Assaying Large-scale Testing Models to InterpretCovid-19 Case Numbers: A Cross-country Study

Large-scale testing is considered key to assessing the state of the current COVID-19 pandemic, yet interpreting such data remains elusive. We modeled competing hypotheses regarding the underlying testing mechanisms, thereby providing different prevalence estimates based on case numbers, and used them to predict SARS-CoV-2-attributed death rate trajectories. Assuming that individuals were tested based solely on a predefined risk of being infectious implied the absolute case numbers reflected prevalence, but turned out to be a poor predictor. In contrast, models accounting for testing capacity, limiting the pool of tested individuals, performed better. This puts forward the percentage of positive tests as a robust indicator of epidemic dynamics in absence of country-specific information. We next demonstrated this strongly affects data interpretation. Notably absolute case numbers trajectories consistently overestimated growth rates at the beginning of two COVID-19 epidemic waves. Overall, this supports non-trivial testing mechanisms can be inferred from data and should be scrutinized.