Corpus overview


Overview

MeSH Disease

Infections (554)

Disease (464)

Death (402)

Coronavirus Infections (276)

Pneumonia (109)


Human Phenotype

Pneumonia (113)

Fever (109)

Cough (88)

Fatigue (28)

Hypertension (20)


Transmission

Seroprevalence
    displaying 11 - 20 records in total 1146
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    Value of laboratory tests in COVID-19 hospitalized patients for clinical decision-makers: a predictive model, using data mining approach

    Authors: Atefeh Mousavi; Soheyla Rezaei; Jamshid Salamzadeh; Ali Mirzazadeh; Farzad Peiravian; Nazila Yousefi

    doi:10.21203/rs.3.rs-56252/v1 Date: 2020-08-09 Source: ResearchSquare

    Purpose: Because of the rapid increase in confirmed cases TRANS of COVID-19, in particular those with severe or critical status, overwhelming of health systems is a worldwide concern. Therefore, identifying high-risk COVID-19 patients, can help service providers for priority setting and hospital resource allocation. Methods: 4542 adult TRANS patients with confirmed COVID-19 admitted in 15 hospitals in Tehran, Iran, from Feb 20 to April 18, 2020 were included in this retrospective cohort study with final outcomes of survived and died patients. Demographic features including age TRANS and sex, and laboratory data measured at admission were extracted and compared between recovered and died patients. Data analysis was performed applying SPSS modeler software using a logistic regression method.Results: Of 4542 hospitalized adult TRANS patients, 822 patients (18.09%) died during hospitalization, and 3720 (81.90%) recovered and discharged. Based on logistic regression model, older age TRANS, 40-49 (RR= 1.80, CI: 1.13-2.87), 50-59 (RR=2.63, CI: 1.71-4.02), 60-69 (RR= 4.40, CI: 2.92-6.63), 70-79 (RR=7.49, CI: 5.01-11.19), Above 80 (RR=13.85, CI: 9.23-2.77), ALT ≥ 55 IU/ (RR=2.20, CI: 1.69-2.86), AST ≥ 100 IU/L (RR=5.93, CI: 4.75-7.39), ALP ≥ 200 IU/L (RR=2.46, CI: 1.80-3.37), sodium < 135 mEq/l (RR=1.69, CI: 1.35-2.11) or more than 145 mEq/l (RR=7.24, CI: 5.07-10.33), potassium > 5.50 mEq/l (RR=7.53, CI: 4.15-13.64), and calcium < 8.50 mEq/l (RR=3.39, CI: 2.81-4.09), CPK between 307-600 IU/L (RR=2.73, CI: 2.12-3.53) and above 600 IU/L (RR=4.41, CI: 3.40-5.71) in men, and 192-400 IU/L (RR=2.73, CI: 2.12-3.53), and above 400 (RR=4.41, CI: 3.40-5.71) in women, CRP > 3 mg/l (RR=3.22, CI: 1.99-5.20), and creatinine > 1.5 mg/l (RR=6.37, CI: 5.30-7.66) were significantly associated with COVID-19 mortality. Conclusion: Our findings suggested less than one in five hospitalized patients with COVID-19 die mostly due to electrolyte disbalance, liver, and renal dysfunctions. Better supportive care is needed to improve outcomes for patients with COVID-19.

    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.

    CRISPR-based and RT-qPCR surveillance of SARS-CoV-2 in asymptomatic TRANS individuals uncovers a shift in viral prevalence SERO among a university population

    Authors: Jennifer N Rauch; Eric Valois; Jose Carlos Ponce-Rojas; Zach Aralis; Ryan L Lach; Francesca Zappa; Morgane Audouard; Sabrina C Solley; Chinmay Vaidya; Michael Costello; Holly Smith; Ali Javanbakht; Betsy Malear; Laura Polito; Stewart Comer; Katherine Arn; Kenneth S Kosik; Diego Acosta-Alvear; Maxwell Z Wilson; Lynn Fitzgibbons; Carolina Arias

