Currently, most of the commonly used mathematical epidemiology models rely on ordinal differential equations based on the SIR model, in which individuals can be either Sucseptible, Infectious or Recovered. It is common to include an “Exposed” stage, which follows infection but precedes the infectious phase, during which the disease is incubating and cannot yet be transmitted to others. These are the SEIR models. While these models are simple and flexible, their usual mathematical formulation imply an exponential distribution for the time each individual remains in the exposed and infectious stages. However, several studies show that the incubation periods of most infectious diseases, including the recent COVID-19 pandemic, typically follow Gamma-like distributions.

In this poster, we analyze the impact of varying the incubation time distribution using a refined version of the SEIR model, and demonstrate how predictions change with different Gamma distributions for the incubation period. The observed variations in key epidemic predictions, such as the height and timing of infection peaks, underscore the importance of selecting model parameters that accurately reflect the experimentally measured time distributions, and that using an exponential distribution underestimates the impact of the epidemic.