Corpus overview


Overview

MeSH Disease

Human Phenotype

Transmission

Seroprevalence
    displaying 1 - 10 records in total 87
    records per page




    Predicting Critical State after COVID-19 Diagnosis Using Real-World Data from 20152 Confirmed US Cases TRANS

    Authors: Mike Domenik Rinderknecht; Yannick Klopfenstein

    doi:10.1101/2020.07.24.20155192 Date: 2020-07-27 Source: medRxiv

    The global COVID-19 pandemic caused by the virus SARS-CoV-2 has led to over 10 million confirmed cases TRANS, half a million deaths MESHD, and is challenging healthcare systems worldwide. With limited medical resources, early identification of patients with a high risk of progression to severe disease MESHD or a critical state is crucial. We present a prognostic model predicting critical state within 28 days following COVID-19 diagnosis trained on data from US electronic health records (EHR) within IBM Explorys, including demographics, comorbidities, symptoms, laboratory test results, insurance types, and hospitalization. Our entire cohort included 20152 COVID-19 cases, of which 3160 patients went into critical state or died. Random, stratified train-test splits were repeated 100 times to obtain a distribution of performance SERO. The median and interquartile range of the areas under the receiver operating characteristic curve (ROC AUC) and the precision recall SERO curve (PR AUC) were 0.863 [0.857, 0.866] and 0.539 [0.526, 0.550], respectively. Optimizing the decision threshold lead to a sensitivity SERO of 0.796 [0.775, 0.821] and a specificity of 0.784 [0.769, 0.805]. Good model calibration was achieved, showing only minor tendency to over-forecast probabilities above 0.6. The validity of the model was demonstrated by the interpretability analysis confirming existing evidence on major risk factors (e.g., higher age TRANS and weight, male TRANS gender TRANS, diabetes, cardiovascular disease MESHD disease, and chronic kidney HP kidney disease MESHD). The analysis also revealed higher risk for African Americans and "self-pay patients". To the best of our knowledge, this is the largest dataset based on EHR used to create a prognosis model for COVID-19. In contrast to large-scale statistics computing odds ratios for individual risk factors, the present model combining a rich set of covariates can provide accurate personalized predictions enabling early treatment to prevent patients from progressing to a severe or critical state.

    Point-of-care ultrasound for COVID-19 pneumonia MESHD pneumonia HP patients in the ICU

    Authors: zouheir bitar; Mohammed Shamsah; Omar Bamasood; Ossama Maadrani; Huda Al foudri

    doi:10.21203/rs.3.rs-49196/v1 Date: 2020-07-26 Source: ResearchSquare

    BackgroundPoint-of-care ultrasound (POCUS) has a major role in the management of patients with acute hypoxic respiratory and circulatory failure and guides hemodynamic management. There is scarce literature on POCUS assessment characteristics in COVID-19 pneumonia MESHD pneumonia HP with hypoxic respiratory failure HP.MethodsThe study is an observational, prospective, single‐center study conducted in the intensive care unit of Adan General Hospital from May 1st, 2020, to June 25, 2020. The study included adults TRANS suspected to have COVID-19 transferred to the intensive care unit (ICU) with fever MESHD fever HP or suspected respiratory infection MESHD. Patients were transferred to the ICU directly from the ED or general medical wards after reverse transcriptase-polymerase chain reaction (RT-PCR) testing. A certified intensivist in critical care ultrasound who was blinded to the RT-PCR results, if available at the time of examination, performed the lung ultrasound and echocardiology within 12 hours of the patient’s admission to the ICU. We calculated the E/e’, E/A ratio, left ventricular ejection fraction EF, IVC diameter, RV size and systolic function. We performed ultrasound in 12 chest areas.ResultsOf 92 patients with suspected COVID-19 pneumonia MESHD pneumonia HP, 77 (84%) cases were confirmed TRANS. The median age TRANS of the patients was 53 (82-36) years, and 71 (77%) were men.In the group of patients with confirmed COVID-19 pneumonia MESHD pneumonia HP, echocardiographic findings showed normal E/e’, deceleration time (DT), and transmittal E/A ratio in comparison to the non-COVID19 patients (P .001 for both). The IVC diameter was <2 cm with > 50% collapsibility in 62 (81%) patients with COVID-19 pneumonia MESHD pneumonia HP; a diameter of > 2 cm and < 50% collapsibility in all patients, with a P value of 0.001, was detected among those with non-COVID-19 pneumonia MESHD pneumonia HP. There were 3 cases of myocarditis MESHD myocarditis HP with poor EF (5.5%), severe RV dysfunction was seen in 9 cases (11.6%), and 3 cases showed RV thrombus.Chest US revealed four signs suggestive of COVID-19 pneumonia MESHD pneumonia HP in 77 patients (98.6%) ( sensitivity SERO 96.9%, CI 85%‐99.5%) when compared with RT-PCR results.ConclusionPOCUS plays an important role in bedside diagnosis, hemodynamic assessment and management of patients with acute hypoxic respiratory and circulatory failure in patients with COVID-19 pneumonia MESHD pneumonia HP.

