COVID testing can play a number of roles in a pandemic response: distinguishing between COVID and other causes for symptomatic patients, clearing individuals for elective medical procedures, surveillance for public health planning, and proactive testing of individuals not suspected to have disease to reduce disease spread and control an epidemic. In this talk, I look the value of proactive testing using two different mathematical modeling approaches: a simple analytic approximation, and a detailed stochastic SEIR model on a multi-level network. Both models illustrate the value of proactive testing as a component of an infection control strategy
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