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9,674 results for “COVID-19”
Repeated information of benefits reduces COVID-19 vaccination hesitancy: Experimental evidence from Germany
<p>This replication package contains the raw data and code to replicate the findings reported in the paper. The data and code are licensed under a Creative Commons Attribution 4.0 International Public License. See <strong>LICENSE.txt</strong> for details.</p> <p><strong>Software requirements</strong></p> <p>All analysis were done in Stata version 16:</p> <ul> <li>Add-on packages are included in <strong>scripts/libraries/stata</strong> and do not need to be installed by user. The names, installation sources, and installation dates of these packages are available in <strong>scripts/libraries/stata/stata.trk</strong>.</li> </ul> <p><strong>Instructions</strong></p> <ol> <li>Save the folder <strong>‘replication_PLOS’</strong> to your local drive.</li> <li>Open the master script <strong>‘run.do’</strong> and change the global pointing to the working direction (line 20) to the location where you save the folder on your local drive</li> <li>Run the master script <strong>‘run.do’</strong> to replicate the analysis and generate all tables and figures reported in the paper and supplementary online materials</li> </ol> <p><strong>Datasets</strong></p> <ul> <li>Wave 1 – Survey experiment: <strong>‘wave1_survey_experiment_raw.dta’</strong></li> <li>Wave 2 – Follow-up Survey: <strong>‘wave2_follow_up_raw.dta'</strong></li> <li>Map: shape-files <strong>‘plz2stellig.shp’ ‘OSM_PLZ.shp’</strong>, area codes <em><em>‘Postleitzahlengebiete</em>-_OSM.csv’</em>_, (all links to the sources can be found in the script ‘04_figure2_germany_map.do’)</li> <li>Pretest: <strong>‘pre-test_corona_raw.dta’</strong></li> <li>For Appendix S7: <strong>‘alter_geschlecht_zensus_det.xlsx’, ‘vaccination_landkreis_raw.dta’, ‘census2020_age_gender.csv’</strong> (all links to the sources can be found in the script ‘06_AppendixS7.do’)</li> <li>For Appendix S10: ‘<strong>vaccination_landkreis_raw.dta’</strong> (all links to the sources can be found in the script ‘07_AppendixS10.do’)</li> </ul> <p><strong>Descriptions of scripts</strong></p> <p><strong>1_1_clean_wave1.do</strong><br> This script processes the raw data from wave 1, the survey experiment<br> <strong>1_2_clean_wave2.do</strong><br> This script processes the raw data from wave 2, the follow-up survey<br> <strong>1_3_merge_generate.do</strong><br> This script creates the datasets used in the main analysis and for robustness checks by merging the cleaned data from wave 1 and 2, tests the exclusion criteria and creates additional variables<br> <strong>02_analysis.do</strong><br> This script estimates regression models in Stata, creates figures and tables, saving them to <strong>results/figures and results/tables</strong><br> <strong>03_robustness_checks_no_exclusion.do</strong><br> This script runs the main analysis using the dataset without applying the exclusion criteria. Results are saved in <strong>results/tables</strong><br> <strong>04_figure2_germany_map.do</strong><br> This script creates Figure 2 in the main manuscript using publicly available data on vaccination numbers in Germany.<br> <strong>05_figureS1_dogmatism_scale.do</strong><br> This script creates Figure S1 using data from a pretest to adjust the dogmatism scale.<br> <strong>06_AppendixS7.do</strong><br> This script creates the figures and tables provided in Appendix S7 on the representativity of our sample compared to the German average using publicly available data about the age distribution in Germany.<br> <strong>07_AppendixS10.do</strong><br> This script creates the figures and tables provided in Appendix S10 on the external validity of vaccination rates in our sample using publicly available data on vaccination numbers in Germany.</p>
Lex-Atlas:Covid-19 Parliaments Dataset
<p>Data on the impact on national parliaments resulting from the Covid-19 pandemic mined from country reports published by the Lex-Atlas: Covid-19 project and the Oxford University Press. For more information see https://lexatlas-c19.org</p>
Treating anti-vax patients, a new stressor for COVID-19 center doctors. Data from a 2-year prospective study.
