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De economische schok veroorzaakt door de CORONA-crisis is ongezien, onwezenlijk en dramatisch. We wagen ons niet aan een voorspelling van de macro-economische gevolgen. We vertalen gegeven ramingen van de inzinking van de bbp-groei in een impact op de overheidsfinanciën. Daaruit blijkt dat het tijdelijk karakter van deze crisis en de herneming van de economische groei cruciaal zijn. Als de groei volgend jaar herneemt, dan is de factuur van steunmaatregelen niet onbetaalbaar. De lange termijn determinanten van houdbare overheidsfinanciën zijn niet veranderd: ontvangsten die de verwachte groei van de primaire uitgaven dekken. ispartof: Leuvense Economische Standpunten vol:2020 issue:176 pages:1-7 status: published

ispartof: European Society for Child and Adolescent Psychiatry (ESCAP) Conference 2022 location:Maastricht, the Netherlands status: published

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: bioRxiv ispartof: bioRxiv ispartof: location:United States status: Published online

ispartof: The European Academy of Occupational Health Psychology (EAOHP) location:online date:2 Sep - 4 Sep 2020 status: published

Twee jaar geleden dook in Wuhan het Sars-Cov-2 virus op. Het overspoelde in een mum van tijd de wereld. Half maart 2020 besliste de Belgische regering om een lockdown af te kondigen. Een begrip dat tot kort daarvoor letterlijk onvoorstelbaar was. In landen over de hele wereld werd een noodtoestand afgekondigd die overheden ongeziene slagkracht gaf om ingrijpende beslissingen te nemen. Zonder daarbij de geijkte democratische procedures te moeten volgen. Het onbekende virus en de angstaanjagende beelden die de wereld rondgingen, hielden hele bevolkingen aan het scherm gekluisterd, terwijl nieuwsuitzendingen zich beperkten tot één item: het nieuwe coronavirus en de daaraan gerelateerde cijfers, statistieken en beslissingen. Angst regeerde het land en beslissingen moesten worden genomen onder grote druk en onzekerheid. Dat het gevoerd beleid er een was van vallen en opstaan is in die omstandigheden volledig te begrijpen. Maar de onbekendheid van het virus maakte plaats voor een nooit geziene hoeveelheid wetenschappelijke publicaties – we naderen de 1 miljoen peer reviewed papers . Merkwaardig genoeg ontbreekt nu een grondige reflectie van het gevoerde beleid van de afgelopen twee jaar Two years ago, the Sars-Cov-2 virus surfaced in Wuhan. It engulfed the world in no time. In mid-March 2020, the Belgian government decided to declare a lockdown. A concept that was literally unimaginable until recently. In countries all over the world a state of emergency was declared that gave governments unprecedented power to take far-reaching decisions. Without having to follow the usual democratic procedures. The unknown virus and the terrifying images that circulated around the world kept entire populations glued to the screen, while news broadcasts were limited to one item: the new coronavirus and the related figures, statistics and decisions. Fear ruled the country and decisions had to be made under great pressure and uncertainty. That the policy pursued was one of trial and error is entirely understandable in these circumstances. But the obscurity of the virus gave way to an unprecedented number of scientific publications - we are approaching 1 million peer reviewed papers. Strangely enough, a thorough reflection of the policies pursued over the past two years is now missing.

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. ispartof: medRxiv ispartof: medRxiv status: published

ispartof: pages:S305-S306 ispartof: SAFETY AND HEALTH AT WORK vol:13 pages:S305-S306 status: published