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Objective : This paper motivates and justifies the use of antigen tests for epidemic control as distinct from a diagnostic test. Study Design and Setting : We discuss the relative advantages of antigen and PCR tests, summarising evidence from both the literature as well as Austrian schools, which conducted frequent, mass rapid antigen testing during the spring of 2021. While our report on testing predates Delta, we have updated the review with recent data on viral loads in breakthrough infections and more information about testing efficacy, especially in children. Results : Rapid antigen tests detect proteins at the surface of virus particles, identifying the disease during its infectious phase. In contrast, PCR tests detect viral genomes: they can thus diagnose COVID-19 before the infectious phase but also react to remnants of the virus genome, even weeks after live virus ceases to be detectable in the respiratory tract. Furthermore, the logistics for administering the tests are different. Large-scale rapid antigen testing in Austrian schools showed low false-positive rates along with an approximately 10% lower effective reproduction number in the tested cohort. Conclusion : Using antigen tests at least 2-3 times per week could become a powerful tool to suppress the COVID-19 pandemic.

A primary quantity of interest in the study of infectious diseases is the average number of new infections that an infected person produces. This so-called reproduction number has significant implications for the disease progression. There has been increasing literature suggesting that superspreading, the significant variability in number of new infections caused by individuals, plays an important role in the spread of COVID-19. In this paper, we consider the effect that such superspreading has on the estimation of the reproduction number and subsequent estimates of future cases. Accordingly, we employ a simple extension to models currently used in the literature to estimate the reproduction number and present a case-study of the progression of COVID-19 in Austria. Our models demonstrate that the estimation uncertainty of the reproduction number increases with superspreading and that this improves the performance of prediction intervals. In a simplified model, we derive a transparent formula that connects the extent of superspreading to the width of credible intervals for the reproduction number.

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We demonstrate the usefulness of submodularity in statistics as a characterization of the difficulty of the search problem of feature selection. The search problem is the ability of a procedure to identify an informative set of features as opposed to the performance of the optimal set of features. Submodularity arises naturally in this setting due to its connection to combinatorial optimization. In statistics, submodularity isolates cases in which collinearity makes the choice of model features difficult from those in which this task is routine. Researchers often report the signal-to-noise ratio to measure the difficulty of simulated data examples. A measure of submodularity should also be provided as it characterizes an independent component difficulty. Furthermore, it is closely related to other statistical assumptions used in the development of the Lasso, Dantzig selector, and sure information screening.