Group Testing Enables Asymptomatic Screening for COVID-19 Mitigation: Feasibility and Optimal Pool Size Selection with Dilution Effects

Semi-Quantitative Group Testing for Efficient and Accurate qPCR Screening of Pathogens with a Wide Range of Loads

“…Our FNR model is based on the following “sigmoid” function, where a, b are two tunable parameters that can be used to fit the measured/estimated FNRs. Note that similar ideas were also discussed in [31]; however, as may be seen from Figure 6 (b), the FNR function ( a = 35.8, b = 0.08) proposed in [31] significantly deviates from real-world experimental data.…”

“…Specifically, we consider the right half of the curve ( Ct > 25) to be caused by the false negative results, which agrees with the intuition that the FNR increases as the Ct value increases. (b) We fit the FNR model from Equation (4), and the ones from [28, 31] to the real-world experimental data. As it is apparent, the black and purple lines provide a poor fit to the data while our model (green line) with parameters ( a = 36.9, b = 2.145) represents a significantly more accurate fit.…”

“…Here we consider the right half of the curve (Ct ą 25) to be caused by the false negative results, which agrees with the intuition that the FNR increases as the Ct value increases. Our fitted FNR curve is shown in Figure 6 (b), along with the estimated FNR curve from experimental results, and the models from [28,31]. As it is apparent, the latter provides a poor fit to the data while our model with parameters pa " 36.9, b " 2.145q represents a significantly more accurate fit.…”