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Abstract:
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Surveillance research is of great importance for monitoring disease prevalence among populations by effective statistical sampling approaches. Benefiting from multiple surveillance efforts in current epidemiological initiatives for monitoring disease, we extend and customize traditional capture-recapture (CRC) methods in tandem with epidemiological design and implementation principles in order to leverage error-prone diagnostic signals toward defensible estimates of case counts.
In this paper, we start with the independence assumption for two data streams (the LP, for "Lincoln-Petersen" condition) and propose a CRC framework to estimate case counts incorporating positive predictive values (PPV). Next, we relax the LP condition and assume the two data streams are not necessarily operating independently. In this case, we propose an approach for sensitivity and uncertainty analyses focusing on key non-identifiable parameters to generalize the LP condition-based CRC estimator. Finally, to justify the LP conditions by design, we consider the benefits of implementing an additional well-controlled surveillance effort (an “anchor stream”), which is conducted independent
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