    A Partially Observable MDP Approach for Sequential Testing for Infectious Diseases MESHD such as COVID-19

    Authors: Rahul Singh; Fang Liu; Ness B. Shroff

    id:2007.13023v1 Date: 2020-07-25 Source: arXiv

    The outbreak of the novel coronavirus (COVID-19) is unfolding as a major international crisis whose influence extends to every aspect of our daily lives. Effective testing allows infected individuals to be quarantined, thus reducing the spread of COVID-19, saving countless lives, and helping to restart the economy safely and securely. Developing a good testing strategy can be greatly aided by contact tracing TRANS that provides health care providers information about the whereabouts of infected patients in order to determine whom to test. Countries that have been more successful in corralling the virus typically use a ``test, treat, trace TRANS, test'' strategy that begins with testing individuals with symptoms, traces contacts TRANS of positively tested individuals via a combinations of patient memory, apps, WiFi, GPS, etc., followed by testing their contacts, and repeating this procedure. The problem is that such strategies are myopic and do not efficiently use the testing resources. This is especially the case with COVID-19, where symptoms may show up several days after the infection MESHD (or not at all, there is evidence to suggest that many COVID-19 carriers TRANS are asymptotic TRANS, but may spread the virus). Such greedy strategies, miss out population areas where the virus may be dormant and flare up in the future. In this paper, we show that the testing problem can be cast as a sequential learning-based resource allocation problem with constraints, where the input to the problem is provided by a time-varying social contact graph obtained through various contact tracing TRANS tools. We then develop efficient learning strategies that minimize the number of infected individuals. These strategies are based on policy iteration and look-ahead rules. We investigate fundamental performance SERO bounds, and ensure that our solution is robust to errors in the input graph as well as in the tests themselves.

    Backtesting the predictability of COVID-19

    Authors: Dmitry Gordeev; Philipp Singer; Marios Michailidis; Mathias Müller; SriSatish Ambati

    id:2007.11411v1 Date: 2020-07-22 Source: arXiv

    The advent of the COVID-19 pandemic has instigated unprecedented changes in many countries around the globe, putting a significant burden on the health sectors, affecting the macro economic conditions, and altering social interactions HP social interactions TRANS amongst the population. In response, the academic community has produced multiple forecasting models, approaches and algorithms to best predict the different indicators of COVID-19, such as the number of confirmed infected cases. Yet, researchers had little to no historical information about the pandemic at their disposal in order to inform their forecasting methods. Our work studies the predictive performance SERO of models at various stages of the pandemic to better understand their fundamental uncertainty and the impact of data availability on such forecasts. We use historical data of COVID-19 infections MESHD from 253 regions from the period of 22nd January 2020 until 22nd June 2020 to predict, through a rolling window backtesting framework, the cumulative number of infected cases for the next 7 and 28 days. We implement three simple models to track the root mean squared logarithmic error in this 6-month span, a baseline model that always predicts the last known value of the cumulative confirmed cases TRANS, a power growth model and an epidemiological model called SEIRD. Prediction errors are substantially higher in early stages of the pandemic, resulting from limited data. Throughout the course of the pandemic, errors regress slowly, but steadily. The more confirmed cases TRANS a country exhibits at any point in time, the lower the error in forecasting future confirmed cases TRANS. We emphasize the significance of having a rigorous backtesting framework to accurately assess the predictive power of such models at any point in time during the outbreak which in turn can be used to assign the right level of certainty to these forecasts and facilitate better planning.