<p>Dataset of the study "Treating anti-vax patients, a new stressor for COVID-19 center doctors. Data from a 2-year prospective study."</p>
Planned behaviour in the COVID-19 context
<p>This dataset contains information about the intentions of Spanish tourists to travel to destinations that have a low impact of COVID-19, their risk perceptions in times of uncertainty, and past behaviour.</p>
Dataset Timeline Covid-19
<p>O projeto Timeline Covid-19 tem como objetivo produzir, através da recuperação de informações online, notícias veiculadas pela grande mídia e pelas instituições federais a respeito de acontecimentos e fatos ocorridos em solo brasileiro ligados à Covid-19. Consiste em uma linha do tempo iniciada no dia 26 de fevereiro de 2020, data da confirmação do primeiro caso do novo coronavírus no Brasil, e não possui uma data final pré-definida, continuando a ser alimentada até o momento [24 mar 2022].</p> <p>A planilha é o <em>dataset</em> base para a Timeline (usando tecnologia <a href="http://timeline.knightlab.com/">Timeline JS</a> do<a href="https://knightlab.northwestern.edu/"> KnightLab</a> da <a href="https://www.northwestern.edu/">Northwestern University</a>, EUA) publicada no portal do Ministério da Ciência, Tecnologia e Inovações no combate à COVID-19 <<a href="http://covid19.mctic.gov.br/graf/">http://covid19.mctic.gov.br/graf/</a>>. Neste <em>dataset</em> está presente o conteúdo da linha do tempo completa.</p> <p>Na planilha, as colunas de A a I são relacionadas à data de início e encerramento de um evento. Como a proposta da linha do tempo é tratar de notícias diárias, apenas uma data foi mantida para marcar seu acontecimento e não denotar sua continuidade. As demais colunas consistem em:</p> <ul> <li> <p><strong>Headline</strong>: o título da notícia, sintetiza o conteúdo da reportagem e o exibe nas duas seções da linha do tempo;</p> </li> <li> <p><strong>Text</strong>: uma breve sinopse ou descrição do que trata a notícia para ser exibida no detalhamento da linha do tempo;</p> </li> <li> <p><strong>Media</strong>: link para a imagem exibida na descrição da notícia presente na segunda seção;</p> </li> <li> <p><strong>Media Credit</strong>: indicação do veículo de informação que publicou a notícia referendada;</p> </li> <li> <p><strong>Media Caption</strong>: link para a notícia publicada;</p> </li> <li> <p><strong>Background</strong>: código para a cor a ser utilizada no fundo da seção inferior da linha do tempo.</p> </li> </ul> <p>Disponibilizamos aqui atualizações da planilha onde cada versão é mais completa que sua antecessora. Além disso, também promovemos o trabalho de troca dos links que quebraram ao longo do tempo.</p>
Contact tracing solutions for COVID-19: applications, data privacy and security :: Suplementary Material
<p>Supplementary Material for the paper "Contact tracing solutions for COVID-19: applications, data privacy and security"</p>
Extracellular Vesicles Analysis in the COVID-19 Era: Insights on Serum Inactivation Protocols towards Downstream Isolation and Analysis
<p>Representative AFM images of the samples analyzed in the relative manuscript. Raw data just imported from the AFM multimode native format to the Gwyddion Open source data analysis software</p>
A subset dataset of COVID-19 Blood Atlas for CellDrift input
<p>A subset dataset of COVID-19 Blood Atlas for CellDrift input. The original data can be found in this paper: <a href="https://doi.org/10.1016/j.cell.2022.01.012">https://doi.org/10.1016/j.cell.2022.01.012</a>. We did subsetting on the data and extracted 116,124 cells covering 8 disease conditions, 6 PBMC cell types and a series of time points (days since onset) ranging from day 0 to day 25. </p>
Moral Values of Twitter COVID-19 Vaccine Data
<p>This data is part of our accepted paper "Learning to Adapt Domain Shifts of Moral Values via Instance Weighting" at the 33rd ACM Conference on Hypertext and Social Media (HT ’22). We annotate moral values of COVID-19 vaccine-related tweets. </p>
Risk of bias assessments for the Cochrane review 'SARS-CoV-2-neutralising monoclonal antibodies to prevent COVID-19'
<p>Risk of bias assessments and support for judgement with ROB 2 tool for the Cochrane Review: SARS-CoV-2-neutralising monoclonal antibodies to prevent COVID-19.</p>
