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A lack of adaptation to the dynamic rate of COVID-19-related events could lead to significantly skewed measurements. Inaccuracies in the estimations can result from neglecting the variations in the occurrence of COVID-19 associated outcomes. Evaluating the error-detecting power of the DetectDeviatingCells (DDC) algorithm, we investigated its capability to identify unusual observations (cases) and cell-level anomalies within continuous variables. We evaluated its performance using a simulated data set and contrasted it with the performance of other techni
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