The importance of the incubation time distribution in compartmental epidemiological models.

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. • We show how key epidemiological predictions depend on the incubation time distribution. • We utilized two kinds of models: mean-field SEIR models, and agent-based models. • We emphasize that integrating authentic distributions increases the models' reliability and applicability. [ABSTRACT FROM AUTHOR]