Efficient and flexible methods for simulating models of time since infection

Area of Science:

• Epidemiology
• Computational Biology
• Mathematical Modeling

Background:

• Epidemic models aid in infectious disease control and economic impact assessment.
• Compartment models are common but less precise than time since infection (TSI) models.
• TSI models offer more detailed disease transmission insights but are computationally intensive.

Purpose of the Study:

• To demonstrate that TSI models can be computationally efficient.
• To present a method for decoupling disease stages in TSI models.
• To generalize TSI models for age-structured populations.

Main Methods:

• Developed a discretization scheme to improve TSI model efficiency.
• Proposed a method to decouple disease transmission dynamics from residence time distributions.
• Generalized methods for age-structured TSI models.

Main Results:

• TSI models are computationally comparable to compartment models with appropriate discretization.
• Decoupled disease stages enhance TSI model flexibility.
• Efficient numerical methods enable optimal epidemic control calculations.

Conclusions:

• TSI models offer a more precise and computationally feasible approach to epidemic modeling.
• The proposed methods facilitate the development of more reliable disease control strategies.
• The pyross software package implements these efficient numerical tools.