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Applications and Case Studies

Overcoming Repeated Testing Schedule Bias in Estimates of Disease Prevalence

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Pages 1-13 | Received 01 Sep 2022, Accepted 05 Jul 2023, Published online: 06 Sep 2023

Figures & data

Fig. 1 Simulations satisfying assumptions. Target for mean row is the mean of true prevalences across datasets. The RMSEs of the test-positive rate and HT-known estimator are identical under simple random testing. The variance of the HT-known estimator is higher than that of the HT-estimated due to occasionally very large weights.

Fig. 1 Simulations satisfying assumptions. Target for mean row is the mean of true prevalences across datasets. The RMSEs of the test-positive rate and HT-known estimator are identical under simple random testing. The variance of the HT-known estimator is higher than that of the HT-estimated due to occasionally very large weights.

Fig. 2 Daily test counts, including multiple tests per week by the same student. Vertical grid lines correspond to Mondays.

Fig. 2 Daily test counts, including multiple tests per week by the same student. Vertical grid lines correspond to Mondays.

Fig. 3 Daily test counts, including multiple tests per week by the same student. Vertical grid lines correspond to Mondays.

Fig. 3 Daily test counts, including multiple tests per week by the same student. Vertical grid lines correspond to Mondays.

Fig. 4 Daily prevalence estimates, adjusted for test sensitivity and reporting delays. Shaded ribbon is BCa 95% confidence band. No estimates for days with < 100 tests taken. Vertical grid lines correspond to Mondays.

Fig. 4 Daily prevalence estimates, adjusted for test sensitivity and reporting delays. Shaded ribbon is BCa 95% confidence band. No estimates for days with < 100 tests taken. Vertical grid lines correspond to Mondays.
Supplemental material

UASA_A_2238943_supplemental.zip

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