The discrete-time Kermack-McKendrick model: A versatile and computationally attractive framework for modeling...

Area of Science:

• Epidemiology
• Mathematical Biology
• Computational Science

Background:

• COVID-19 pandemic spurred development of numerous infectious disease spread models.
• Existing large compartmental models implicitly constrain generation-time distributions.
• Continuous-time Kermack-McKendrick (KM27) model allows flexible generation-time distributions but is computationally complex.

Purpose of the Study:

• Introduce a computationally efficient, discrete-time version of the KM27 model.
• Provide a flexible tool for exploring infectious disease control scenarios, such as for COVID-19.
• Investigate the impact of model parameters on disease incidence peak size.

Main Methods:

• Developed a discrete-time implementation of the KM27 epidemic model.
• Numerically simulated disease spread scenarios using the new model.
• Compared incidence-peak predictions with traditional compartmental models.

Main Results:

• The discrete-time KM27 model is general, flexible, and computationally easy to implement.
• Models with fixed latent and infectious periods predict lower peak sizes than compartmental models.
• This difference persists even with identical reproduction numbers and initial growth rates.

Conclusions:

• The discrete-time KM27 model offers a powerful and accessible tool for epidemic modeling and control strategy evaluation.
• Accurate modeling of generation-time distributions is crucial for predicting epidemic peak sizes.
• Findings highlight the importance of model structure in forecasting disease dynamics.