Analysis of COVID-19 case numbers: adjustment for diagnostic misclassification on the example of German case reporting data
Abstract
Background
Reported COVID-19 case numbers are key to monitoring pandemic spread and decision-making on policy measures but require careful interpretation as they depend substantially on testing strategy. A high and targeted testing activity is essential for a successful Test-Trace-Isolate strategy. However, it also leads to increased numbers of false-positives and can foster a debate on the actual pandemic state, which can slow down action and acceptance of containment measures.
Aim
We evaluate the impact of misclassification in COVID-19 diagnostics on reported case numbers and estimated numbers of disease onsets (epidemic curve).
Methods
We developed a statistical adjustment of reported case numbers for erroneous diagnostic results that facilitates a misclassification-adjusted real-time estimation of the epidemic curve based on nowcasting. Under realistic misclassification scenarios, we provide adjusted case numbers for Germany and illustrate misclassification-adjusted nowcasting for Bavarian data.
Results
We quantify the impact of diagnostic misclassification on time-series of reported case numbers, highlighting the relevance of a specificity smaller than one when test activity changes over time. Adjusting for misclassification, we find that the increase of cases starting in July might have been smaller than indicated by raw case counts, but cannot be fully explained by increasing numbers of false-positives due to increased testing. The effect of misclassification becomes negligible when true incidence is high.
Conclusions
Adjusting case numbers for misclassification can improve this important measure on short-term dynamics of the pandemic and should be considered in data-based surveillance. Further limitations of case reporting data exist and have to be considered.
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