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197 Research products, page 1 of 20

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  • Open Access English
    Authors: 
    Brizzi, Andrea; Whittaker, Charles; Servo, Luciana MS; Hawryluk, Iwona; Prete, Carlos A; de Souza, William M; Aguiar, Renato S; Araujo, Leonardo JT; Bastos, Leonardo S; Blenkinsop, Alexandra; +31 more
    Country: Belgium

    The SARS-CoV-2 Gamma variant spread rapidly across Brazil, causing substantial infection and death waves. We use individual-level patient records following hospitalisation with suspected or confirmed COVID-19 to document the extensive shocks in hospital fatality rates that followed Gamma's spread across 14 state capitals, and in which more than half of hospitalised patients died over sustained time periods. We show that extensive fluctuations in COVID-19 in-hospital fatality rates also existed prior to Gamma's detection, and were largely transient after Gamma's detection, subsiding with hospital demand. Using a Bayesian fatality rate model, we find that the geographic and temporal fluctuations in Brazil's COVID-19 in-hospital fatality rates are primarily associated with geographic inequities and shortages in healthcare capacity. We project that approximately half of Brazil's COVID-19 deaths in hospitals could have been avoided without pre-pandemic geographic inequities and without pandemic healthcare pressure. Our results suggest that investments in healthcare resources, healthcare optimization, and pandemic preparedness are critical to minimize population wide mortality and morbidity caused by highly transmissible and deadly pathogens such as SARS-CoV-2, especially in low- and middle-income countries. NOTE: The following manuscript has appeared as 'Report 46 - Factors driving extensive spatial and temporal fluctuations in COVID-19 fatality rates in Brazilian hospitals' at https://spiral.imperial.ac.uk:8443/handle/10044/1/91875 . ONE SENTENCE SUMMARY: COVID-19 in-hospital fatality rates fluctuate dramatically in Brazil, and these fluctuations are primarily associated with geographic inequities and shortages in healthcare capacity. ispartof: medRxiv ispartof: medRxiv ispartof: location:United States status: Published online

  • Open Access English
    Authors: 
    Van Puyvelde, Bart; Van Uytfanghe, Katleen; Van Oudenhove, Laurence; Gabriels, Ralf; Van Royen, Tessa; Matthys, Arne; Razavi, Morteza; Yip, Richard; Pearson, Terry; van Hulle, Marijn; +11 more
    Country: Belgium

    INTRODUCTION The pandemic readiness toolbox needs to be extended, providing diagnostic tools that target different biomolecules, using orthogonal experimental setups and fit-for-purpose specification of detection. Here we build on a previous Cov-MS effort that used liquid chromatography-mass spectrometry (LC-MS) and describe a method that allows accurate, high throughput measurement of SARS-CoV-2 nucleocapsid (N) protein. MATERIALS and METHODS We used Stable Isotope Standards and Capture by Anti-Peptide Antibodies (SISCAPA) technology to enrich and quantify proteotypic peptides of the N protein from trypsin-digested samples from COVID-19 patients. RESULTS The Cov2MS assay was shown to be compatible with a variety of sample matrices including nasopharyngeal swabs, saliva and blood plasma and increased the sensitivity into the attomole range, up to a 1000-fold increase compared to direct detection in matrix. In addition, a strong positive correlation was observed between the SISCAPA antigen assay and qPCR detection beyond a quantification cycle (Cq) of 30-31, the level where no live virus can be cultured from patients. The automatable “addition only” sample preparation, digestion protocol, peptide enrichment and subsequent reduced dependency upon LC allow analysis of up to 500 samples per day per MS instrument. Importantly, peptide enrichment allowed detection of N protein in a pooled sample containing a single PCR positive sample mixed with 31 PCR negative samples, without loss in sensitivity. MS can easily be multiplexed and we also propose target peptides for Influenza A and B virus detection. CONCLUSIONS The Cov2MS assay described is agnostic with respect to the sample matrix or pooling strategy used for increasing throughput and can be easily multiplexed. Additionally, the assay eliminates interferences due to protein-protein interactions including those caused by anti-virus antibodies. The assay can be adapted to test for many different pathogens and could provide a tool enabling longitudinal epidemiological monitoring of large numbers of pathogens within a population, applied as an early warning system.

