This study assesses the accuracy of COVID-19 scenarios for new infections produced by the Swedish Public Health Agency (PHAS) from December 1, 2020, to March 20, 2023. We introduce a Similarity Error ( \(\:SEr\) ), which evaluates the dissimilarity between simulated and observed case time series using the following attributes: area under the curves, peak timings, and growth/decline rates before and after peaks. Rather than using an arbitrary cut-off, we used a threshold determined through Receiver Operating Characteristic (ROC) analysis, with performance evaluated using the Area Under the Curve (AUC), based on true positives identified by visual inspection for categorization. To further evaluate \(\:SEr\) ’s effectiveness, we conducted a sensitivity analysis across the full range of possible threshold values within the unit interval. Applying \(\:SEr\) with an optimal threshold determined through ROC-analysis 7 rounds out of 11 rounds were classified as having one or more similar scenarios, including the 6 rounds identified by visual inspection. Our findings indicate that, despite the challenges of a rapidly evolving epidemic, PHAS delivered simulations that reflected real-world trends in most of the rounds.
