Surveillance-to-Diagnostic Testing Program for Asymptomatic SARS-CoV-2 Infections on a Large, Urban Campus in Fall 2020.

Background: Six months into the COVID-19 pandemic, college campuses faced uncertainty regarding the likely prevalence and spread of disease, necessitating large-scale testing to help guide policy following re-entry.

Methods: A SARS-CoV-2 testing program combining pooled saliva sample surveillance leading to diagnosis and intervention surveyed over 112,000 samples from 18,029 students, staff and faculty, as part of integrative efforts to mitigate transmission at the Georgia Institute of Technology in Fall 2020.

Results: Cumulatively, we confirmed 1,508 individuals diagnostically, 62% of these through the surveillance program and the remainder through diagnostic tests of symptomatic individuals administered on or off campus.

A learning algorithm for applying synthesized stable dynamics to system identification.

In this paper the models discussed by Cohen are extended by introducing an input term. This allows the resulting models to be utilized for system identification tasks. This approach gives a direct way to encode qualitative information such as attractor dimension into the model.