  • Other research product . Other ORP type . 2020
    Open Access English
    Authors: 
    Longman, Chia;
    Country: Belgium
  • Open Access English
    Authors: 
    Verschelden, Gil; Noeparast, Maxim; Noeparast, Maryam; Michel, Charlotte; Cotton, Frederic; Goyvaerts, Cleo; Hites, Maya;
    Country: Belgium

    Background SARS-CoV-2 is associated with significant mortality and morbidity in a subgroup of patients who develop cytokine releasing syndrome (CRS) and the related acute respiratory distress syndrome. Precedent evidence suggests that deficiency of the element zinc can be associated with similar complications as well as impaired antiviral response. Herein, beyond determining the zinc status, we explore the association between the plasma zinc concentration, the development of CRS, and the clinical outcomes in hospitalized COVID-19 patients. Methods We conducted a prospective, single-center, observational study in a tertiary university hospital (CUB-Hôpital Erasme, Brussels). Hospitalized adult patients with PCR-confirmed SARS-CoV-2 infection were enrolled within 72 hours of hospital admission. As a surrogate endpoint for CRS, we assessed the presence and severity of COVID-19-associated hyperinflammatory syndrome, using an additive six-point clinical scale (cHIS) that we independently validated in the current study. We defined the clinical outcomes as the length of hospitalization, the incidence of mechanical ventilation, and mortality. We recorded the outcomes with a follow-up of 90 days from hospital admission. Results One hundred and thirty-nine eligible patients were included between May 2020 and November 2020 (median age of 65 years [IQR, 54 to 77]). Our cohort’s mean plasma zinc concentration was 56.2 mcg/dL (standard deviation [SD], 14.8). The absolute majority of patients (96%) were zinc deficient (<80mcg/dL). The mean plasma zinc concentration was lower in patients with CRS (cHIS ≧ 2) compared to those without CRS (−5 mcg/dL; 95% CI, -10.5 to 0.051; p = 0.048). We observed that the plasma zinc concentration is weakly but significantly correlated with the length of hospital stay (rho = -0.19; p = 0.022). However, the plasma zinc concentration was not significantly associated with mortality or morbidity. Conclusions Markedly, an absolute majority of hospitalized COVID-19 patients are zinc deficient. We found no significant association between zinc plasma concentration and cHIS. We find a weak (reverse) correlation between plasma zinc concentration and the length of hospital stay, but not with mortality or morbidity. As such, our findings do not support the role of zinc as a robust prognostic factor among hospitalized COVID-19 patients. We encourage further studies to explore the role of zinc as a biomarker for assessing the risk of developing a tissue-damaging CRS and predicting outcomes in patients diagnosed with COVID-19.

  • Restricted English
    Authors: 
    Somville, Francis; Van Bogaert, P; Vercauteren, Leonie; De Cauwer, Harald; Mortelmans, Luc; Pauwels, Sofie; De Boeck, Lisanne; Franck, Eric;
    Country: Belgium
  • Open Access
    Authors: 
    Wynants, Laure; Van Calster, Ben; Bonten, Marc MJ; Collins, Gary; Debray, Thomas PA; De Vos, Maarten; Haller, Maria; Heinze, Georg; Moons, Karel GM; Riley, Richard; +6 more
    Publisher: Cold Spring Harbor Laboratory, BMJ and Yale University
    Country: Belgium

    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

  • Open Access English
    Authors: 
    Meyers, Eline; Deschepper, Ellen; Duysburgh, Els; De Rop, Liselore; Deburghgraeve, Tine; Van Ngoc, Pauline; Di Gregorio, Marina; Delogne, Simon; Coen, Anja; De Clercq, Nele; +7 more
    Publisher: Sciensano
    Country: Belgium
  • Other research product . Other ORP type . 2020
    Open Access
    Authors: 
    Boie, Gideon;
    Country: Belgium

    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

  • Open Access Dutch; Flemish
    Authors: 
    Van de Velde, Justine; Levecque, Katia; Valcke, Martin;
    Country: Belgium
  • Open Access
    Authors: 
    Decoster, André;
    Publisher: KU Leuven. Faculteit Economie en Bedrijfswetenschappen. Departement Economie
    Country: Belgium