    doi:10.1101/2020.08.06.20169771 Date: 2020-08-07 Source: medRxiv

    Background: The progress of the COVID-19 pandemic profoundly impacts the health of communities around the world, with unique impacts on colleges and universities. Transmission TRANS of SARS-CoV-2 by asymptomatic TRANS people is thought to be the underlying cause of a large proportion of new infections MESHD. However, the local prevalence SERO of asymptomatic TRANS and pre-symptomatic carriers TRANS of SARS-CoV-2 is influenced by local public health restrictions and the community setting. Objectives: This study has three main objectives. First, we looked to establish the prevalence SERO of asymptomatic TRANS SARS-CoV-2 infection MESHD on a university campus in California. Second, we sought to assess the changes in viral prevalence SERO associated with the shifting community conditions related to non-pharmaceutical interventions (NPIs). Third, we aimed to compare the performance SERO of CRISPR- and PCR-based assays for large-scale virus surveillance sampling in COVID-19 asymptomatic TRANS persons. Methods: We enrolled 1,808 asymptomatic TRANS persons for self-collection of oropharyngeal (OP) samples to undergo SARS-CoV-2 testing. We compared viral prevalence SERO in samples obtained in two time periods: May 28th-June 11th; June 23rd-July 2nd. We detected viral genomes in these samples using two assays: CREST, a CRISPR-based method recently developed at UCSB, and the RT-qPCR test recommended by US Centers for Disease MESHD Control and Prevention (CDC). Results: Of the 1,808 participants, 1,805 were affiliates of the University of California, Santa Barbara, and 1,306 were students. None of the tests performed on the 732 samples collected between late May to early June were positive. In contrast, tests performed on the 1076 samples collected between late June to early July, revealed nine positive cases. This change in prevalence SERO met statistical significance, p = 0.013. One sample was positive by RT-qPCR at the threshold of detection, but negative by both CREST and CLIA-confirmation testing. With this single exception, there was perfect concordance in both positive and negative results obtained by RT-qPCR and CREST. The estimated prevalence SERO of the virus, calculated using the confirmed cases TRANS, was 0.74%. The average age TRANS of our sample population was 28.33 (18-75) years, and the average age TRANS of the positive cases was 21.7 years (19-30). Conclusions: Our study revealed that there were no COVID-19 cases in our study population in May/June. Using the same methods, we demonstrated a substantial shift in prevalence SERO approximately one month later, which coincided with changes in community restrictions and public interactions. This increase in prevalence SERO, in a young and asymptomatic TRANS population which would not have otherwise accessed COVID-19 testing, indicated the leading wave of a local outbreak, and coincided with rising case counts in the surrounding county and the state of California. Our results substantiate that large, population-level asymptomatic TRANS screening using self-collection may be a feasible and instructive aspect of the public health approach within large campus communities, and the almost perfect concordance between CRISPR- and PCR-based assays indicate expanded options for surveillance testing

    Association of mental disorders with SARS-CoV-2 infection MESHD infection and severe HP and severe health outcomes: a nationwide cohort study

    Authors: Ha-Lim Jeon; Jun Soo Kwon; So-Hee Park; Ju-Young Shin

    doi:10.1101/2020.08.05.20169201 Date: 2020-08-07 Source: medRxiv

    Background: No epidemiological data exists for the association between mental disorders and the risk of severe acute respiratory syndrome MESHD coronavirus 2 (SARS-CoV-2) infection MESHD and coronavirus disease MESHD 2019 (COVID-19) severity. Aims: To evaluate the association between mental disorders and the risk of SARS-CoV-2 infection MESHD infection and severe HP and severe outcomes following COVID-19. Methods: We performed a cohort study using the Korean COVID-19 patient database based on the national health insurance data. Each patient with a mental or behavioral disorder (diagnosed during six months prior to the first SARS-CoV-2 test) was matched by age TRANS, sex, and Charlson comorbidity index with up to four patients without mental disorders. SARS-CoV-2 positivity risk and risk of death MESHD or severe events (intensive care unit admission, use of mechanical ventilation, and acute respiratory distress HP syndrome MESHD) post- infection MESHD were calculated using conditional logistic regression analysis. Results: Among 230,565 patients tested for SARS-CoV-2, 33,653 (14.6%) had mental disorders, 928/33,653 (2.76%) tested positive, and 56/928 (6.03%) died. In multivariate analysis with the matched cohort, there was no association between mental disorders and SARS-CoV-2 positivity risk (odds ratio [OR], 1.02; 95% confidence interval [CI], 0.92-1.12); however, a higher risk was associated with schizophrenia HP-related disorders (OR, 1.36; 95% CI, 1.02-1.81). Among confirmed cases TRANS, mortality risk significantly increased in patients with mental disorders (OR, 1.84, 95% CI, 1.07-3.15). Conclusion: Mental disorders are likely contributing factors of mortality following COVID-19. Although the infection MESHD infection risk TRANS infection risk TRANS risk did not increase in overall mental disorders, patients with schizophrenia HP-related disorders were more vulnerable to the infection MESHD.