Influence of conspiracy theories and distrust of community health volunteers on adherence to COVID-19 guidelines and vaccine uptake in Kenya
<p>This cross-sectional study collected data between 25 May –27 June 2021 n=447. It involved all registered community health volunteers (CHVs) who had participated in the COVID-19 vaccine hesitancy study. This data was collected as part of an Epidemic Ethics/WHO initiative that FCDO/Wellcome Grant 214711/Z/18/Z has supported. WHO’s specific grant number was 2020/1077878-0). The funders had no role in study design, data collection and analysis, decision to publish, or manuscript preparation. No authors received a salary from the funders.</p>
Hyperglycemia and steroid use increase the risk of rhino-orbito-cerebral mucormycosis regardless of COVID-19 hospitalization: Case-control study, India
<p><strong>Abstract</strong></p> <p><strong><em>BACKGROUND</em></strong></p> <p>In the context of the ongoing COVID-19 pandemic increased incidence of ROCM was noted in India, among those infected with COVID. We determined risk factors for rhino-orbito-cerebral mucormycosis (ROCM) post Coronavirus disease 2019 (COVID-19) among those never and ever hospitalized for COVID-19 separately through a multi-centric, hospital-based, unmatched case-control study across India.</p> <p><strong><em>METHODS</em></strong></p> <p>We defined cases and controls as those with and without post-COVID ROCM, respectively. We compared their socio-demographics, comorbidities, steroid use, glycaemic status, and practices. We calculated crude and adjusted odds ratio (AOR) with 95% confidence intervals (CI) through logistic regression. The covariates with p-value for crude OR of less 0·20 were considered for the regression model.</p> <p><strong><em>RESULTS</em></strong></p> <p>Among hospitalised, we recruited 267 cases and 256 controls and 116 cases and 231 controls among never hospitalised. Risk factors (AOR; 95% CI) for post-COVID ROCM among the hospitalised were age 45-59 years (2·1; 1·4 to 3·1), having diabetes mellitus (4·9; 3·4 to 7·1), elevated plasma glucose (6·4; 2·4 to 17·2), steroid use (3·2; 2 to 5·2) and frequent nasal washing (4·8; 1·4 to 17). Among those never hospitalised, age ≥ 60 years (6·6; 3·3 to 13·3), having diabetes mellitus (6·7; 3·8 to 11·6), elevated plasma glucose (13·7; 2·2 to 84), steroid use (9·8; 5·8 to 16·6), and cloth facemask use (2·6; 1·5 to 4·5) were associated with increased risk of post-COVID ROCM.</p> <p><strong><em>CONCLUSIONS</em></strong></p> <p>Hyperglycemia irrespective of having diabetes mellitus and steroid use was associated with increased risk of ROCM independent of COVID-19 hospitalisation. Rational steroid usage and glucose monitoring may reduce the risk of post-COVID.</p>
COVID-19 epidemic in Fiji
<p>This study involves the estimation of a key epidemiological parameter for evaluating and monitoring the transmissibility of a disease. The time-varying reproduction number is the index for quantifying the transmissibility of infectious diseases. Accurate and timely estimation of the time-varying reproduction number is essential for optimising non-pharmacological interventions and movement control orders during epidemics. The time-varying reproduction number for the second wave of the pandemic in Fiji is estimated using the popular EpiEstim R package and the publicly available COVID-19 data from 19 April 2021 to 01 December 2021. Our findings show that the non-pharmacological interventions and movement control orders introduced and enforced by the Fijian Government had a significant impact in preventing the spread of COVID-19. Moreover, the results show that many restrictions were either relaxed or eased when the time-varying reproduction number was below the threshold value of 1. The results have equipped some information on the second wave of the COVID-19 pandemic that could be used in the future as a guide for public health policymakers in Fiji. Estimation of time-varying reproduction numbers would be helpful for continuous monitoring of the effectiveness of the current public health policies that are being implemented in Fiji.</p>
Risk of bias assessments and support for judgement with ROB 2 tool for the Cochrane Review: Janus kinase inhibitors for the treatment of COVID-19
