Corpus overview


Overview

MeSH Disease

Human Phenotype

Pneumonia (1049)

Fever (651)

Cough (525)

Hypertension (362)

Anxiety (286)


Transmission

age categories (2647)

Transmission (2432)

gender (1227)

fomite (1108)

contact tracing (876)


Seroprevalence
    displaying 5891 - 5900 records in total 12932
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    How to Analyze Cancer Progression in COVID-19 Pandemic?

    Authors: Atanu Bhattacharjee; GajendraK.Vishwakarma; Souvik Banerjee; Sharvari Shukla

    doi:10.21203/rs.3.rs-31799/v1 Date: 2020-05-26 Source: ResearchSquare

    Background: The constant news about the coronavirus is scary. It is not possible to separate treatment for Cancer due to COVID-19. An effective treatment comparison strategy is needed. We need to have a handy tool to understand cancer progression in this unprecedented scenario. Linking different events of cancer progression is the need of the hour. It is a methodological challenge.Methods: This article is dedicated to explore the time lag effect and make statistical inference about the best experimental arm using Accelerated failure time model and regression methods. The work is presented as the occurrence of other events as a hazard rate after the first event (relapse). The time lag effect between the events is linked and analyzed. Results: The results are further analyzed and presented to show the efficacy of our study. An accelerated failure time model is also applied with the transition states and the dependency structure between the gap times are explained using auto-regression. The effects of Arms are compared using the coefficient of auto-regression and accelerated failure time (AFT) models.Conclusions: We provide the solutions to overcome the issue with interval between two consecutive events in motivating head and neck cancer (HNC) data. COVID-19 is not going to leave us soon. We have to carry several cancer clinical trials in presences of COVID-19. We presented a comprehensive analytical strategy to analyze cancer clinical trial data during this pandemic.Keywords: COVID-19, Accelerated Failure Time, Proportional Hazard Model, Bayesian, Auto-Regression.

    Lifestyle acquired immunity, decentralized intelligent infrastructures and revised healthcare expenditures may limit pandemic catastrophe: a lesson from COVID-19

    Authors: Asif Ahmed; Tasnima Haque; Mohammad Mahmudur Rahman

    doi:10.1101/2020.05.23.20111104 Date: 2020-05-26 Source: medRxiv

    Human race has often faced pandemic with substantial number of fatalities. As COVID-19 pandemic reached and endured in every corner on earth, countries with moderate to strong healthcare support and expenditure seemed to struggle in containing disease MESHD transmission TRANS and casualties. COVID-19 affected countries have variability in demographic, socioeconomic and life style health indicators. At this context it is important to find out at what extent these parametric variations are actually modulating disease MESHD outcomes. To answer this, we have selected demographic, socioeconomic and health indicators e.g. population density, percentage of urban population, median age TRANS, health expenditure per capita, obesity MESHD obesity HP, diabetes prevalence SERO, alcohol intake, tobacco use, case fatality of non communicable diseases MESHD (NCDs) as independent variables. Countries were grouped according to these variables and influence on dependent variables e.g. COVID-19 test positive, case fatality and case recovery rates were statistically analyzed. The results suggest that countries with variable median age TRANS has significantly different outcome on test positive rate (P<0.01). Both median age TRANS (P=0.0397) and health expenditure per capita (P=0.0041) has positive relation with case recovery. Increasing number of test per 100K population showed positive and negative relation with number of positives per 100K population (P=0.0001) and percentage of test positives (P<0.0001) respectively. Alcohol intake per capita in liter (P=0.0046), diabetes prevalence SERO (P=0.0389) and NCDs mortalities (P=0.0477) also showed statistical relation with case fatality rate. Further analysis revealed that countries with high healthcare expenditure along with high median age TRANS and increased urban population showed more case fatality but also had better recovery rate. Investment in health sector alone is insufficient in controlling pandemic severity. Intelligent and sustainable healthcare both in urban and rural settings and healthy lifestyle acquired immunity may reduce disease MESHD transmission TRANS and comorbidity induced fatalities respectively.