    Short-term forecasting COVID-19 cumulative confirmed cases TRANS: Perspectives for Brazil

    Authors: Matheus Henrique Dal Molin Ribeiro; Ramon Gomes da Silva; Viviana Cocco Mariani; Leandro dos Santos Coelho

    id:2007.12261v1 Date: 2020-07-21 Source: arXiv

    The new Coronavirus (COVID-19) is an emerging disease MESHD responsible for infecting millions of people since the first notification until nowadays. Developing efficient short-term forecasting models allow knowing the number of future cases. In this context, it is possible to develop strategic planning in the public health system to avoid deaths MESHD. In this paper, autoregressive integrated moving average (ARIMA), cubist (CUBIST), random forest (RF), ridge regression (RIDGE), support vector regression (SVR), and stacking-ensemble learning are evaluated in the task of time series forecasting with one, three, and six-days ahead the COVID-19 cumulative confirmed cases TRANS in ten Brazilian states with a high daily incidence. In the stacking learning approach, the cubist, RF, RIDGE, and SVR models are adopted as base-learners and Gaussian process (GP) as meta-learner. The models' effectiveness is evaluated based on the improvement index, mean absolute error, and symmetric mean absolute percentage error criteria. In most of the cases, the SVR and stacking ensemble learning reach a better performance SERO regarding adopted criteria than compared models. In general, the developed models can generate accurate forecasting, achieving errors in a range of 0.87% - 3.51%, 1.02% - 5.63%, and 0.95% - 6.90% in one, three, and six-days-ahead, respectively. The ranking of models in all scenarios is SVR, stacking ensemble learning, ARIMA, CUBIST, RIDGE, and RF models. The use of evaluated models is recommended to forecasting and monitor the ongoing growth of COVID-19 cases, once these models can assist the managers in the decision-making support systems.

    Winter is Coming: A Southern Hemisphere Perspective of the Environmental Drivers of SARS-CoV-2 and the Potential Seasonality of COVID-19

    Authors: Albertus Smit; Jennifer Fitchett; Francois Engelbrecht; Robert Scholes; Godfrey Dzhivhuho; Neville Sweijd

    id:10.20944/preprints202007.0456.v1 Date: 2020-07-20 Source: Preprints.org

    SARS-CoV-2 virus infections MESHD in humans were first reported in December 2019, the boreal winter. The resulting COVID-19 pandemic was declared by the WHO in March 2020. By July 2020 COVID-19 is present in 213 countries and territories, with over 12 million confirmed cases TRANS and over half a million attributed deaths MESHD. Knowledge of other viral respiratory diseases MESHD suggests that the transmission TRANS of SARS-CoV-2 could be modulated by seasonally-varying environmental factors such as temperature and humidity. Many studies on the environmental sensitivity SERO of COVID-19 are appearing online, and some have been published in peer-reviewed journals. Initially, these studies raised the hypothesis that climatic conditions would subdue the viral transmission TRANS rate in places entering the boreal summer and that southern hemisphere countries would experience enhanced disease MESHD. For the latter, the COVID-19 peak would coincide with the peak of the influenza season, increasing misdiagnosis and placing an additional burden on health systems. In this review, we assess the evidence that environmental drivers are a significant factor in the trajectory of the COVID-19 pandemic, globally and regionally. We critically assessed 42 peer-reviewed and 80 preprint publications that met qualifying criteria. Since the disease MESHD has been prevalent for only half a year in the northern, and a quarter of a year in the southern hemisphere, datasets capturing a full seasonal cycle in one locality are not yet available. Analyses based on space-for-time substitutions, i.e. using data from climatically distinct locations as a surrogate for seasonal progression, have been inconclusive. The reported studies present a strong northern bias. Socio-economic conditions peculiar to the ‘Global South’ have been omitted as confounding variables, thereby weakening evidence of environmental signals. We explore why research to date has failed to show convincing evidence for environmental modulation of COVID-19, and discuss directions for future research. We conclude that the evidence thus far suggests a weak modulation effect, currently overwhelmed by the scale and rate of the spread of COVID-19. Seasonally-modulated transmission TRANS, if it exists, will be more evident in 2021 and subsequent years.

    RT-PCR testing to detect a COVID-19 outbreak in Austria: rapid, accurate and early diagnosis in primary care (The REAP study)

