecasts, highlighting the crucial role of
precise modeling in shaping effective public health interventions. The research
underscores the necessity of integrating authentic distribution patterns into
epidemic modeling to increase its reliability and applicability
