nosoi

nosoi simulates stochastic agent-based transmission chains as an R package to model infectious disease spread across single- and dual-host systems (e.g., arboviruses) and to explore within-host and population-level epidemic dynamics.

Key Features:

• Agent-Based Framework: Operates an agent-based model where each host/agent undergoes events during discrete simulation time steps, including movement between locations and transmission of infection.
• Stochastic Simulation: Incorporates stochastic elements to represent the inherent randomness of real-world disease transmission processes.
• Dual-Host Systems Support: Supports simulation of dual-host systems such as arboviruses as well as single-host epidemics, enabling exploration of within-host viral dynamics and complex host interactions.
• Parameter Variability: Allows manipulation of epidemiological and behavioral parameters to assess their impact on epidemic potential.
• Empirical Data Integration: Accepts empirical inputs (e.g., travel patterns) and user-defined rules to govern agent behaviors and transmission events.

Scientific Applications:

• Transmission Chain Reconstruction: Simulates transmission chains under varied conditions to validate and benchmark reconstruction methods.
• Epidemic Modeling and Phylodynamic Analyses: Generates diverse epidemic scenarios for testing and refining models of disease dynamics and evolutionary processes.
• Empirical Data Integration: Enables incorporation of real-world data into simulated outbreaks to increase realism and applicability of analyses.
• Educational Use: Provides simulation scenarios for teaching and illustrating epidemiological processes and drivers of transmission.

Methodology:

Agent-based stochastic simulations proceed in discrete time steps where agents undergo events (movement, transmission) governed by user-defined or empirical rules in a non-mathematical, algorithmic framework.