    Age-group TRANS targeted testing for COVID-19 as new prevention strategy

    Authors: Ranjit Kumar Upadhyay; S. Chatterjee; S. Saha; R.K. Azad

    doi:10.21203/rs.3.rs-31786/v1 Date: 2020-05-26 Source: ResearchSquare

    Robust testing and tracing TRANS are key to fighting the menace of coronavirus disease MESHD 2019 (COVID-19). This outbreak has progressed with tremendous impact on human life, society and economy. In this paper, we propose an age TRANS-structured SIQR model to track the progression of the pandemic in India, Italy and USA, taking into account the different age TRANS-structures of these countries. We have made predictions about the disease MESHD dynamics, identified the most infected age-groups TRANS and analysed the effectiveness of social distancing measures taken in the early stages of infection MESHD. The basic reproductive ratio R0 TRANS has been numerically calculated for each country.We propose a strategy of age TRANS-targeted testing, with increased testing in the most proportionally infected age-groups TRANS. We observe a marked flattening of the infection MESHD curve upon simulating increased testing in the 15-40 year age-groups TRANS in India. Thus, we conclude that social distancing and widespread testing are effective methods of control, with emphasis on testing and identifying the hotspots of highly infected populations.

    Impact of Covid-19 Pandemic on Quality of Sleep Among Nepalese Residents

    Authors: Avinash Chandra; Pramila Karki; Pooja Prakash; Ayush Chandra; Sweta Khadka

    doi:10.21203/rs.3.rs-31619/v1 Date: 2020-05-26 Source: ResearchSquare

    Background A periodic state of rest accompanied by varying degree of unconsciousness MESHD and relative inactivity, the sleep is vital to human health, insufficiency of which can lead to serious problem with physical and mental health consequences. Because of COVID-19 Pandemic, Nepal is under a total lockdown, with total restrictions on the movement of individual in the entire nation, forcing people to home confinement. People are extremely worried about their and families’ health as well as lost or verge of losing jobs. The daily news of increasing COVID-19 cases inside nation and all around the globe is adding to the fear that leads to anger, anxiety HP, frustration, and stress that directly affects the quality of sleep.Objectives The study aimed to assess the quality of sleep before and after COVID-19 pandemic among Nepalese residents.Method: A cross-sectional study was conducted that recruited 206 Nepalese residents. The participants completed an anonymous, self-administered questionnaires (SAQ). Insomnia HP Severity Index (ISI) questionnaire was used to measure the sleep quality before and after the COVID-19 pandemic. Analysis of gathered data was done by descriptive statistics and inferential statistics using SPSS-20 version statistical software.Result There was significant variation on sleep quality among Nepalese residents before and after COVID-19 pandemic (t = 3.227) at p < 0.001. Clinical moderate insomnia HP increased tremendously high in Nepalese. Before the onset of pandemic only 2.9% of participants had moderate and 1% had severe level of clinical insomnia HP that increased up to 16.5% and 1% after the pandemic, respectively. The mean ISI score was 6.35 ± 4.65 and 8.02 ± 6.01 before and after COVID-19 pandemic, respectively.Conclusion This is only one study being carried in Nepal so far that looks for the sleep quality during COVID-19 pandemic. These results show that the people are suffering tremendously with their sleep quality and calls for further research and active measures to help people have good sleep quality during the COVID-19 pandemic. Public awareness regarding the importance of good sleep quality to maintain their mental and physical health during the COVID-19 pandemic.

