Corpus overview


Overview

MeSH Disease

Human Phenotype

Transmission

Seroprevalence
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    Clustering of age TRANS standardised COVID-19 infection MESHD fatality ratios and death MESHD trajectories

    Authors: Thu-Lan Kelly; Greer Humphrey; Caroline Miller; Jacqueline A Bowden; Joanne Dono; Paddy A Phillips

    doi:10.1101/2020.08.11.20172478 Date: 2020-08-11 Source: medRxiv

    Background An accurate measure of the impact of COVID-19 is the infection MESHD fatality ratio, or the proportion of deaths MESHD among those infected, which does not depend on variable testing rates between nations. The risk of mortality from COVID-19 depends strongly on age TRANS and current estimates of the infection MESHD fatality ratio do not account for differences in national age TRANS profiles. Comparisons of cumulative death MESHD trajectories allow the effect and timing of public health interventions to be assessed. Our purpose is to (1) determine whether countries are clustered according to infection MESHD fatality ratios and (2) compare interventions to slow the spread of the disease TRANS disease MESHD by clustering death MESHD trajectories. Methods National age TRANS standardised infection MESHD fatality ratios were derived from age TRANS stratified estimates from China and population estimates from the World Health Organisation. The IFRs were clustered into groups using Gaussian mixture models. Trajectory analysis clustered cumulative death MESHD rates in two time windows, 50 and 100 days after the first reported death MESHD. Findings Infection MESHD fatality ratios from 201 nations were clustered into three groups: young, medium and older, with corresponding means (SD) of 0.20% (0.03%), 0.38% (0.11%) and 0.93% (0.21%). At 50 and 100 days after the first reported death MESHD, there were two clusters of cumulative death MESHD trajectories from 113 nations with at least 25 deaths MESHD reported at 100 days. The first group had slowly increasing or stable cumulative death MESHD rates, while the second group had accelerating rates at the end of the time window. Fifty-two nations changed group membership between the time windows. Conclusion A cluster of younger nations have a lower estimated infection MESHD fatality ratio than older nations. The effect and timing of public health interventions in preventing the spread of the disease TRANS disease MESHD can be tracked by clustering death MESHD rate trajectories into stable or accelerating and comparing changes over time.

    Population perspective comparing COVID-19 to all and common causes of death MESHD in seven European countries

    Authors: Bayanne Olabi; Jayshree Bagaria; Sunil Bhopal; Gwenetta Curry; Nazmy Villarroel; Raj Bhopal

    doi:10.1101/2020.08.07.20170225 Date: 2020-08-11 Source: medRxiv

    Background: Mortality statistics on the COVID-19 pandemic have led to widespread concern and fear. To contextualise these data, we compared mortality related to COVID-19 with all and common causes of death MESHD, stratifying by age TRANS and sex. We also calculated deaths MESHD as a proportion of the population by age TRANS and sex. Methods: COVID-19 related mortality and population statistics from seven European countries were extracted: England and Wales, Italy, Germany, Spain, France, Portugal and Netherlands. Available data spanned 14-16 weeks since the first recorded deaths MESHD in each country, except Spain, where only comparable stratified data over an 8-week time period was available. The Global Burden of Disease MESHD database provided data on all deaths MESHD and those from pneumonia MESHD pneumonia HP, cardiovascular disease MESHD combining ischaemic heart disease MESHD and stroke MESHD stroke HP, chronic obstructive pulmonary disease MESHD chronic obstructive pulmonary disease HP, cancer, road traffic accidents and dementia MESHD dementia HP. Findings: Deaths MESHD related to COVID-19, while modest overall, varied considerably by age TRANS. Deaths MESHD as a percentage of all cause deaths MESHD during the time period under study ranged from <0.01% in children TRANS in Germany, Portugal and Netherlands, to as high as 41.65% for men aged TRANS over 80 years in England and Wales. The percentage of the population who died from COVID-19 was less than 0.2% in every age group TRANS under the age TRANS of 80. In each country, over the age TRANS of 80, these proportions were: England and Wales 1.27% males TRANS, 0.87% females TRANS; Italy 0.6% males TRANS, 0.38% females TRANS; Germany 0.13% males TRANS, 0.09% females TRANS; France 0.39% males TRANS, 0.2% females TRANS; Portugal 0.2% males TRANS, 0.15% females TRANS; and Netherlands 0.6% males TRANS, 0.4% females TRANS. Interpretation: Mortality rates from COVID-19 remains low including when compared to other common causes of death MESHD and will likely decline further while control measures are maintained. These data may help people contextualise their risk and policy makers in decision-making.

