Evaluation of diagnostic test procedures for SARS-CoV-2 using latent class models: comparison of antigen test kits and sampling for PCR testing based on Danish national data registries (preprint)

Antigen test kits have been used extensively as a screening tool during the worldwide pandemic of coronavirus (SARS-CoV-2). While it is generally expected that taking samples for analysis with PCR testing gives more reliable results than using antigen test kits, the overall sensitivity and specificity of the two protocols in the field have not yet been estimated without assuming that the PCR test constitutes a gold standard. We use latent class models to estimate the in situ performance of both PCR and antigen testing, using data from the Danish national registries. The results are based on 240,000 paired tests results sub-selected from the 55 million test results that were obtained in Denmark during the period from February 2021 until June 2021. We found that the specificity of both tests is very high in our data sample (>99.7%), while the sensitivity of PCR sampling was estimated to be 95.7% (95% CI: 92.8-98.4%) and that of the antigen test kits used in Denmark over the study period was estimated at 53.8% (95% CI: 49.8-57.9%). Our findings can be used as supplementary information for consideration when implementing serial testing strategies that employ a confirmatory PCR sample following a positive result from an antigen test kit, such as the policy used in Denmark. We note that while this strategy reduces the number of false positives associated with antigen test screening, it also increases the false negatives. We demonstrate that the balance of trading false positives for false negatives only favours the use of serial testing when the expected true prevalence is low. Our results contain substantial uncertainty in the estimates for sensitivity due to the relatively small number of positive test results over this period: validation of our findings in a population with higher prevalence would therefore be highly relevant for future work.

The Epidemic Volatility Index: an early warning tool for epidemics (preprint)

Background: . This paper presents, for the first time, the Epidemic Volatility Index (EVI), a conceptually simple, early warning tool for emerging epidemic waves. Methods: . EVI is based on the volatility of the newly reported cases per unit of time, ideally per day, and issues an early warning when the rate of the volatility change exceeds a threshold. Results: . Results from the COVID-19 epidemic in Italy and New York are presented here, while daily updated predictions for all world countries and each of the United States are available online. Interpretation . EVI’s application to data from the current COVID-19 pandemic revealed a consistent and stable performance in terms of detecting oncoming waves. The application of EVI to other epidemics and syndromic surveillance tasks in combination with existing early warning systems will enhance our ability to act fast and optimize containment of outbreaks.