    European lockdowns and the consequences of relaxation during the COVID-19 pandemic

    Authors: David H Glass

    doi:10.1101/2020.05.19.20106542 Date: 2020-05-26 Source: medRxiv

    This paper investigates the lockdowns introduced in France, Germany, Italy, Spain, and the UK and also explores the potential consequences of different degrees of relaxation. The analysis employs a two-stage SEIR model with different reproductive numbers TRANS pre- and post-lockdown. These parameters are estimated from data on the daily number of confirmed cases TRANS in a process that automatically detects the time at which the lockdown became effective. The model is evaluated by considering its predictive accuracy on current data and it is then deployed to explore partial relaxations. The results indicate that the different countries have been successful in reducing the reproductive number TRANS to values ranging from 0.67 (95% CI: 0.64 - 0.70) to 0.92 (95% CI: 0.89 - 0.95). Results also suggest that a relaxation of 25% could halt the decline in cases in all five countries, while a 50% relaxation could lead to second peaks that are higher and last longer than the earlier peaks in each country. Even though the relaxations so far may have preserved the success of the lockdowns, vigilance is still needed. A relaxation of around 10-15% is recommended if COVID-19 is to continue to decline in all five countries.

    Enveloped Virus Inactivation on Personal Protective Equipment by Exposure to Ozone

    Authors: Emmeline L. Blanchard; Justin D. Lawrence; Jeffery A. Noble; Minghao Xu; Taekyu Joo; Nga Lee Ng; Britney E. Schmidt; Philip J. Santangelo; M.G. Finn

    doi:10.1101/2020.05.23.20111435 Date: 2020-05-26 Source: medRxiv

    Ozone is a highly oxidizing gas easily generated from atmospheric oxygen with inexpensive equipment and is commonly used for the disinfection of municipal water, foods, and surfaces. We report tests of the ability of ozone to inactivate enveloped respiratory viruses (influenza A virus and respiratory syncytial virus), chosen as more easily handled surrogates for SARS-CoV-2, on N95 respirators and other personal protective equipment (PPE) commonly used in hospitals. At 20 ppm, an ozone concentration easily achieved by standard commercial equipment, the viruses were inactivated with high efficiency as long as the relative humidity was above a threshold value of approximately 50%. In the absence of humidity control, disinfection is more variable and requires considerably longer exposure under relatively dry conditions. This report extends the observations of a previous publication (http://doi.org/10.1080/01919510902747969) to hospital-relevant materials and provides additional details about the relationship of humidity to the antiviral activity of ozone. Home CPAP disinfection devices using ozone can provide effective results for individuals. Ozone did not appear to degrade any of the materials tested except for elastic bands if strained during treatment (such as by the pressure exerted by stapled attachment to N95 respirators). The filtration efficiency of N95 respirator material was not compromised.

    1-C Nonlinear Covid-19 Epidemic Model and Application to the Epidemic Prediction in France

    Authors: Jean-Pierre Quadrat

    doi:10.1101/2020.05.24.20111807 Date: 2020-05-26 Source: medRxiv

    We have shown in a previous paper that the standard time-invariant SIR model was not effective to predict the 2019-20 coronavirus pandemic propagation. We have proposed a new model predicting z the logarithm of the number of detected-contaminated people. It follows a linear dynamical system z'= b-a z. We show here that we can improve this prediction using a non linear model z' = b-a z^r where r is an exponent that we have also to estimate from data. Some countries have an epidemic with a bell shaped form that we call unimodal epidemic. With this new model, we fit observed data of different countries having an unimodal epidemic with a surprising quality. We discuss also the prediction quality obtained with these models at the epidemic start in France. Finally, we evaluate the containment impact on the Covid French mortality in hospitals.

    Explainable machine learning models to understand determinants of COVID-19 mortality in the United States

    Authors: Piyush Mathur; Tavpritesh Sethi; Anya Mathur; Ashish Kumar Khanna; Kamal Maheshwari; Jacek B Cywinski; Simran Dua; Frank Papay