    Long-term exposure to air-pollution and COVID-19 mortality in England: a hierarchical spatial analysis

    Authors: Garyfallos Konstantinoudis; Tullia Padellini; James E Bennett; Bethan Davies; Majid Ezzati; Marta Blangiardo

    doi:10.1101/2020.08.10.20171421 Date: 2020-08-11 Source: medRxiv

    Background: Recent studies suggested a link between long-term exposure to air-pollution and COVID-19 mortality. However, due to their ecological design, based on large spatial units, they neglect the strong localised air-pollution patterns, and potentially lead to inadequate confounding adjustment. We investigated the effect of long-term exposure to NO2 and PM2.5 on COVID-19 deaths MESHD up to June 30, 2020 in England using high geographical resolution. Methods: We included 38 573 COVID-19 deaths MESHD up to June 30, 2020 at the Lower Layer Super Output Area level in England (n=32 844 small areas). We retrieved averaged NO2 and PM2.5 concentration during 2014-2018 from the Pollution Climate Mapping. We used Bayesian hierarchical models to quantify the effect of air-pollution while adjusting for a series of confounding and spatial autocorrelation. Findings: We find a 0.5% (95% credible interval: -0.2%-1.2%) and 1.4% (-2.1%-5.1%) increase in COVID-19 mortality rate for every 1g/m3 increase in NO2 and PM2.5 respectively, after adjusting for confounding and spatial autocorrelation. This corresponds to a posterior probability of a positive effect of 0.93 and 0.78 respectively. The spatial relative risk at LSOA level revealed strong patterns, similar for the different pollutants. This potentially captures the spread of the disease TRANS disease MESHD during the first wave of the epidemic. Interpretation: Our study provides some evidence of an effect of long-term NO2 exposure on COVID-19 mortality, while the effect of PM2.5 remains more uncertain. Funding: Medical Research Council, Wellcome Trust, Environmental Protection Agency and National Institutes of Health.

    Telmisartan for treatment of Covid-19 patients: an open randomized clinical trial. Preliminary report.

    Authors: Mariano Duarte; Facundo G Pelorosso; Liliana Nicolosi; M. Victoria Salgado; Hector Vetulli; Analia Aquieri; Francisco Azzato; Mauro Basconcel; Marcela Castro; Javier Coyle; Ignacio Davolos; Eduardo Esparza; Ignacio Fernandez Criado; Rosana Gregori; Pedro Mastrodonato; Maria Rubio; Sergio Sarquis; Fernando Wahlmann; Rodolfo Pedro Rothlin