<p>Risk of bias assessments and support for judgement with ROB 2 tool for the Cochrane Review: Janus kinase inhibitors for the treatment of COVID-19.</p>
Is Open Data Strategy for Covid-19 used for other global health threats? A systematic review of the literature
<p>Dataset result of the literature review run between march and may 2022 on PubMed, Cinahl, Scopus and Google Scholar about open data and growing trend in scientific research about infection risk comparing Covid-19, Anti Microbial Resistance (AMR), and Health Assistance Correlated Infections (HAIs).</p>
Risk of bias assessments for version 5 of the Cochrane review 'Convalescent plasma for people with COVID-19'
<p>Risk of bias assessments and support for judgement with ROB 2 tool for the Cochrane Living Systematic Review: Convalescent plasma for people with COVID-19 (Version 5). </p>
The impact of stress and its influencing factors among dentists during the COVID-19 pandemic in Kingdom of Bahrain
<p><strong><span>Background:</span></strong><span> It is well known that all medical professions are linked to work stress, including dentistry, which is seen as facing higher risk due to the nature of the job, especially the working conditions. </span></p> <p><span><strong>Objective:</strong> This study aimed to assess the impact of stress and its influencing factors among dentists during the COVID-19 pandemic in Bahrain.</span></p> <p><strong><span>Methods</span></strong><span><strong>:</strong> A cross-sectional survey was designed to assess the impact of stress and its influencing factors among Bahraini dentists. A total of 306 participants were randomly selected from 1489 registered professionals in the NHRA (National Health Regulatory Authority Bahrain). In addition, an online survey was used to minimise face-to-face communication as well as to accommodate dental practitioners who work in private and government hospitals in Bahrain and a convenient sample of dentists was requested to participate in this study.</span></p> <p><strong><span>Results</span></strong><span><strong>:</strong> Out of 306 participants invited in the survey, only 253 responded, which was adequate for the study. Overall, the participants have reported moderate stress. All the variables of the study showed different effects, but the highest stressor with a strong correlation was "fear of social isolation "(FI) at the significance level of 0.01 (β= 0.393, t= 5.090, p < 0.05= (0.000) with </span> <span>= 0.201 above 0.15 and less than 0.35 which was considered as a moderate effect size approximately (20%), which strongly supported the hypothesis that researchers have proposed. Overall, the total effect for all stressors were (30 %) which was considered as a moderate effect size. All hypotheses were supported except BCP -> OUTCOME due to insufficient evidence at the insignificant level of 0.01 (β= -0.184, t=1.560, p > 0.05 = (0.060). whereas the R² values of independent variables were above 95% for the variance of dentists' outcome, which is considered an excellent fit to the data as evidenced by the squared multiple correlations (</span> <span>) values for the dependent variables.</span></p> <p><span><strong>Conclusions:</strong> </span><span>The study is unique based on its findings that reveal the impact of stress among dentists. Moreover, the results of this study may serve as guidance for future monitoring of dental practitioners' burnout, anxiety, and workload. Furthermore, it may provide supports in different aspects.</span></p>
Perspectives and experiences of Covid-19: Two Irish studies of families in disadvantaged communities
<p>Data files related to the manuscript <em>Perspectives and experiences of Covid-19: Two Irish studies of families in disadvantaged communities</em>. The manuscript includes two studies. The following materials are shared below.</p> <p>Study 1: </p> <p>- Qualitative data (Microsoft Office Excel file)</p> <p>- Codebook for coding the qualitative data developed through content analysis (pdf file)</p> <p>Study 2:</p> <p>- Qualitative data (Microsoft Office Excel file)</p> <p>Data are named using the following naming convention: Project acronym_Date (YYYYMMDD)_Study_Type of data_Type of participant_Version number of the file.</p> <p>Both studies in the manuscript were developed by the Childhood Development Initiative (CDI), Dublin, Ireland. Study 1 was conducted within the project PEAR_EC, that has received funding from the European Union’s Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie grant agreement No 890925. Study 2 was conducted within the Child Poverty research project, funded by Tusla under the Area Based Childhood funding and the Child and Youth Participation Initiatives grant.</p>
