Presented By: Michigan Institute for Data Science
The Testing Paradox for COVID-19
Bhramar Mukherjee - Professor and Chair, Biostatistics
Reported case-counts for coronavirus are wrinkled with data errors, namely misclassification of the tests and selection bias associated with who got tested. The number of covert or unascertained infections is large across the world. How can one determine optimal testing strategies with such imperfect data? In this talk, we propose an optimization algorithm for allocating diagnostic/surveillance tests when your objective is estimating the true population prevalence or detecting an outbreak. Infectious disease models and survey sampling techniques are used jointly to come up with these strategies
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