ArticlePublic health in practice (Oxford, England)2026
From case counts to probability sampling: Simulation insights into pandemic surveillance.
Article in Public health in practice (Oxford, England), 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
Objectives: To identify data strategies that ensure valid epidemic surveillance across different prevalence levels. Study design: A simulation study using realistic microdata from the German MikroSim project, reflecting the demographic structure of two districts. Epidemic dynamics were modeled over one year with an SIR framework, varying prevalence (>0% to 12%), test accuracy (80% to 98%), and sample size (5000 to 30,000). Methods: Passive case-based surveillance relying on reported infections was compared with probability-based population sampling for estimating weekly prevalence levels and changes. Results: Case-based surveillance was reliable only at very low prevalence. Once prevalence exceeded 3-5%, estimates became unstable and systematically biased, reflecting testing patterns rather than true infection dynamics. Probability sampling, in contrast, produced unbiased, precise estimates and enabled timely integration of individual-level social and health data. Conclusion: Surveillance systems should be adaptive. While passive reporting may suffice at low prevalence by practicability and costs, probability-based sampling becomes essential once moderate prevalence thresholds are crossed (3-5%). Such thresholds vary by disease and are shaped by symptom profile and transmission dynamics. Embedding predefined prevalence-based switch points that trigger representative sampling would ensure valid estimates, strengthen preparedness, and support timely, evidence-based public health decision-making.
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