Efficient coupling of within-and between-host infectious disease dynamics

Area of Science:

• Epidemiology
• Mathematical Biology
• Computational Science

Background:

• Mathematical models of infectious disease transmission often overlook crucial within-host dynamics.
• Within-host processes like pathogen replication and immune response significantly impact individual disease progression, evolution, and population-level transmission.
• Existing models struggle to couple within- and between-host dynamics realistically and efficiently.

Purpose of the Study:

• To develop a novel, adaptable, and broadly applicable computational method for modeling both within- and between-host infectious disease dynamics.
• To create a model that realistically couples dynamics across scales, offering computational efficiency.
• To investigate how simplifying assumptions in epidemiological models affect disease dynamics.

Main Methods:

• A novel method coupling deterministic within-host dynamics of individuals with stochastic population-level host state variables.
• Utilizes fast numerical methods for both individual and population scales.
• Validates the approach against full stochastic individual-based simulations.

Main Results:

• The proposed method accurately captures transient within-host dynamics and stochastic transmission.
• It demonstrates close agreement with full stochastic individual-based simulations.
• Analysis reveals how common simplifying assumptions can fundamentally alter epidemiological and evolutionary dynamics.

Conclusions:

• The novel computational approach provides a realistic and efficient way to model infectious disease transmission across scales.
• This method is particularly valuable for scenarios where within-host dynamics are not rapid or for long-term pathogen evolution tracking.
• The study highlights the importance of incorporating detailed within-host processes for accurate disease modeling.