6th CEN Conference

Beyond Case Counts: Simulation Evidence for Probability-Based Pandemic Surveillance

20 May 2026, 14:39

18m

Room 13 A

oral presentation Methods in epidemiology 2

Speaker

Inken Siems (Trier University)

Description

High-quality data are essential for reliable epidemic surveillance. Traditional systems relying on passive case reporting that may lead to unreliable prevalence estimates depending on the specific disease. Using the example of the COVID-19 pandemic, we show that once prevalence exceeds moderate levels, conventional reporting becomes biased and unstable. Beyond this point, drawing additional representative samples provides accurate estimates and enables the collection of additional information necessary for deeper insights into the pandemic’s impact. Adaptive surveillance designs incorporating probability sampling are necessary to ensure data quality and enable reliable evidence-based policies.

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