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 .
Large-scale testing is considered key to assess the state of the current COVID-19 pandemic . Yet, the link between the reported case numbers and the true state of the pandemic remains elusive. We develop mathematical models based on competing hypotheses regarding this link , thereby providing different prevalence estimates based on case numbers, and validate them by predicting SARS-CoV-2-attributed death rate trajectories. Assuming that individuals were tested based solely on a predefined risk of being infectious implies the absolute case numbers reflect the prevalence, but turned out to be a poor predictor , consistently overestimating growth rates at the beginning of two COVID-19 epidemic waves . In contrast, assuming that testing capacity is fully exploited performs better. This leads to using the percent-positive rate as a more robust indicator of epidemic dynamics , however we find it is subject to a saturation phenomenon that needs to be accounted for as the number of tests becomes larger .
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validate them by predicting
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implies
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, consistently overestimating growth rates at the beginning of two COVID-19 epidemic waves
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models accounting for testing capacity , limiting the pool of tested individuals, performed
assuming that testing capacity is fully exploited performs
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puts forward the percentage of positive tests as a
leads to using the percent-positive rate as a more
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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
, however we find it is subject to a saturation phenomenon that needs to be accounted for as the number of tests becomes larger