SimAEN -- Simulated Automated Exposure Notification
Ted Londner, Jonathan Saunders, Dieter W. Schuldt, and Bill Streilein

TL;DR
This paper investigates the effectiveness of automated contact tracing using simulated disease spread models to inform public health decisions, balancing intervention benefits and costs.
Contribution
It introduces a simulation framework to evaluate automated exposure notification strategies and identifies critical parameters influencing their effectiveness.
Findings
Automated contact tracing can significantly reduce infection spread under certain conditions.
Model parameters such as testing delay and contact logging accuracy are critical for intervention success.
The study provides insights into optimal intervention strategies balancing efficacy and resource constraints.
Abstract
Mitigation strategies that remove infectious individuals from the greater population have to balance their efficacy with the economic effects associated with quarantine and have to contend with the limited resources available to the public health authorities. Prior strategies have relied on testing and contact tracing to find individuals before they become infectious and in order to limit their interactions with others until after their infectious period has passed. Manual contact tracing is a public health intervention where individuals testing positive are interviewed to identify other members of the community who they may have come into contact with. These interviews can take a significant amount of time that has to be tallied in the overall accounting of the outbreak cost. The concept of contact tracing has been expanded recently into Automated Exposure Notification whereby…
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Taxonomy
TopicsAir Quality Monitoring and Forecasting · Anomaly Detection Techniques and Applications · Radiation Detection and Scintillator Technologies
