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The interpretation of sampling data plays a crucial role in policy responseto the spread of a disease during an epidemic, such as the COVID-19 epidemic of2020. However, this is a non-trivial endeavor due to the complexity of realworld conditions and limits to the availability of diagnostic tests, whichnecessitate a bias in testing favoring symptomatic individuals. A thoroughunderstanding of sampling confidence and bias is necessary in order makeaccurate conclusions. In this manuscript, we provide a stochastic model ofsampling for assessing confidence in disease metrics such as trend detection,peak detection, and disease spread estimation. Our model simulates testing fora disease in an epidemic with known dynamics, allowing us to use Monte-Carlosampling to assess metric confidence. This model can provide realisticsimulated data which can be used in the design and calibration of data analysisand prediction methods. As an example, we use this method to show that trendsin the disease may be identified using under $10000$ biased samples each day,and an estimate of disease spread can be made with additional $1000-2000$unbiased samples each day. We also demonstrate that the model can be used toassess more advanced metrics by finding the precision and recall of a strategyfor finding peaks in the dynamics.

Confidence in the dynamic spread of epidemics under biased sampling conditions