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Objective To review and critically appraise published and preprint reports of models that aim to predict either (i) presence of existing COVID-19 infection, (ii) future complications in individuals already diagnosed with COVID-19, or (iii) models to identify individuals at high risk for COVID-19 in the general population. Design Rapid systematic review and critical appraisal of prediction models for diagnosis or prognosis of COVID-19 infection. Data sources PubMed, EMBASE via Ovid, Arxiv, medRxiv and bioRxiv until 24 th March 2020. Study selection Studies that developed or validated a multivariable COVID-19 related prediction model. Two authors independently screened titles, abstracts and full text. Data extraction Data from included studies were extracted independently by at least two authors based on the CHARMS checklist, and risk of bias was assessed using PROBAST. Data were extracted on various domains including the participants, predictors, outcomes, data analysis, and prediction model performance. Results 2696 titles were screened. Of these, 27 studies describing 31 prediction models were included for data extraction and critical appraisal. We identified three models to predict hospital admission from pneumonia and other events (as a proxy for covid-19 pneumonia) in the general population; 18 diagnostic models to detect COVID-19 infection in symptomatic individuals (13 of which were machine learning utilising computed tomography (CT) results); and ten prognostic models for predicting mortality risk, progression to a severe state, or length of hospital stay. Only one of these studies used data on COVID-19 cases outside of China. Most reported predictors of presence of COVID-19 in suspected patients included age, body temperature, and signs and symptoms. Most reported predictors of severe prognosis in infected patients included age, sex, features derived from CT, C-reactive protein, lactic dehydrogenase, and lymphocyte count. Estimated C-index estimates for the prediction models ranged from 0.73 to 0.81 in those for the general population (reported for all 3 general population models), from 0.81 to > 0.99 in those for diagnosis (reported for 13 of the 18 diagnostic models), and from 0.85 to 0.98 in those for prognosis (reported for 6 of the 10 prognostic models). All studies were rated at high risk of bias, mostly because of non-representative selection of control patients, exclusion of patients who had not experienced the event of interest by the end of the study, and poor statistical analysis, including high risk of model overfitting. Reporting quality varied substantially between studies. A description of the study population and intended use of the models was absent in almost all reports, and calibration of predictions was rarely assessed. Conclusion COVID-19 related prediction models are quickly entering the academic literature, to support medical decision making at a time where this is urgently needed. Our review indicates proposed models are poorly reported and at high risk of bias. Thus, their reported performance is likely optimistic and using them to support medical decision making is not advised. We call for immediate sharing of the individual participant data from COVID-19 studies to support collaborative efforts in building more rigorously developed prediction models and validating (evaluating) existing models. The aforementioned predictors identified in multiple included studies could be considered as candidate predictors for new models. We also stress the need to follow methodological guidance when developing and validating prediction models, as unreliable predictions may cause more harm than benefit when used to guide clinical decisions. Finally, studies should adhere to the TRIPOD statement to facilitate validating, appraising, advocating and clinically using the reported models. Systematic review registration protocol osf.io/ehc47/ , registration: osf.io/wy245 Summary boxes What is already known on this topic - The sharp recent increase in COVID-19 infections has put a strain on healthcare systems worldwide, necessitating efficient early detection, diagnosis of patients suspected of the infection and prognostication of COVID-19 confirmed cases. - Viral nucleic acid testing and chest CT are standard methods for diagnosing COVID-19, but are time-consuming. - Earlier reports suggest that the elderly, patients with comorbidity (COPD, cardiovascular disease, hypertension), and patients presenting with dyspnoea are vulnerable to more severe morbidity and mortality after COVID-19 infection. What this study adds - We identified three models to predict hospital admission from pneumonia and other events (as a proxy for COVID-19 pneumonia) in the general population. - We identified 18 diagnostic models for COVID-19 detection in symptomatic patients. - 13 of these were machine learning models based on CT images. - We identified ten prognostic models for COVID-19 infected patients, of which six aimed to predict mortality risk in confirmed or suspected COVID-19 patients, two aimed to predict progression to a severe or critical state, and two aimed to predict a hospital stay of more than 10 days from admission. - Included studies were poorly reported compromising their subsequent appraisal, and recommendation for use in daily practice. All studies were appraised at high risk of bias, raising concern that the models may be flawed and perform poorly when applied in practice, such that their predictions may be unreliable. status: published ispartof: medRxiv

In the last few months, the COVID-19 pandemic has spread all across the world and is still dominating the headlines at this time. Notwithstanding global and regional differences, the basic elements of how we live have radically changed due to government prevention policies, the fear of contracting the virus and the actual spread of the disease. In this context, at CLEARING HOUSE, we realised that it was not possible to carry on our work and research as if nothing was happening. This is why we decided to adapt our work plan and during April we launched a new line of reflection and research with the objective to look at how the COVID-19 pandemic was changing both, our professional perspective and citizens’ attitude on green spaces and urban forests. In this blog, we bring together different stories looking at the issue from various angles (tag: COVID-19), and at the same time we present the findings of a survey. This post provides a general overview of the responses; in the next blog posts we will also provide details about specific groups of respondents; and at the same time we are working on a more elaborate analysis that will be published in the form of a scientific paper. Read more: http://clearinghouseproject.eu/2020/07/23/green-spaces-urban-forests-during-the-covid-19-pandemic/

SARS-CoV-2 entry into host cells is orchestrated by the spike (S) glycoprotein that contains an immunodominant receptor-binding domain (RBD) targeted by the largest fraction of neutralizing antibodies (Abs) in COVID-19 patient plasma. Little is known about neutralizing Abs binding to epitopes outside the RBD and their contribution to protection. Here, we describe 41 human monoclonal Abs (mAbs) derived from memory B cells, which recognize the SARS-CoV-2 S N-terminal domain (NTD) and show that a subset of them neutralize SARS-CoV-2 ultrapotently. We define an antigenic map of the SARS-CoV-2 NTD and identify a supersite recognized by all known NTD-specific neutralizing mAbs. These mAbs inhibit cell-to-cell fusion, activate effector functions, and protect Syrian hamsters from SARS-CoV-2 challenge. SARS-CoV-2 variants, including the 501Y.V2 and B.1.1.7 lineages, harbor frequent mutations localized in the NTD supersite suggesting ongoing selective pressure and the importance of NTD-specific neutralizing mAbs to protective immunity. ispartof: location:United States status: Published online ispartof: bioRxiv

Na weken lockdown is niets noemenswaardigs gebeurd om social distancing in de openbare ruimte te faciliteren. In dit artikel bespreken we hoe de veiligheidsmaatregelen in de strijd tegen covid-19 aanleiding geven tot een herverdeling van de publieke ruimte. Het artikel situeert enkele weerstanden tegen ruimtelijke maatregelen en hoe deze te overwinnen. ispartof: De Standaard issue:15 April 2020 pages:26-27 status: published

no abstract available