    Authors: Werner Leber; Oliver Lammel; Monika Redlberger-Fritz; Maria Elisabeth Mustafa-Korninger; Karin Stiasny; Reingard Christina Glehr; Eva-Maria Hochstrasser; Christian Hoellinger; Andrea Siebenhofer; Chris Griffiths; Jasmina Panovska-Griffiths

    doi:10.1101/2020.07.13.20152439 Date: 2020-07-15 Source: medRxiv

    Background Delay in COVID-19 detection has led to a major pandemic. We report rapid early detection of SARS-CoV-2 by reverse transcriptase-polymerase chain reaction (RT-PCR), comparing it to the serostatus of convalescent infection MESHD, at an Austrian National Sentinel Surveillance Practice in an isolated ski-resort serving a population of 22,829 people. Methods Retrospective dataset of all 73 patients presenting with mild to moderate flu-like symptoms to a sentinel practice in the ski-resort of Schladming-Dachstein, Austria, between 24 February and 03 April, 2020. We split the outbreak in two halves, by dividing the period from the first to the last case by two, to characterise the following three cohorts of patients with confirmed infection TRANS infection MESHD: people with reactive RT-PCR presenting during the first half (early acute infection MESHD) vs. those presenting in the second half (late acute), and people with non-reactive RT-PCR (late convalescent). For each cohort we report the number of cases detected, the accuracy of RT-PCR and the duration of symptoms. We also report multivariate regression of 15 clinical symptoms as covariates, comparing all people with convalescent infection MESHD to those with acute infection MESHD. Findings All 73 patients had SARS-CoV-2 RT-PCR testing. 22 patients were diagnosed with COVID-19, comprising: 8 patients presenting early acute, and 7 presenting late acute and 7 late convalescent respectively; 44 patients tested SARS-COV-2 negative, and 7 were excluded. RT-PCR sensitivity SERO was high (100%) among acute presenters, but dropped to 50% in the second half of the outbreak; specificity was 100%. The mean duration of symptoms was 2 days (range 1-4) among early acute presenters, and 4.4 days (1-7) among late acute and 8 days (2-12) among late convalescent presenters respectively. Convalescent infection MESHD was only associated with loss of taste (ORs=6.02;p=0.047). Acute infection MESHD was associated with loss of taste (OR=571.72;p=0.029), nausea MESHD nausea and vomiting HP and vomiting MESHD (OR=370.11;p=0.018), breathlessness (OR=134.46;p=0.049), and myalgia MESHD myalgia HP (OR=121.82;p=0.032); but not loss of smell, fever MESHD fever HP or cough MESHD cough HP. Interpretation RT-PCR rapidly and reliably detects early COVID-19 among people presenting with viral illness and multiple symptoms in primary care, particularly during the early phase of an outbreak. RT-PCR testing in primary care should be prioritised for effective COVID-19 prevention and control.

    Role of interleukin 6 as a predictive factor for a severe course of Covid-19: retrospective data analysis of patients from a Long-term Care Facility during Covid-19 Outbreak

    Authors: Peter Sabaka; Alena Koščálová; Igor Straka; Julius Hodosy; Robert Lipták; Barbora Kmotorková; Mária Kachlíková; Alica Kušnírová

    doi:10.21203/rs.3.rs-42503/v1 Date: 2020-07-13 Source: ResearchSquare

    Background: Covid-19 is a disease MESHD with high morbidity and mortality among elderly TRANS residents of long-term care facilities (LTCF). During an outbreak of SARS-CoV-2 infection MESHD in the LTCF an effective screening tool is essential to identify the patients at risk for severe illness and death MESHD. We explored the role of interleukin 6 (IL-6) as a predictive factor for severe disease MESHD during the outbreak of Covid-19 in one LTCF in Slovakia.Methods: We conducted a retrospective data analysis of all laboratory- confirmed cases TRANS of COVID-19, diagnosed during the outbreak in one LTCF in Slovakia between April 11, 2020, and May 5, 2020. Within 24 hours after the diagnosis of Covid-19, clinical and laboratory screening was performed in the LTCF by trained clinicians to identify patients in need of hospitalization. Patients with oxygen saturation below 90% were immediately referred to the hospital. Patients staying in the LFTC were monitored daily and those that developed hypoxemia HP were transferred to the hospital. We analyzed the association between the level of IL-6 at the initial assessment and development of hypoxemia HP during the course of the disease MESHD and determined the cut-off of the IL-6 able to predict the development of hypoxemia HP requiring oxygen therapy or ventilatory support.Results: Fifty-three patients (11 men, 42 women) with diagnosed Covid-19 were included in the analysis. 19 (53%) patients developed hypoxemia HP during the course of the disease MESHD. Patients with hypoxemia HP had significantly higher concentrations of IL-6 at initial screening. The concentration of IL-6 > 24 pg/mL predicted the development of hypoxemia HP with the sensitivity SERO of 100% and specificity of 88.9%. The positive and negative predictive values SERO were 76.9%, and 100% respectively.Conclusions: The concentration of IL-6 > 24 pg/mL at initial assessment predicted the development of hypoxemia HP requiring hospitalization with excellent sensitivity SERO and good specificity. IL-6 appears as a potential negative predictive factor for the development of the severe form of Covid-19 and might serve for early identification of patients in need of hospitalization. Further studies are needed to evaluate the robustness of the use of IL-6 as an effective screening tool for the severe course of Covid-19.

