Testing, tracing and isolation in compartmental models.

Area of Science:

• Epidemiology
• Mathematical Biology
• Computational Science

Background:

• Standard compartmental models like SEIR struggle to accurately represent contact tracing effects.
• Agent-based models (ABMs) are complex, computationally expensive, and less understood alternatives for evaluating testing and tracing strategies.

Purpose of the Study:

• To present a novel method for integrating Testing, contact-Tracing, and Isolation (TTI) strategies into compartmental epidemic models.
• To develop a computationally efficient model that accurately approximates agent-based model behavior for outbreak management.

Main Methods:

• Derivation of a new SEIR-TTI model using probabilistic arguments to link individual-level contact tracing to population-level dynamics.
• Comparison of the SEIR-TTI model's accuracy and computational cost against mechanistic agent-based models.

Main Results:

• The developed SEIR-TTI model accurately approximates the behavior of agent-based models.
• The SEIR-TTI model offers significantly reduced computational cost compared to ABMs.
• The model is suitable for exploratory analysis to determine optimal testing and tracing levels.

Conclusions:

• The SEIR-TTI model provides an accurate and computationally efficient approach for incorporating TTI strategies into epidemic modeling.
• This method facilitates adaptive planning for disease outbreak management by quantifying intervention requirements.
• The model enhances the evaluation of public health strategies for epidemic containment.