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Preprint in English | medRxiv | ID: ppmedrxiv-21256133


BackgroundIn the 2nd year of the Covid-19 pandemic, knowledge about the dynamics of the infection in the general population is still limited. Such information is essential for health planners, as many of those infected show no or only mild symptoms and thus, escape the surveillance system. We therefore aimed to describe the course of the pandemic in the Munich general population living in private households from April 2020 to January 2021. MethodsThe KoCo19 baseline study took place from April to June 2020 including 5313 participants (age 14 years and above). From November 2020 to January 2021, we could again measure SARS-CoV-2 antibody status in 4,433 of the baseline participants (response 83%). Participants were offered a self-sampling kit to take a capillary blood sample (dry blood spot; DBS). Blood was analysed using the Elecsys(R) Anti-SARS-CoV-2 assay (Roche). Questionnaire information on socio-demographics and potential risk factors assessed at baseline was available for all participants. In addition, follow-up information on health-risk taking behaviour and number of personal contacts outside the household (N=2768) as well as leisure time activities (N=1263) were collected in summer 2020. ResultsWeighted and adjusted (for specificity and sensitivity) SARS-CoV-2 sero-prevalence at follow-up was 3.6% (95% CI 2.9-4.3%) as compared to 1.8% (95% CI 1.3-3.4%) at baseline. 91% of those tested positive at baseline were also antibody-positive at follow-up. While sero-prevalence increased from early November 2021 to January 2021, no indication of geospatial clustering across the city of Munich was found, although cases clustered within households. Taking baseline result and time to follow-up into account, men and participants in the age group 20-34 years were at the highest risk of sero-positivity. In the sensitivity analyses, differences in health-risk taking behaviour, number of personal contacts and leisure time activities partly explained these differences. ConclusionThe number of citizens in Munich with SARS-CoV-2 antibodies was still below 5% during the 2nd wave of the pandemic. Antibodies remained present in the majority of baseline participants. Besides age and sex, potentially confounded by differences in behaviour, no major risk factors could be identified. Non-pharmaceutical public health measures are thus still important.

Preprint in English | medRxiv | ID: ppmedrxiv-21249735


BackgroundSerosurveys are essential to understand SARS-CoV-2 exposure and enable population-level surveillance, but currently available tests need further in-depth evaluation. We aimed to identify testing-strategies by comparing seven seroassays in a population-based cohort. MethodsWe analysed 6,658 samples consisting of true-positives (n=193), true-negatives (n=1,091), and specimens of unknown status (n=5,374). For primary testing, we used Euroimmun-Anti-SARS-CoV-2-ELISA-IgA/IgG and Roche-Elecsys-Anti-SARS-CoV-2; and virus-neutralisation, GeneScript(R)cPass, VIRAMED-SARS-CoV-2-ViraChip(R), and Mikrogen-recomLine-SARS-CoV-2-IgG, including common-cold CoVs, for confirmatory testing. Statistical modelling generated optimised assay cut-off-thresholds. FindingsSensitivity of Euroimmun-anti-S1-IgA was 64.8%, specificity 93.3%; for Euroimmun-anti-S1-IgG, sensitivity was 77.2/79.8% (manufacturers/optimised cut-offs), specificity 98.0/97.8%; Roche-anti-N sensitivity was 85.5/88.6%, specificity 99.8/99.7%. In true-positives, mean and median titres remained stable for at least 90-120 days after RT-PCR-positivity. Of true-positives with positive RT-PCR (<30 days), 6.7% did not mount detectable seroresponses. Virus-neutralisation was 73.8% sensitive, 100.0% specific (1:10 dilution). Neutralisation surrogate tests (GeneScript(R)cPass, Mikrogen-recomLine-RBD) were >94.9% sensitive, >98.1% specific. Seasonality had limited effects; cross-reactivity with common-cold CoVs 229E and NL63 in SARS-CoV-2 true-positives was significant. ConclusionOptimised cut-offs improved test performances of several tests. Non-reactive serology in true-positives was uncommon. For epidemiological purposes, confirmatory testing with virus-neutralisation may be replaced with GeneScript(R)cPass or recomLine-RBD. Head-to-head comparisons given here aim to contribute to the refinement of testing-strategies for individual and public health use.

