Adding a reaction-restoration type transmission rate dynamic-law to the basic SEIR COVID-19 model

Area of Science:

• Epidemiology
• Mathematical Biology
• Infectious Disease Dynamics

Background:

• Classical SEIR models have limitations in replicating real-world epidemic curves, often showing a unimodal shape not typically observed.
• Pandemic situations involve complex population behaviors affecting disease transmission rates over time.

Purpose of the Study:

• Introduce the βSEIR model, an extension of the SEIR model, to better represent pandemic dynamics.
• Incorporate a differential law to dynamically model variations in disease transmission rates.
• Capture the influence of population behaviors, such as mitigation adherence and restoration urges, on epidemic curves.

Main Methods:

• Developed the βSEIR model by adding a differential law to the classical SEIR model to account for transmission rate variations.
• Modeled two opposing population thrives: reaction to disease (decreasing transmission) and urge to return to normalcy (restoring transmission).
• Applied the model to COVID-19 data from Chile and Italy to validate its predictive capabilities.

Main Results:

• The βSEIR model generates a wider spectrum of dynamic variabilities in infected curves compared to classical models.
• Results demonstrate the model's ability to capture observed COVID-19 dynamics, including decreasing adherence to mitigation and seasonal effects.
• The model successfully replicated the evolution of new confirmed cases in Chile and Italy for several months post-onset.

Conclusions:

• The βSEIR model provides a more realistic representation of pandemic trajectories by accounting for time-varying transmission rates.
• Population behaviors significantly impact disease spread, and the model effectively integrates these factors.
• This enhanced modeling approach has potential applications for understanding and predicting future pandemic evolutions.