    A SEIR-like model with a time-dependent contagion factor describes the dynamics of the Covid-19 pandemic

    Authors: Ronald Dickman

    doi:10.1101/2020.08.06.20169557 Date: 2020-08-07 Source: medRxiv

    I consider a simple, deterministic SEIR-like model without spatial or age TRANS structure, including a presymptomatic state and distinguishing between reported and nonreported infected individuals. Using a time-dependent contagion factor {beta}(t) (in the form a piecewise constant function) and literature values for other epidemiological parameters, I obtain good fits to observational data for the cumulative number of confirmed cases TRANS in over 160 regions (103 countries, 24 Brazilian states and 34 U.S. counties). The evolution of {beta} is useful for characterizing the state of the epidemic. The analysis provides insight into general trends associated with the pandemic, such as the tendency toward reduced contagion, and the fraction of the population exposed to the virus.

    Estimating the Changing Infection MESHD Rate of COVID-19 Using Bayesian Models of Mobility

    Authors: Luyang Liu; Sharad Vikram; Junpeng Lao; Xue Ben; Alexander D'Amour; Shawn O'Banion; Mark Sandler; Rif A. Saurous; Matthew D. Hoffman

    doi:10.1101/2020.08.06.20169664 Date: 2020-08-07 Source: medRxiv

    In order to prepare for and control the continued spread of the COVID-19 pandemic while minimizing its economic impact, the world needs to be able to estimate and predict COVID-19's spread. Unfortunately, we cannot directly observe the prevalence SERO or growth rate of COVID-19; these must be inferred using some kind of model. We propose a hierarchical Bayesian extension to the classic susceptible-exposed-infected-removed (SEIR) compartmental model that adds compartments to account for isolation and death MESHD and allows the infection MESHD rate to vary as a function of both mobility data collected from mobile phones and a latent time-varying factor that accounts for changes in behavior not captured by mobility data. Since confirmed-case TRANS data is unreliable, we infer the model's parameters conditioned on deaths MESHD data. We replace the exponential-waiting-time assumption of classic compartmental models with Erlang distributions, which allows for a more realistic model of the long lag between exposure and death MESHD. The mobility data gives us a leading indicator that can quickly detect changes in the pandemic's local growth rate and forecast changes in death MESHD rates weeks ahead of time. This is an analysis of observational data, so any causal interpretations of the model's inferences should be treated as suggestive at best; nonetheless, the model's inferred relationship between different kinds of trips and the infection MESHD rate do suggest some possible hypotheses about what kinds of activities might contribute most to COVID-19's spread.

    A Machine Learning based approach to unleash the impact of COVID-19 on Indian Stock Market

    Authors: Nusrat Rouf; Majid Bashir Malik; Tasleem Arif

    doi:10.21203/rs.3.rs-54882/v1 Date: 2020-08-06 Source: ResearchSquare

    Introduction: Advancement in information technology, be it hardware, software or communication technology, over few decades has rapidly impacted almost every field of study. Machine learning tools and techniques are nowadays applied to every field. It has opened the ways for interdisciplinary research by promising effective analyzation and decision-making strategies. COVID-19 has badly affected more than 200 countries within a short span of time. It has drastically affected both daily activities as well as economic activities. Herd behavior of investors has triggered panic selling. As a result, stock markets around the world have plunged down.Methods: In this paper, we analyze the impact of COVID-19 on NSE (National Stock Exchange) index Nifty50. We employ Pearson Correlation and investigate the impact of total confirmed cases TRANS and daily cases on Nifty50 closing price. We use various machine learning regression models for predictive analysis viz, linear regression with polynomial terms (quadratic, cubic), Decision Tree Regression and Random Forest Regression. Model performance SERO is measured using MSE (Mean Square Error), RMSE (Root Mean Square Error) and R2 (R Squared) evaluators. Results: Correlation analysis reveals that total confirmed cases TRANS and daily cases in both India and the World have negative correlation with Nifty50 closing prices. Moreover, Nifty50 closing prices are more negatively correlated with total confirmed and daily cases in India. Predictive analysis shows that the Random Forest Regression model outperforms all other models. Conclusion: We analyze and predict the impact of COVID-19 on closing price of Nifty50 index. We employ Pearson Correlation and investigate the impact of COVID-19 on Nifty50 closing prices. We use various machine learning regression models to predict the closing price of Nifty50 index. Results reveal that the market volatility is directly proportional to increase in number of COVID-19 cases. Random Forest Regression model has comparatively shown better RMSE and R2 values.

