Impact of heterogeneity on infection probability: Insights from single-hit dose-response models

Area of Science:

• Mathematical Biology
• Infectious Disease Modeling
• Microbial Ecology

Background:

• Host infection processes are intricate, affected by microbial variations within and between hosts, and varying doses.
• Understanding these factors is crucial for predicting infection outcomes and developing effective interventions.

Purpose of the Study:

• To investigate the impact of microbial heterogeneity and dose variation on host infection probability using mathematical models.
• To analyze how within-host and between-host variations influence the likelihood of infection.

Main Methods:

• Utilized dose-response and within-host microbial infection models.
• Employed analytical methods, including those assuming small infectivity.
• Incorporated a within-host microbial growth model to explore heterogeneous infectivity dynamics.

Main Results:

• Within-host heterogeneity in microbial infectivity was demonstrated to enhance infection probability.
• Both within-host and between-host variations in infectivity and dose were found to reduce the expected infection probability.
• The observed trend held across various dose-response models, indicating robustness beyond the small infectivity assumption.

Conclusions:

• Heterogeneity in microbial infectivity plays a significant role in infection dynamics.
• Mathematical models provide valuable predictions for experimental validation in infection studies.
• Further research can be guided by these findings to deepen the understanding of host-pathogen interactions.