A dataset of anonymised hospitalised COVID-19 patient data: outcomes, demographics and biomarker measurements for two New York hospitals
<p>These datasets are for a cohort of n=1540 anonymised hospitalised COVID-19 patients, and the data provide information on outcomes (i.e. patient death or discharge), demographics and biomarker measurements for two New York hospitals: State<br> University of New York (SUNY) Downstate Health Sciences University and Maimonides<br> Medical Center.</p> <p>The file "demographics_both_hospitals.csv" contains the ultimate outcomes of hospitalisation (whether a patient was discharged or died), demographic information and known comorbidities for each of the patients.</p> <p>The file "dynamics_clean_both_hospitals.csv" contains cleaned dynamic biomarker measurements for the n=1233 patients where this information was available and the data passed our various checks (see https://doi.org/10.1101/2021.11.12.21266248 for information of these checks and the cleaning process). Patients can be matched to demographic data via the "id" column.</p> <p><strong>Study approval and data collection</strong></p> <p>Study approval was obtained from the State University of New York (SUNY) Downstate Health Sciences University Institutional Review Board (IRB\#1595271-1) and Maimonides Medical Center Institutional Review Board/Research Committee (IRB\#2020-05-07). A retrospective query was performed among the patients who were admitted to SUNY Downstate Medical Center and Maimonides Medical Center with COVID-19-related symptoms, which was subsequently confirmed by RT PCR, from the beginning of February 2020 until the end of May 2020. Stratified randomization was used to select at least 500 patients who were discharged and 500 patients who died due to the complications of COVID-19. Patient outcome was recorded as a binary choice of “discharged” versus “COVID-19 related mortality”. Patients whose outcome was unknown were excluded. Demographic, clinical history and laboratory data was extracted from the hospital’s electronic health records.</p>
Alternative Covid-19 mitigation measures in school classrooms: Analysis using an agent-based model of SARS-CoV-2 transmission
<p>The SARS-CoV-2 epidemic continues to have major impacts on children's education, with schools required to implement infection control measures that have led to long periods of absence and classroom closures. We have developed an agent-based epidemiological model of SARS-CoV-2 transmission that allows us to quantify projected infection patterns within primary school classrooms, and related uncertainties; the basis of our approach is a contact model constructed using random networks, informed by structured expert judgment. The effectiveness of mitigation strategies is considered in terms of effectiveness at suppressing infection outbreaks and limiting pupil absence. Covid-19 infections in schools in the UK in Autumn 2020 are re-examined and the model used for forecasting infection levels in autumn 2021, as the more infectious Delta-variant was emerging and school transmission was thought likely to play a major role in an incipient new wave of the epidemic. Our results are in good agreement with available data and indicate that testing-based surveillance of infections in the classroom population with isolation of positive cases is a more effective mitigation measure than bubble quarantine both for reducing transmission in primary schools and for avoiding pupil absence, even accounting for the insensitivity of self-administered tests. Bubble quarantine entails large numbers of pupils being absent from school, with only a modest impact on classroom infection levels. However, maintaining a reduced contact rate within the classroom can have a major beneficial impact on managing Covid-19 in school settings.</p>
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
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