  Since a significant amount of disease transmission occurs through
human-to-human or social contact, understanding who interacts with whom in time
and space is essential for disease transmission modeling, prediction, and
assessment of prevention strategies in different environments and special
settings. Thus, measuring contact mixing patterns, often in the form of a
contacts matrix, has been a key component of heterogeneous disease transmission
modeling research. Several data collection techniques estimate or calculate a
contacts matrix at different geographical scales and population mixes based on
surveys and sensors. This paper presents a methodology for generating a
contacts matrix by using high fidelity simulations which mimic actual workflow
and movements of individuals in time and space. Results of this study show that
such simulations can be a feasible, flexible, and reasonable alternative method
for estimating social contacts and generating contacts mixing matrices for
various settings under different conditions.