    doi:10.1101/2020.05.23.20110189 Date: 2020-05-26 Source: medRxiv

    COVID-19 is now one of the leading causes of mortality amongst adults TRANS in the United States for the year 2020. Multiple epidemiological models have been built, often based on limited data, to understand the spread and impact of the pandemic. However, many geographic and local factors may have played an important role in higher morbidity and mortality in certain populations. The goal of this study was to develop machine learning models to understand the relative association of socioeconomic, demographic, travel TRANS, and health care characteristics of different states across the United States and COVID-19 mortality. Using multiple public data sets, 24 variables linked to COVID-19 disease MESHD were chosen to build the models. Two independent machine learning models using CatBoost regression and random forest were developed. SHAP feature importance and a Boruta algorithm were used to elucidate the relative importance of features on COVID-19 mortality in the United States. Feature importances from both the categorical models, i.e., CatBoost and random forest consistently showed that a high population density, number of nursing homes, number of nursing home beds and foreign travel TRANS were strongest predictors of COVID-19 mortality. Percentage of African American amongst the population was also found to be of high importance in prediction of COVID-19 mortality whereas racial majority (primarily, Caucasian) was not. Both models fitted the data well with a training R2 of 0.99 and 0.88 respectively. The effect of median age TRANS,median income, climate and disease MESHD mitigation measures on COVID-19 related mortality remained unclear. COVID-19 policy making will need to take population density, pre-existing medical care and state travel TRANS policies into account. Our models identified and quantified the relative importance of each of these for mortality predictions using machine learning.

    Computational Simulation to Assess Patient Safety of Uncompensated COVID-19 Two-patient Ventilator Sharing Using the Pulse Physiology Engine

    Authors: Jeffrey B. Webb; Aaron Bray; Philip K. Asare; Rachel B. Clipp; Yatin B. Mehta; Sudheer Penupolu; Aalpen A. Patel; S. Mark Poler

    doi:10.1101/2020.05.19.20107201 Date: 2020-05-26 Source: medRxiv

    Background: The COVID-19 pandemic is stretching medical resources internationally, including creating ventilator shortages that complicate clinical and ethical situations. The possibility of needing to ventilate multiple patients with a single ventilator raises patient health and safety concerns. This simulation study explores patient compatibility and ventilator settings during multi-patient ventilation without the use of flow compensating resistances. Methods: A whole-body computational physiology model was used to simulate each patient on a ventilator. The primary model of a single patient with a dedicated ventilator was augmented to model two patients sharing a single ventilator. A range of ventilator settings and patient characteristics were simulated for paired patients. In addition to mechanical ventilation parameters, the full physiological simulation provides estimates of additional values for oxyhemoglobin saturation, arterial oxygen tension, and other patient parameters. Findings: These simulations show patient outcome during multi-patient ventilation is most closely correlated to lung compliance, oxygenation index, oxygen saturation index, and endtidal carbon dioxide of individual patients. The simulated patient outcome metrics were satisfactory when the lung compliance difference between two patients was less than 12 cmH2O/mL, and the oxygen saturation index difference was less than 2 mmHg. Interpretation: In resource-limited regions of the world, the COVID-19 pandemic will result in equipment shortages. While single-patient ventilation is preferable, if unavailable, these simulations provide a conceptual framework for clinical patient selection guidelines if ventilator sharing is the only available alternative.

    A mathematical epidemic model using genetic fitting algorithm with cross-validation and application to early dynamics of COVID-19 in Algeria

    Authors: Mohamed Taha Rouabah; Abdellah Tounsi; Nacer Eddine Belaloui

    id:2005.13516v3 Date: 2020-05-26 Source: arXiv

    A compartmental epidemic model based on genetic fitting algorithm and using cross-validation method to overcome the overfitting problem is proposed. This generic enhanced SEIR model allows to estimate approximate nowcast and forecast of epidemic evolution including key epidemic parameters and non-measurable asymptomatic TRANS infected portion of the susceptible population. The model is used to study COVID-19 outbreak dynamics in Algeria between February 25th and May 24th. Basic reproduction number TRANS on Feb. 25th is estimated to 3.78 (95% CI 3.033-4.53) and effective reproduction number TRANS on May 24th after three months of the outbreak is estimated to 0.651 (95% CI 0.539-0.761). Infection MESHD peak time is predicted to the end of April while active cases peak time is predicted to the end of May 2020. The disease MESHD incidence, CFR and IFR are calculated. Information provided by this study could help establish a realistic assessment of the situation in Algeria for the time being, inform predictions about potential future evolution, and guide the design of appropriate public health measures.

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MeSH Disease
Human Phenotype
Transmission
Seroprevalence


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