    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

Advanced search in Research products
Research products
arrow_drop_down
Searching FieldsTerms
Any field
arrow_drop_down
includes
arrow_drop_down
Include:
The following results are related to COVID-19. Are you interested to view more results? Visit OpenAIRE - Explore.
197 Research products, page 1 of 20
  • Open Access English
    Authors: 
    Brizzi, Andrea; Whittaker, Charles; Servo, Luciana MS; Hawryluk, Iwona; Prete, Carlos A; de Souza, William M; Aguiar, Renato S; Araujo, Leonardo JT; Bastos, Leonardo S; Blenkinsop, Alexandra; +31 more
    Country: Belgium

    The SARS-CoV-2 Gamma variant spread rapidly across Brazil, causing substantial infection and death waves. We use individual-level patient records following hospitalisation with suspected or confirmed COVID-19 to document the extensive shocks in hospital fatality rates that followed Gamma's spread across 14 state capitals, and in which more than half of hospitalised patients died over sustained time periods. We show that extensive fluctuations in COVID-19 in-hospital fatality rates also existed prior to Gamma's detection, and were largely transient after Gamma's detection, subsiding with hospital demand. Using a Bayesian fatality rate model, we find that the geographic and temporal fluctuations in Brazil's COVID-19 in-hospital fatality rates are primarily associated with geographic inequities and shortages in healthcare capacity. We project that approximately half of Brazil's COVID-19 deaths in hospitals could have been avoided without pre-pandemic geographic inequities and without pandemic healthcare pressure. Our results suggest that investments in healthcare resources, healthcare optimization, and pandemic preparedness are critical to minimize population wide mortality and morbidity caused by highly transmissible and deadly pathogens such as SARS-CoV-2, especially in low- and middle-income countries. NOTE: The following manuscript has appeared as 'Report 46 - Factors driving extensive spatial and temporal fluctuations in COVID-19 fatality rates in Brazilian hospitals' at https://spiral.imperial.ac.uk:8443/handle/10044/1/91875 . ONE SENTENCE SUMMARY: COVID-19 in-hospital fatality rates fluctuate dramatically in Brazil, and these fluctuations are primarily associated with geographic inequities and shortages in healthcare capacity. ispartof: medRxiv ispartof: medRxiv ispartof: location:United States status: Published online

  • Open Access English
    Authors: 
    Van Puyvelde, Bart; Van Uytfanghe, Katleen; Van Oudenhove, Laurence; Gabriels, Ralf; Van Royen, Tessa; Matthys, Arne; Razavi, Morteza; Yip, Richard; Pearson, Terry; van Hulle, Marijn; +11 more
    Country: Belgium

    INTRODUCTION The pandemic readiness toolbox needs to be extended, providing diagnostic tools that target different biomolecules, using orthogonal experimental setups and fit-for-purpose specification of detection. Here we build on a previous Cov-MS effort that used liquid chromatography-mass spectrometry (LC-MS) and describe a method that allows accurate, high throughput measurement of SARS-CoV-2 nucleocapsid (N) protein. MATERIALS and METHODS We used Stable Isotope Standards and Capture by Anti-Peptide Antibodies (SISCAPA) technology to enrich and quantify proteotypic peptides of the N protein from trypsin-digested samples from COVID-19 patients. RESULTS The Cov2MS assay was shown to be compatible with a variety of sample matrices including nasopharyngeal swabs, saliva and blood plasma and increased the sensitivity into the attomole range, up to a 1000-fold increase compared to direct detection in matrix. In addition, a strong positive correlation was observed between the SISCAPA antigen assay and qPCR detection beyond a quantification cycle (Cq) of 30-31, the level where no live virus can be cultured from patients. The automatable “addition only” sample preparation, digestion protocol, peptide enrichment and subsequent reduced dependency upon LC allow analysis of up to 500 samples per day per MS instrument. Importantly, peptide enrichment allowed detection of N protein in a pooled sample containing a single PCR positive sample mixed with 31 PCR negative samples, without loss in sensitivity. MS can easily be multiplexed and we also propose target peptides for Influenza A and B virus detection. CONCLUSIONS The Cov2MS assay described is agnostic with respect to the sample matrix or pooling strategy used for increasing throughput and can be easily multiplexed. Additionally, the assay eliminates interferences due to protein-protein interactions including those caused by anti-virus antibodies. The assay can be adapted to test for many different pathogens and could provide a tool enabling longitudinal epidemiological monitoring of large numbers of pathogens within a population, applied as an early warning system.

