A novel approach to disease modeling: The SEIVR model with a vulnerable compartment

Abstract

Background

The SEIR model is a cornerstone in epidemiology and offers insights into the spread of infectious diseases. It extends the basic SIR model to include an Exposed (E) compartment, to account for the incubation period of diseases. However, the traditional SEIR model falls short in addressing varying disease severity, particularly the need for hospitalization, which is crucial for understanding the full impact of a disease outbreak.

Objective

This study aims to increase the predictive power of the traditional SEIR model by introducing a Vulnerable (V) compartment, thus creating the SEIVR model. This new compartment captures individuals who are exposed to a transmitted disease and require hospitalization to recover, thereby providing a more nuanced view of disease progression.

Methods

To develop the SEIVR model, we modified the SEIR framework to include the Vulnerable (V) compartment. We used differential equations to describe the transitions between compartments. Parameter estimation was performed using least squares fitting, and the model was rigorously validated against real-world data to ensure its accuracy in predicting hospitalizations and healthcare demands.

Results

The SEIVR model accurately predicted the progression of disease and its impact on healthcare resources. Model predictions closely mirrored the observed data, thus showcasing its effectiveness in estimating new cases, hospitalizations, and recoveries. This validation underscores the capability of the model to provide a realistic representation of disease dynamics to inform public health interventions.

Conclusion

With the addition of the Vulnerable compartment, the SEIVR model offers a more precise and comprehensive understanding of disease dynamics. It excels in predicting new cases, hospitalizations, and recoveries, making it an invaluable tool for public health planning and resource allocation. This model is particularly beneficial for diseases which may require hospitalization, such as COVID-19, thus enhancing the accuracy of predictions of healthcare demand.