A modeller's perspective on infection dynamics within and between hosts

Area of Science:

• Infectious disease dynamics
• Mathematical modeling
• Computational biology

Background:

• Understanding complex host-parasite interactions is crucial for disease control.
• Existing models often struggle to incorporate host and parasite heterogeneity.
• A series of research projects were undertaken to address these limitations.

Purpose of the Study:

• To develop mathematical and computational models for complex infection dynamics.
• To capture host and/or parasite heterogeneity within these models.
• To enhance understanding of within- and between-host infection processes.

Main Methods:

• A case-series approach was used, progressing in model complexity.
• Models were developed for diverse systems: experimental autoimmune encephalomyelitis (EAE) in mice, Mycobacterium avium subspecies paratuberculosis in cattle, Eimeria acervulina in chickens, and human malaria.
• Model components were designed with direct links to biological reality.

Main Results:

• Models successfully captured host and parasite heterogeneity.
• Complex experimental results were explained and simplified by the models.
• The modeling process itself provided significant insights.

Conclusions:

• Mathematical and computational models are valuable tools for understanding infection processes.
• Models can reconcile contradictory experimental findings.
• These models aid in predicting control measure efficacy and generating novel control strategies.