    Modelling COVID-19 cases in Nigeria: Forecasts, uncertainties, projections and the link with weather

    Authors: Adeyeri O.E.; Oyekan K.S.A.; Ige S.O.; Akinbobola A.; Okogbue E.C.

    doi:10.21203/rs.3.rs-41193/v1 Date: 2020-07-11 Source: ResearchSquare

    The World Health Organization (WHO) declared COVID-19 a global pandemic on 11 March 2020 due to its global spread. In Nigeria, the first case was documented on 27 February 2020. Since then, it has spread to most parts of the country. This study models, forecasts and projects COVID-19 incidence, cumulative incidence and death MESHD cases in Nigeria using six estimation methods i.e. the attack rate TRANS, maximum likelihood, exponential growth, Markov chain monte Carlo (MCMC), time-dependent and the sequential Bayesian approaches. A sensitivity SERO analysis with respect to the mean generation time is used to quantify the associated reproduction number TRANS uncertainties. The relationship between the COVID-19 incidence and five meteorological variables are further assessed. The result shows that the highest incidences are recorded in days with either religious activities or market days while the weekday trend decreases towards the weekend. It is also established that COVID-19 incidence significantly increases with increasing sea level pressure (0.7 correlation coefficient) and significantly decreases with increasing maximum temperature (-0.3 correlation coefficient). Also, selecting an optimal period for reproduction number TRANS estimates reduces the variability between estimates. As an example, in the EG approach, the epidemic curve that optimally fits the exponential growth is between 1- and 53-time units with reproduction number TRANS estimate of 1.60 [1.58; 1.62] at 95% confidence interval. However, this optimal reproduction number TRANS estimate is different from the default reproduction number TRANS estimate.  Using the MCMC approach, the correlation coefficients between the observed and forecasted incidence, cumulative death MESHD and cumulative confirmed cases TRANS are 0.66, 0.92 and 0.90 respectively. The projections till December shows values approaching 1,000,000, 120,000 and 3,000,000 respectively. Therefore, timely intervention and effective preventive measures are immediately needed to mitigate a full-scale epidemic in the country. 

    ALeRT-COVID: Attentive Lockdown-awaRe Transfer Learning for Predicting COVID-19 Pandemics in Different Countries

    Authors: Yingxue Li; Wenxiao Jia; Junmei Wang; Jianying Guo; Qin Liu; Xiang Li; Guotong Xie; Fei Wang

    doi:10.1101/2020.07.09.20149831 Date: 2020-07-10 Source: medRxiv

    Countries across the world are in different stages of COVID-19 trajectory, among which many have implemented the lockdown measures to prevent its spread. Although the lockdown is effective in such prevention, it may put the economy into a depression. Predicting the epidemic progression with government switching the lockdown on or off is critical. We propose a transfer learning approach called ALeRT-COVID using attention-based recurrent neural network (RNN) architecture to predict the epidemic trends for different countries. A source model was trained on the pre-defined source countries and then transferred to each target country. The lockdown measure was introduced to our model as a predictor and the attention mechanism was utilized to learn the different contributions of the confirmed cases TRANS in the past days to the future trend. Results demonstrated that the transfer learning strategy is helpful especially for early-stage countries. By introducing the lockdown predictor and the attention mechanism, ALeRT-COVID showed a significant improvement on the prediction performance SERO. We predicted the confirmed cases TRANS in one week when extending and easing lockdown separately. Results showed the lockdown measures is still necessary for a number of countries. We expect our research can help different countries to make better decisions on the lockdown measures.

The ZB MED preprint Viewer preVIEW includes all COVID-19 related preprints from medRxiv and bioRxiv, from ChemRxiv, from ResearchSquare, from arXiv and from Preprints.org and is updated on a daily basis (7am CET/CEST).

Sources


Annotations

All
MeSH Disease
Human Phenotype
Transmission
Seroprevalence


Export subcorpus as Endnote

This service is developed in the project nfdi4health task force covid-19 which is a part of nfdi4health.

nfdi4health is one of the funded consortia of the National Research Data Infrastructure programme of the DFG.