TITAN Agent Based Model

# TITAN Simulation

TITAN (Treatment of Infectious Transmissions through Agent-based Network) is an agent-based simulation model used to explore contact transmission in complex social networks. Starting with the initializing agent population, TITAN iterates over a series of stochastic interactions where agents can interact with one another, transmit infections through various medium, and enter and exit the care continuum. The purpose of TITAN is to evaluate the impact of prevention and treatment models on incidence and prevalence rates of the targeted disease(s) through the use of data fitting simulated trajectories and rich statistics of primary/sub-population attributable proportions.

Agent populations are defined as graphs (nodes connected by edges). Nodes in the graph are used to represent the attributes (or collection of attributes) of an agent (person), and edges define the type of relationship between agents. In practice, a graph represents a social network of connected people through various relationship types, and provides the medium for which agents can interact.

## Getting Started

Install the package

pip install titan-model


This includes the script run_titan which can then be used to run the model.

### Prerequisites

• Python (or pypy) 3.6 or later

## Running the Model

run_titan -p my_params.yml


To run the model, execute the run_titan program. See TITAN params for documentation on how to set and use parameters.

Results of the model are generated and aggregated into the /results/ directory by default. If the model is re-run, the existing results will be overwritten.

## Built With

• Python3.x - Programming language

Van Rossum G, Drake FL. Python 3 Reference Manual. Scotts Valley, CA: CreateSpace; 2009.

• Networkx - Network structure backend

Hagberg A, Swart P, S Chult D. Exploring network structure, dynamics, and function using NetworkX. 2008.

• Numpy - Numerical libraries

Oliphant TE. A guide to NumPy. Vol. 1. Trelgol Publishing USA; 2006.

## Authors

• Lars Seeman - Initial work
• Max King - Continued development
• Sarah Bessey - Continued development
• Mary McGrath - Continued development

## Project details

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