    doi:10.1101/2020.08.04.20167205 Date: 2020-08-11 Source: medRxiv

    Background. Covid-19, the disease MESHD caused by SARS-CoV-2, is associated with significant respiratory-related morbidity and mortality. Angiotensin receptor blockers (ARBs) have been postulated as tentative pharmacological agents to treat Covid-19-induced lung inflammation MESHD. Trial design. This trial is a parallel group, randomized, two arm, open label, multicenter superiority trial with 1:1 allocation ratio. Methods. Participants included patients who were 18 years of age TRANS or older and who had been hospitalized with confirmed Covid-19 with 4 or fewer days since symptom onset TRANS. Exclusion criteria included intensive care unit admission prior to randomization and use of angiotensin receptor blocker or angiotensin converting enzyme inhibitors at admission. Participants in the treatment arm received telmisartan 80 mg bid during 14 days plus standard care. Participants in the control arm received standard care alone. Primary outcome was to achieve significant reductions in plasma SERO levels of C-reactive protein in telmisartan treated Covid-19 patients at day 5 and 8 after randomization. Key secondary outcomes included time to discharge evaluated at 15 days after randomization and admission to ICU and death MESHD at 15- and 30-days post randomization. We present here a preliminary report. Results. A total of 78 patients were included in the interim analysis, 40 in the telmisartan and 38 in the control groups. CRP levels at day 5 in the control group were 51.1 +/- 44.8 mg/L (mean +/- SD; n=28) and in the telmisartan group were 24.2 +/- 31.4 mg/L (mean +/- SD; n=32, p<0.05). At day 8, CRP levels were 41.6 +/- 47.6 mg/L (mean +/- SD; n=16) and 9.0 +/- 10.0 mg/L (mean +/- SD; n=13, p < 0.05) in the control and telmisartan groups, respectively. Also, analysis of time to discharge by Kaplan-Meier method showed that telmisartan treated patients had statistically significant lower time to discharge (median time to discharge control group=15 days; telmisartan group=9 days). No differences were observed for ICU admission or death MESHD. No significant adverse events related to telmisartan were reported. Conclusions. In the present preliminary report, despite the small number of patients studied, ARB telmisartan, a well-known inexpensive safe antihypertensive drug, administered in high doses, demonstrates anti-inflammatory effects and improved morbidity in hospitalized patients infected with SARS -CoV-2, providing support for its use in this serious pandemia (NCT04355936).

    Deciphering the state of immune silence in fatal COVID-19 patients

    Authors: Ido Amit; Pierre Bost; Francesco De Sanctis; Stefania Canè; Ugel Stefano; Katia Donadello; Monica Castellucci; Eyal David; Alessandra Fiore; Cristina Anselmi; Roza Barouni; Rosalinda Trovato; Simone Caligola; Alessia Lamolinara; Manuela Iezzi; Federica Facciotti; Anna Mazzariol; Davide Gibellini; Pasquale De Nardo; Evelina Tacconelli; Leonardo Gottin; Enrico Polati; Benno Schwikowski; Vincenzo Bronte

    doi:10.21203/rs.3.rs-56689/v1 Date: 2020-08-10 Source: ResearchSquare

    Since the beginning of the SARS-CoV-2 pandemic, COVID-19 has appeared as a unique disease MESHD with unconventional tissue and systemic immune features. While COVID-19 severe forms share clinical and laboratory aspects with various pathologies such as hemophagocytic lymphohistiocyto-sis, sepsis MESHD sepsis HP or cytokine release syndrome MESHD, their exact nature remains unknown. This is severely imped-ing the ability to treat patients facing severe stages of the disease MESHD. To this aim, we performed an in-depth, single-cell RNA-seq analysis of more than 150.000 immune cells isolated from matched blood SERO samples and broncho-alveolar lavage fluids of COVID-19 patients and healthy controls, and integrated it with clinical, immunological and functional ex vivo data. We unveiled an immune sig-nature of disease MESHD severity that correlated with the accumulation of naïve lymphoid cells in the lung and an expansion and activation of myeloid cells in the periphery. Moreover, we demonstrated that myeloid-driven immune suppression is a hallmark of COVID-19 evolution and arginase 1 expression is significantly associated with monocyte immune regulatory features. Noteworthy, we found mon-ocyte and neutrophil immune suppression loss associated with fatal clinical outcome in severe pa-tients. Additionally, our analysis discovered that the strongest association of the patients clinical outcome and immune phenotype is the lung T cell response. We found that patients with a robust CXCR6+ effector memory T cell response have better outcomes. This result is line with the rs11385942 COVID-19 risk allel, which is in proximity to the CXCR6 gene and suggest effector memory T cell are a primary feature in COVID-19 patients. By systemically quantifying the viral landscape in the lung of severe patients, we indeed identified Herpes-Simplex MESHD-Virus 1 (HSV-1) as a potential opportunistic virus in COVID-19 patients. Lastly, we observed an unexpectedly high SARS-CoV-2 viral load in an immuno-compromised patient, allowing us to study the SARS-CoV-2 in-vivo life cycle. The development of myeloid dysfunctions and the impairment of lymphoid arm establish a condition of immune paralysis MESHD paralysis HP that supports secondary bacteria and virus infection MESHD and can progress to “immune silence” in patients facing death MESHD.

