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The Simulation of Automated Exposure Notification (SimAEN) Model

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[Technical Report, Other]

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Automated Exposure Notication AEN was implemented in 2020 to supplement traditional contact tracing for COVID-19 by estimating too close for too long proximities of people using the service. AEN uses Bluetooth messages to privately label and recall proximity events, so that persons who were likely exposed to SARS-CoV-2 can take the appropriate steps recommended by their health care authority. This paper describes an agent-based model that estimates the effects of AEN deployment on COVID-19 caseloads and public health workloads in the context of other critical public health measures available during the COVID-19 pandemic. We selected simulation variables pertinent to AEN deployment options, varied them in accord with the system dynamics available in 2020-2021, and calculated the outcomes of key metrics across repeated runs of the stochastic multi-week simulation. SimAENs parameters were set to ranges of observed values in consultation with public health professionals and the rapidly accumulating literature on COVID-19 transmission the model was validated against available population-level disease metrics. Estimates from SimAEN can help public health officials determine what AEN deployment decisions e.g., configuration, workflow integration, and targeted adoption levels can be most effective in their jurisdiction, in combination with other COVID-19 interventions e.g., mask use, vaccination, quarantine and isolation periods.

Subject Categories:

  • Medicine and Medical Research
  • Computer Programming and Software

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[A, Approved For Public Release]