How to remove the testing bias in CoV-2 statistics

BACKGROUNDPublic health measures and private behaviour are based on reported numbers of SARS-CoV-2 infections. Some argue that testing influences the confirmed number of infections.OBJECTIVES/METHODSDo time series on reported infections and the number of tests allow one to draw conclusions about actual infection numbers? A SIR model is presented where the true numbers of susceptible, infectious and removed individuals are unobserved. Testing is also modelled.RESULTSOfficial confirmed infection numbers are likely to be biased and cannot be compared over time. The bias occurs because of different reasons for testing (e.g. by symptoms, representative or testing travellers). The paper illustrates the bias and works out the effect of the number of tests on the number of reported cases. The paper also shows that the positive rate (the ratio of positive tests to the total number of tests) is uninformative in the presence of non-representative testing.CONCLUSIONSA severity index for epidemics is proposed that is comparable over time. This index is based on Covid-19 cases and can be obtained if the reason for testing is known.

Should Contact Bans Have Been Lifted More in Germany?

Many countries consider the lifting of restrictions of social contacts (RSC). We quantify the effects of RSC for Germany. We initially employ a purely statistical approach to predicting prevalence of Covid-19 if RSC had been upheld after 20 April. We employ these findings and feed them into our theoretical model. We find that the peak of the number of sick individuals would have been reached already end of April. The number of sick individuals would have fallen below 1000 at the beginning of July. If restrictions had been lifted completely on April 20, the number of sick should have risen quickly again from around 27 April. A balance between economic and individual costs of RSC and public health objectives consists in lifting RSC for activities that have high economic benefits but low health costs. In the absence of large-scale representative testing of CoV-2 infections, these activities can most easily be identified if federal states of Germany adopted exit strategies that differ across states.

The Signicance of the Detection Ratio for Predictions on the Outcome of an Epidemic - A Message from Mathematical Modelers

In attempting to predict the further course of the novel coronavirus (COVID-19) pandemic caused by SARS-CoV-2, mathematical models of different types are frequently employed and calibrated to reported case numbers. Among the major challenges in interpreting these data is the uncertainty about the amount of undetected infections, or conversely: the detection ratio. As a result, some models include assumptions about the percentage of detected cases among total infections while others completely neglect undetected cases. Here, we illustrate how model projections about case and fatality numbers vary significantly under varying assumptions on the detection ratio. Uncertainties in model predictions can be significantly reduced by representative testing, both for antibodies and active virus RNA, to uncover past and current infections that have gone undetected thus far.

Real-world Independent Testing of e-ASPECTS Software (RITeS): statistical analysis plan

Background: Artificial intelligence-based software may automatically detect ischaemic stroke lesions and provide an Alberta Stroke Program Early CT score (ASPECTS) on CT, and identify arterial occlusion and provide a collateral score on CTA. Large-scale independent testing will inform clinical use, but is lacking. We aim to test e-ASPECTS and e-CTA (Brainomix, Oxford UK) using CT scans obtained from a range of clinical studies. Methods: Using prospectively collected baseline CT and CTA scans from 10 national/international clinical stroke trials or registries (total >6600 patients), we will select a large clinically representative sample for testing e-ASPECTS and e-CTA compared to previously acquired independent expert human interpretation (reference standard). Our primary aims are to test agreement between software-derived and masked human expert ASPECTS, and the diagnostic accuracy of e-ASPECTS for identifying all causes of stroke symptoms using follow-up imaging and final clinical opinion as diagnostic ground truth. Our secondary aims are to test when and why e-ASPECTS is more or less accurate, or succeeds/fails to produce results, agreement between e-CTA and human expert CTA interpretation, and repeatability of e-ASPECTS/e-CTA results. All testing will be conducted on an intention-to-analyse basis. We will assess agreement between software and expert-human ratings and test the diagnostic accuracy of software. Conclusions: RITeS will provide comprehensive, robust and representative testing of e-ASPECTS and e-CTA against the current gold-standard, expert-human interpretation.

Should contact bans be lifted in Germany? A quantitative prediction of its effects

Many countries consider the lifting of restrictions of social contacts (RSC). We quantify the effects of RSC for Germany. We initially employ a purely statistical approach to predicting prevalence of COVID19 if RSC were upheld after April 20. We employ these findings and feed them into our theoretical model. We find that the peak of the number of sick individuals would be reached already mid April. The number of sick individuals would fall below 1,000 at the beginning of July. When restrictions are lifted completely on April 20, the number of sick should rise quickly again from around April 27. A balance between economic and individual costs of RSC and public health objectives consists in lifting RSC for activities that have high economic benefits but low health costs. In the absence of large-scale representative testing of CoV-2 infections, these activities can most easily be identified if federal states of Germany adopted exit strategies that differ across states.

Investigations on the FSI Characteristic of a Scaled and Integral Fuel Structure Under Axial Flow

Hydraulic coupling effects are essential for the fuel assembly dynamic behavior in reactor operation and under accident conditions when the fuel structures are subjected to external excitations by core barrel motions or inlet mass flow variations. A representative testing in a flow test loop, thus, requires the consideration of several fuel assemblies in a row to assess the associated FSI effects in a relevant manner. Since the existing flow test facility at AREVA Erlangen is designed for testing of a single integral fuel structure, a scaling approach is applied to enable forthcoming flow tests in a row of scaled fuel structures. The validity of the derived scaling principles for the static and dynamic FSI characteristic is demonstrated via benchmarking flow tests between an integral bundle and a scaled down sub bundle structure of an AREVA fuel design. On an analytical level, it is shown that the implementation of a dynamic FSI coupled CFD model is capable to reproduce the dynamic behavior of the scaled fuel design in the flow tests in a quantitative and consistent fashion. This rigorous FSI modeling approach is based on the detailed flow test geometry without any adjustable parameters and, therefore, features predictive qualities for the development of simplified FSI approaches.