    Comparative analyses of SARS-CoV-2 binding (IgG, IgM, IgA) and neutralizing antibodies SERO from human serum samples SERO

    Authors: Livia Mazzini; Donata Martinuzzi; Inesa Hyseni; Giulia Lapini; Linda Benincasa; Pietro Piu; Claudia Maria Trombetta; Serena Marchi; Ilaria Razzano; Alessandro Manenti; Emanuele Montomoli

    doi:10.1101/2020.08.10.243717 Date: 2020-08-10 Source: bioRxiv

    A newly identified coronavirus, named SARS-CoV-2, emerged in December 2019 in Hubei Province, China, and quickly spread throughout the world; so far, it has caused more than 18 million cases of disease MESHD and 700,000 deaths MESHD. The diagnosis of SARS-CoV-2 infection MESHD is currently based on the detection of viral RNA in nasopharyngeal swabs by means of molecular-based assays, such as real-time RT-PCR. Furthermore, serological assays SERO aimed at detecting different classes of antibodies SERO constitute the best surveillance strategy for gathering information on the humoral immune response to infection MESHD and the spread of the virus through the population, in order to evaluate the immunogenicity of novel future vaccines and medicines for the treatment and prevention of COVID-19 disease MESHD. The aim of this study was to determine SARS-CoV-2-specific antibodies SERO in human serum samples SERO by means of different commercial and in-house ELISA SERO kits, in order to evaluate and compare their results first with one another and then with those yielded by functional assays using wild-type virus. It is important to know the level of SARS-CoV-2-specific IgM, IgG and IgA antibodies SERO in order to predict population immunity and possible cross-reactivity with other coronaviruses and to identify potentially infectious subjects. In addition, in a small sub-group of samples, we performed a subtyping Immunoglobulin G ELISA SERO. Our data showed an excellent statistical correlation between the neutralization titer and the IgG, IgM and IgA ELISA SERO response against the receptor-binding domain of the spike protein, confirming that antibodies SERO against this portion of the virus spike protein are highly neutralizing and that the ELISA SERO Receptor-Binding Domain-based assay can be used as a valid surrogate for the neutralization assay in laboratories which do not have Biosecurity level-3 facilities.

    Data-driven Inferences of Agency-level Risk and Response Communication on COVID-19 through Social Media based Interactions

    Authors: Md Ashraf Ahmed; Arif Mohaimin Sadri; M. Hadi Amini

    id:2008.03866v1 Date: 2020-08-10 Source: arXiv

    Risk and response communication of public agencies through social media played a significant role in the emergence and spread of novel Coronavirus (COVID-19) and such interactions were echoed in other information outlets. This study collected time-sensitive online social media data and analyzed such communication patterns from public health (WHO, CDC), emergency MESHD (FEMA), and transportation (FDOT) agencies using data-driven methods. The scope of the work includes a detailed understanding of how agencies communicate risk information through social media during a pandemic and influence community response (i.e. timing of lockdown, timing of reopening) and disease MESHD outbreak indicators (i.e. number of confirmed cases TRANS, number of deaths MESHD). The data includes Twitter interactions from different agencies (2.15K tweets per agency on average) and crowdsourced data (i.e. Worldometer) on COVID-19 cases and deaths MESHD were observed between February 21, 2020 and June 06, 2020. Several machine learning techniques such as (i.e. topic mining and sentiment ratings over time) are applied here to identify the dynamics of emergent topics during this unprecedented time. Temporal infographics of the results captured the agency-levels variations over time in circulating information about the importance of face covering, home quarantine, social distancing and contact tracing TRANS. In addition, agencies showed differences in their discussions about community transmission TRANS, lack of personal protective equipment, testing and medical supplies, use of tobacco, vaccine, mental health issues, hospitalization, hurricane season, airports, construction work among others. Findings could support more efficient transfer of risk and response information as communities shift to new normal as well as in future pandemics.