  • Other research product . Other ORP type . 2020
    Open Access English
    Authors: 
    Longman, Chia;
    Country: Belgium
  • Open Access English
    Authors: 
    Verschelden, Gil; Noeparast, Maxim; Noeparast, Maryam; Michel, Charlotte; Cotton, Frederic; Goyvaerts, Cleo; Hites, Maya;
    Country: Belgium

    Background SARS-CoV-2 is associated with significant mortality and morbidity in a subgroup of patients who develop cytokine releasing syndrome (CRS) and the related acute respiratory distress syndrome. Precedent evidence suggests that deficiency of the element zinc can be associated with similar complications as well as impaired antiviral response. Herein, beyond determining the zinc status, we explore the association between the plasma zinc concentration, the development of CRS, and the clinical outcomes in hospitalized COVID-19 patients. Methods We conducted a prospective, single-center, observational study in a tertiary university hospital (CUB-Hôpital Erasme, Brussels). Hospitalized adult patients with PCR-confirmed SARS-CoV-2 infection were enrolled within 72 hours of hospital admission. As a surrogate endpoint for CRS, we assessed the presence and severity of COVID-19-associated hyperinflammatory syndrome, using an additive six-point clinical scale (cHIS) that we independently validated in the current study. We defined the clinical outcomes as the length of hospitalization, the incidence of mechanical ventilation, and mortality. We recorded the outcomes with a follow-up of 90 days from hospital admission. Results One hundred and thirty-nine eligible patients were included between May 2020 and November 2020 (median age of 65 years [IQR, 54 to 77]). Our cohort’s mean plasma zinc concentration was 56.2 mcg/dL (standard deviation [SD], 14.8). The absolute majority of patients (96%) were zinc deficient (<80mcg/dL). The mean plasma zinc concentration was lower in patients with CRS (cHIS ≧ 2) compared to those without CRS (−5 mcg/dL; 95% CI, -10.5 to 0.051; p = 0.048). We observed that the plasma zinc concentration is weakly but significantly correlated with the length of hospital stay (rho = -0.19; p = 0.022). However, the plasma zinc concentration was not significantly associated with mortality or morbidity. Conclusions Markedly, an absolute majority of hospitalized COVID-19 patients are zinc deficient. We found no significant association between zinc plasma concentration and cHIS. We find a weak (reverse) correlation between plasma zinc concentration and the length of hospital stay, but not with mortality or morbidity. As such, our findings do not support the role of zinc as a robust prognostic factor among hospitalized COVID-19 patients. We encourage further studies to explore the role of zinc as a biomarker for assessing the risk of developing a tissue-damaging CRS and predicting outcomes in patients diagnosed with COVID-19.

  • Restricted English
    Authors: 
    Somville, Francis; Van Bogaert, P; Vercauteren, Leonie; De Cauwer, Harald; Mortelmans, Luc; Pauwels, Sofie; De Boeck, Lisanne; Franck, Eric;
    Country: Belgium
  • Open Access
    Authors: 
    Wynants, Laure; Van Calster, Ben; Bonten, Marc MJ; Collins, Gary; Debray, Thomas PA; De Vos, Maarten; Haller, Maria; Heinze, Georg; Moons, Karel GM; Riley, Richard; +6 more
    Publisher: Cold Spring Harbor Laboratory, BMJ and Yale University
    Country: Belgium

    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

  • Open Access English
    Authors: 
    Meyers, Eline; Deschepper, Ellen; Duysburgh, Els; De Rop, Liselore; Deburghgraeve, Tine; Van Ngoc, Pauline; Di Gregorio, Marina; Delogne, Simon; Coen, Anja; De Clercq, Nele; +7 more
    Publisher: Sciensano
    Country: Belgium
  • Other research product . Other ORP type . 2020
    Open Access
    Authors: 
    Boie, Gideon;
    Country: Belgium

    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

  • Open Access Dutch; Flemish
    Authors: 
    Van de Velde, Justine; Levecque, Katia; Valcke, Martin;
    Country: Belgium
  • Open Access
    Authors: 
    Decoster, André;
    Publisher: KU Leuven. Faculteit Economie en Bedrijfswetenschappen. Departement Economie
    Country: Belgium

    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