Marek Ostaszewski; Anna Niarakis; Alexander Mazein; Inna Kuperstein; Robert Phair; Aurelio Orta-Resendiz; Vidisha Singh; Sara Sadat Aghamiri; Marcio Luis Acencio; Enrico Glaab; Andreas Ruepp; Gisela Fobo; Corinna Montrone; Barbara Brauner; Goar Frishman; Julia Somers; Matti Hoch; Shailendra Kumar Gupta; Julia Scheel; Hanna Borlinghaus; Tobias Czauderna; Falk Schreiber; Arnau Montagud; Miguel Ponce de Leon; Akira Funahashi; Yusuke Hiki; Noriko Hiroi; Takahiro G Yamada; Andreas Drager; Alina Renz; Muhammad Naveez; Zsolt Bocskei; Daniela Bornigen; Liam Fergusson; Marta Conti; Marius Rameil; Vanessa Nakonecnij; Jakob Vanhoefer; Leonard Schmiester; Muying Wang; Emily E Ackerman; Jason E Shoemaker; Jeremy Zucker; Kristie L Oxford; Jeremy Teuton; Ebru Kocakaya; Gokce Yagmur Summak; Kristina Hanspers; Martina Kutmon; Susan Coort; Lars Eijssen; Friederike Ehrhart; Rex D. A. B.; Denise Slenter; Marvin Martens; Nhung Pham; Robin Haw; Bijay Jassal; Lisa Matthews; Marija Orlic-Milacic; Andrea Senff-Ribeiro; Karen Rothfels; Veronica Shamovsky; Ralf Stephan; Cristoffer Sevilla; Thawfeek Mohamed Varusai; Jean-Marie Ravel; Vera Ortseifen; Silvia Marchesi; Piotr Gawron; Ewa Smula; Laurent Heirendt; Venkata Satagopam; Guanming Wu; Anders Riutta; Martin Golebiewski; Stuart Owen; Carole Goble; Xiaoming Hu; Rupert Overall; Dieter Maier; Angela Bauch; Benjamin M Gyori; John A Bachman; Carlos Vega; Valentin Groues; Miguel Vazquez; Pablo Porras; Luana Licata; Marta Iannuccelli; Francesca Sacco; Denes Turei; Augustin Luna; Ozgun Babur; Sylvain Soliman; Alberto Valdeolivas; Marina Esteban-Medina; Maria Pena-Chilet; Kinza Rian; Tomas Helikar; Bhanwar Lal Puniya; Anastasia Nesterova; Anton Yuryev; Anita de Waard; Dezso Modos; Agatha Treveil; Marton Laszlo Olbei; Bertrand De Meulder; Aurelien Naldi; Aurelien Dugourd; Laurence Calzone; Chris Sander; Emek Demir; Tamas Korcsmaros; Tom C Freeman; Franck Auge; Jacques S Beckmann; Jan Hasenauer; Olaf Wolkenhauer; Egon Willighagen; Alexander R Pico; Chris Evelo; Lincoln D Stein; Henning Hermjakob; Julio Saez-Rodriguez; Joaquin Dopazo; Alfonso Valencia; Hiroaki Kitano; Emmanuel Barillot; Charles Auffray; Rudi Balling; Reinhard Schneider; - the COVID-19 Disease Map Community.
Preprint in English | bioRxiv | ID: ppbiorxiv-356014


We describe a large-scale community effort to build an open-access, interoperable, and computable repository of COVID-19 molecular mechanisms - the COVID-19 Disease Map. We discuss the tools, platforms, and guidelines necessary for the distributed development of its contents by a multi-faceted community of biocurators, domain experts, bioinformaticians, and computational biologists. We highlight the role of relevant databases and text mining approaches in enrichment and validation of the curated mechanisms. We describe the contents of the Map and their relevance to the molecular pathophysiology of COVID-19 and the analytical and computational modelling approaches that can be applied for mechanistic data interpretation and predictions. We conclude by demonstrating concrete applications of our work through several use cases and highlight new testable hypotheses.

Preprint in English | medRxiv | ID: ppmedrxiv-20071597


Epidemiological models are widely used to analyse the spread of diseases such as the global COVID-19 pandemic caused by SARS-CoV-2. However, all models are based on simplifying assumptions and on sparse data. This limits the reliability of parameter estimates and predictions. In this manuscript, we demonstrate the relevance of these limitations by performing a study of the COVID-19 outbreak in Wuhan, China. We perform parameter estimation, uncertainty analysis and model selection for a range of established epidemiological models. Amongst others, we employ Markov chain Monte Carlo sampling, parameter and prediction profile calculation algorithms. Our results show that parameter estimates and predictions obtained for several established models on the basis of reported case numbers can be subject to substantial uncertainty. More importantly, estimates were often unrealistic and the confidence / credibility intervals did not cover plausible values of critical parameters obtained using different approaches. These findings suggest, amongst others, that several models are oversimplistic and that the reported case numbers provide often insufficient information.