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Tools for the Real-Time Assessment of a Pseudomonas aeruginosa Infection Model

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Joseph D Peterson¹, Ronojoy Adhikari¹

¹University of Cambridge, DAMTP, Center for Mathematical Sciences, Wilberforce Rd., Cambridge CB3 0WA, United Kingdom.

Physical Review. E

|September 16, 2021

Summary

This study demonstrates that time since infection (TSI) epidemic models can be computationally efficient, rivaling compartment models. This allows for more precise disease transmission projections and optimized control strategies.

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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.