Effects of pathogen dependency in a multi-pathogen infectious disease system including population level heterogeneity...

Area of Science:

• Epidemiology
• Computational Biology
• Infectious Disease Modeling

Background:

• Agent-based models (ABMs) are increasingly used for epidemic modeling due to their ability to incorporate heterogeneous population structures like households.
• Existing ABMs primarily focus on single pathogen scenarios, overlooking the complex interactions between multiple pathogens.
• Pathogen interactions can directly or indirectly influence epidemic dynamics, necessitating more sophisticated modeling approaches.

Purpose of the Study:

• To develop and utilize a stochastic agent-based system for simulating epidemics involving multiple pathogens.
• To account for household-based transmission processes and inter-pathogen dependencies within the model.
• To explore the impact of various factors, including household size and pathogen dependency, on epidemic behavior.

Main Methods:

• Simulated epidemic scenarios using a stochastic agent-based model incorporating multiple pathogens.
• Varied parameters such as household size distributions, pathogen dependency (independent vs. dependent), and household isolation during symptomatic phases.
• Employed generalized additive models to analyze the association between epidemiological parameters and simulation data, using R 3.4.0 for analysis.

Main Results:

• Demonstrated the critical importance of considering pathogen dependency, showing significant differences compared to independent pathogen models.
• Found that accounting for pathogen dependency and household size distribution effectively contained epidemic spread.
• Observed that larger households reached epidemic peaks faster, irrespective of pathogen dependencies, and experienced higher overall infection rates.

Conclusions:

• The interaction between multiple pathogens significantly alters epidemic behavior across populations with varying household sizes.
• Future household cohort studies are essential for validating the modeled interaction processes of infectious disease systems with multiple pathogens.