Estimating contact rates at a mass gathering by using video analysis: a proof-of-concept project

BMC Public Health - Tập 14 - Trang 1-6 - 2014
Jeanette J Rainey1, Anil Cheriyadat2, Richard J Radke3, Julie Suzuki Crumly4, Daniel B Koch2
1Division of Global Migration and Quarantine, Centers for Disease Control and Prevention, Atlanta, USA
2Oak Ridge National, Laboratory, Oak Ridge, USA
3Department of Electrical, Computer, and Systems Engineering, Rensselaer Polytechnic Institute, Troy, USA
4[Oak Ridge Associated Universities, Oak Ridge, USA]

Tóm tắt

Current approaches for estimating social mixing patterns and infectious disease transmission at mass gatherings have been limited by various constraints, including low participation rates for volunteer-based research projects and challenges in quantifying spatially and temporally accurate person-to-person interactions. We developed a proof-of-concept project to assess the use of automated video analysis for estimating contact rates of attendees of the GameFest 2013 event at Rensselaer Polytechnic Institute (RPI) in Troy, New York. Video tracking and analysis algorithms were used to estimate the number and duration of contacts for 5 attendees during a 3-minute clip from the RPI video. Attendees were considered to have a contact event if the distance between them and another person was ≤1 meter. Contact duration was estimated in seconds. We also simulated 50 attendees assuming random mixing using a geo-spatially accurate representation of the same GameFest location. The 5 attendees had an overall median of 2 contact events during the 3-minute video clip (range: 0–6). Contact events varied from less than 5 seconds to the full duration of the 3-minute clip. The random mixing simulation was visualized and presented as a contrasting example. We were able to estimate the number and duration of contacts for 5 GameFest attendees from a 3-minute video clip that can be compared to a random mixing simulation model at the same location. The next phase will involve scaling the system for simultaneous analysis of mixing patterns from hours-long videos and comparing our results with other approaches for collecting contact data from mass gathering attendees.

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