    COVID19 Tracking: An Interactive Tracking, Visualizing and Analyzing Platform

    Authors: Zhou Yang; Jiwei Xu; Zhenhe Pan; Fang Jin

    id:2008.04285v1 Date: 2020-08-10 Source: arXiv

    The Coronavirus Disease MESHD 2019 (COVID-19) has now become a pandemic, inflicting millions of people and causing tens of thousands of deaths MESHD. To better understand the dynamics of COVID-19, we present a comprehensive COVID-19 tracking and visualization platform that pinpoints the dynamics of the COVID-19 worldwide. Four essential components are implemented: 1) presenting the visualization map of COVID-19 confirmed cases TRANS and total counts all over the world; 2) showing the worldwide trends of COVID-19 at multi-grained levels; 3) provide multi-view comparisons, including confirmed cases TRANS per million people, mortality rate and accumulative cure rate; 4) integrating a multi-grained view of the disease MESHD disease spreading TRANS spreading dynamics in China and showing how the epidemic is taken under control in China.

    Individualized Prediction of COVID-19 Adverse outcomes with MLHO

    Authors: Hossein Estiri; Zachary H. Strasser; Shawn N. Murphy

    id:2008.03869v1 Date: 2020-08-10 Source: arXiv

    The COVID-19 pandemic has devastated the world with health and economic wreckage. Precise estimates of the COVID-19 adverse outcomes on individual patients could have led to better allocation of healthcare resources and more efficient targeted preventive measures. We developed MLHO (pronounced as melo) for predicting patient-level risk of hospitalization, ICU admission, need for mechanical ventilation, and death MESHD from patients' past (before COVID-19 infection MESHD) medical records. MLHO is an end-to-end Machine Learning pipeline that implements iterative sequential representation mining and feature and model selection to predict health outcomes. MLHO's architecture enables a parallel and outcome-oriented calibration, in which different statistical learning algorithms and vectors of features are simultaneously tested and leveraged to improve prediction of health outcomes. Using clinical data from a large cohort of over 14,000 patients, we modeled the four adverse outcomes utilizing about 600 features representing patients' before-COVID health records. Overall, the best predictions were obtained from extreme and gradient boosting models. The median AUC ROC for mortality prediction was 0.91, while the prediction performance SERO ranged between 0.79 and 0.83 for ICU, hospitalization, and ventilation. We broadly describe the clusters of features that were utilized in modeling and their relative influence on predicting each outcome. As COVID-19 cases are re-surging in the U.S. and around the world, a Machine Learning pipeline like MLHO is crucial to improve our readiness for confronting the potential future waves of COVID-19, as well as other novel infectious diseases MESHD that may emerge in the near future.

    The Multiple Impacts of the COVID-19: A Qualitative Perspective

    Authors: Muhamad KhairulBahri

    id:10.20944/preprints202005.0033.v2 Date: 2020-08-08 Source: Preprints.org

    The world has been highly impacted by the COVID-19 as the virus has spread to all continents – about 200 countries in total. The latest update claims about 4,000,000 confirmed cases TRANS and about 300,000 confirmed deaths MESHD owing to the COVID-19 pandemic. This probably makes the COVID-19 as the most dangerous contagious disease MESHD in the era 2000s. Apart from massive publications on this topic, there is no available qualitative analysis that describes the dynamic spreads of the COVID-19 and its impacts on healthcare and the economy. Through the system archetypes analysis, this paper explains that the dynamic spread of the COVID-19 consists of the limits to growth and the success to successful structures. The limits to growth elucidates that more symptomatic and asymptomatic TRANS patients owing to infected droplets may be bounded by self-healing and isolated treatments. The success to successful structure explains that once the COVID-19 affects the economy through the lockdown, there will be a limited fund to support the government aids and the aggregate demand. In overall, this paper gives readers simplified holistic insights into understanding the dynamic spread of the COVID-19.

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MeSH Disease
Human Phenotype
Transmission
Seroprevalence


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