    Epidemiology of Reopening in the COVID-19 Pandemic in the United States, Europe and Asia

    Authors: Weiqi Zhang; Alina Oltean; Scott Nichols; Fuad Odeh; Fei Zhong

    doi:10.1101/2020.08.05.20168757 Date: 2020-08-06 Source: medRxiv

    Since the discovery of the novel coronavirus (SARS-CoV-2), COVID-19 has become a global healthcare and economic crisis. The United States (US) and Europe exhibited wide impacts from the virus with more than six million cases by the time of our analysis. To inhibit spread, stay-at-home orders and other non-pharmaceutical interventions (NPIs) were instituted. Beginning late April 2020, some US states, European, and Asian countries lifted restrictions and started the reopening phases. In this study, the changes of confirmed cases TRANS, hospitalizations, and deaths MESHD were analyzed after reopening for 11 countries and 40 US states using an interrupted time series analysis. Additionally, the distribution of these categories was further analyzed by age TRANS due to the known increased risk in elderly TRANS patients. Reopening had varied effects on COVID-19 cases depending on the region. Recent increases in cases did not fully translate into increased deaths MESHD. Eight countries had increased cases after reopening while only two countries showed the same trend in deaths MESHD. In the US, 30 states had observed increases in cases while only seven observed increased deaths MESHD. In addition, we found that states with later reopening dates were more likely to have significant decreases in cases, hospitalizations, and deaths MESHD. Furthermore, age TRANS distributions through time were analyzed in relation to COVID-19 in the US. Younger age groups TRANS typically had an increased share of cases after reopening.

    Mathematical Analysis, Model and Prediction of COVID-19 Data

    Authors: Yit Chow Tong

    doi:10.1101/2020.08.04.20168195 Date: 2020-08-06 Source: medRxiv

    A simple and effective mathematical procedure for the description of observed COVID-19 data and calculation of future projections is presented. An exponential function E(t) with a time-varying Growth Constant k(t) is used. E(t) closely approximates observed COVID-19 Daily Confirmed Cases TRANS with NRMSDs of 1 to 2%. An example of prediction of future cases is presented. The Effective Growth Rates of a discrete SIR model were estimated on the basis of k(t) for COVID-19 data for Germany, and were found to be consistent with those reported in a previous study (1). The proposed procedure, which involves less than ten basic algebraic, logarithm and exponentiation operations for each data point, is suitable for use in promoting interdisciplinary research, exchange and sharing of information.

    Genomic epidemiology reveals transmission TRANS patterns and dynamics of SARS-CoV-2 in Aotearoa New Zealand

    Authors: Jemma L Geoghegan; Xiaoyun Ren; Matthew Storey; James Hadfield; Lauren Jelley; Sarah Jefferies; Jill Sherwood; Shevaun Paine; Sue Huang; Jordan Douglas; Fabio K L Mendes; Andrew Sporle; Michael G Baker; David R Murdoch; Nigel French; Colin R Simpson; David Welch; Alexei J Drummond; Edward C Holmes; Sebastian Duchene; Joep de Ligt

    doi:10.1101/2020.08.05.20168930 Date: 2020-08-06 Source: medRxiv

    New Zealand, a geographically remote Pacific island with easily sealable borders, implemented a nation-wide lockdown of all non-essential services to curb the spread of COVID-19. New Zealand has now effectively eliminated the virus, with low numbers of new cases limited to new arrivals in managed quarantine facilities at the border. Here, we generated 649 SARS-CoV-2 genome sequences from infected patients in New Zealand with samples collected between 26 February and 22 May 2020, representing 56% of all confirmed cases TRANS in this time period. Despite its remoteness, the viruses imported into New Zealand represented nearly all of the genomic diversity sequenced from the global virus population. The proportion of D614G variants in the virus spike protein increased over time due to an increase in their importation frequency, rather than selection within New Zealand. These data also helped to quantify the effectiveness of public health interventions. For example, the effective reproductive number TRANS, Re, of New Zealand's largest cluster decreased from 7 to 0.2 within the first week of lockdown. Similarly, only 19% of virus introductions into New Zealand resulted in a transmission TRANS lineage of more than one additional case. Most of the cases that resulted in a transmission TRANS lineage originated from North America, rather than from Asia where the virus first emerged or from the nearest geographical neighbour, Australia. Genomic data also helped link more infections MESHD to a major transmission TRANS cluster than through epidemiological data alone, providing probable sources of infections MESHD for cases in which the source was unclear. Overall, these results demonstrate the utility of genomic pathogen surveillance to inform public health and disease MESHD mitigation.

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MeSH Disease
Human Phenotype
Transmission
Seroprevalence


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