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1,173 results for “Pandemics”
The Impact of the COVID-19 Pandemic on Women's Contribution to Public Code - Replication Package
<p>Replication package for the article "The Impact of the COVID-19 Pandemic on Women's Contribution to Public Code"</p>
Supplementary Materials for Respiratory Syncytial Virus Genetic Diversity and Lineage Changes in Ireland pre- and post-COVID-19 pandemic
<p><span>The datasets presented here are the list of publicly available reference sequences used in NextClade (</span><a href="https://github.com/rsv-lineages"><span>https://github.com/rsv-lineages</span></a><span>) to provide the lineage classification of samples and used as reference for analysis of Irish RSV sequences (Supplementary Table 1), the list of sequenced Human respiratory syncytial viruses (RSV) in the Republic of Ireland during the period 2015-2024 (Supplementary Table 2) and the details of sequences downloaded from GISAID corresponding to European RSV sequences for the same period of time.</span></p> <p><span> </span></p> <p><span>Description of the data and file structure</span></p> <p><span>The files in this dataset were formatted in Microsoft Excel 2019 to allow easy access and manipulation of the data. The data corresponds to details on the sequences used in the analysis of RSV in Europe during the 2015-2024 period. The contents of the files are described below: -<strong>Supplementary Table 1</strong>: NextClade Reference Sequences publicly available at </span><a href="https://github.com/rsv-lineages"><span>https://github.com/rsv-lineages</span></a><span>. -<strong>Supplementary Table 2</strong>: Irish RSV Sequence Details contains details on the sequences generated in our study with the GenBank accession number, the GISAID accession number, reported collection RSV season, Nextclade assigned taxonomical clade. -<strong><span>Supplementary Table 3</span></strong>: European RSV Sequence Details contains a list of European RSV sequences downloaded from GISAID (</span><a href="https://gisaid.org/"><span>https://gisaid.org/</span></a><span>) with details on the sampling RSV season, the accession number, country of collection, and lineage assigned by NextClade.</span></p> <p><span> </span></p> <p><span>Sharing/Access information</span></p> <p><span>The databases used to extract and deposit the data were:</span></p> <p><span>-GenBank: the North American repository of sequences and publicly available at: </span><a href="https://www.ncbi.nlm.nih.gov/genbank/"><strong><span>https://www.ncbi.nlm.nih.gov/genbank/</span></strong></a><span> </span></p> <p><span>-GISAID:International consortium of sequences with some metadata and clinical data. It is a semi-public repository with easy access requiring only to create an account. Available at: </span><a href="https://gisaid.org/"><strong><span>https://gisaid.org/</span></strong></a></p> <p><span>-Github: reference sequences of RSV are available at the repository </span><a href="https://github.com/"><strong><span>https://github.com/</span></strong></a></p>
Examining the oil price and renewable energy price nexus: Comparative wavelet analysis for the aftermath of 2008 financial crisis, shale oil crisis and COVID-19 pandemic.
<p><span>In this study, we conducted a wavelet analysis on the dependence between renewable energy indices and Brent oil index (Brent) for the period of 21st November, 2003 till 24th May, 2024. The objective of the paper includes comparing the co-movement of renewable energy stock prices and oil prices during three different crises including the global financial crisis, shale oil crisis and the covid-19 pandemic. We found that the dependence is similar for both Europe and on a global scale during the pre-crises time, where renewable energy prices lead Brent oil prices in the short and medium term. Furthermore, results confirm that there is substitutability between oil prices and renewable energy prices before all crises which shows a positive correlation. The results further show that the short and medium term dependence disappears after the oil crisis and financial crisis which is supported by the sudden loss in demand for oil. These findings show that co-movement changes between the three crises where there is no dependence between the indices after the financial and oil crisis while there is a negative correlation after the covid-19 pandemic. These findings could have significant ramifications for investors seeking to mitigate risks and for policymakers making decisions about supporting the advancement of renewable energy while understanding the change of behaviour between the two crises.</span></p>
Figure 1 in CHIRONOMID RESEARCH THROUGH PANDEMICS, RECENT ADVANCES Abstract
Figure 1. Group photo on Zoom video conference system at the final forum of ISC2022.
Educational data collected from students - regarding the analysis of online activities in schools in Romania (during the Covid-19 pandemic, March 2020 - April 2020)
<p>The student questionnaire was designed with 20 questions. It was completed by 1,088 respondents and focuses on students' experiences related to online education. The collected data provides a broad perspective on various aspects of this, including access to technology, experiences with different platforms, perceptions of the advantages and disadvantages of this form of education, as well as direct feedback from students regarding their experiences. The full questionnaire can be accessed at: <a href="https://forms.gle/fhgzCUx1SDnxbCfZ6" target="_new" rel="noopener"><strong>https://forms.gle/fhgzCUx1SDnxbCfZ6</strong></a></p> <p>To protect the identity of the respondents and to obtain accurate responses, all data collected from teachers was anonymous. We did not collect any personal information whatsoever. This aspect was made clear to the respondents in the description of the questionnaire.</p>
Educational data collected from teachers - for the analysis of online activities in schools in Romania (during the Covid-19 pandemic, March 2020 - April 2020)
<p>The dataset comes from a questionnaire structured into 24 questions, which can be accessed at <a href="https://forms.gle/bUgYMfoNHh7r6ebs6" target="_new" rel="noopener">https://forms.gle/bUgYMfoNHh7r6ebs6</a>. This questionnaire was completed by 956 respondents and aims to analyze the online activities carried out during March - April 2020, being distributed to teachers.<br>Each question is designed to reveal different aspects of the experiences, skills, and perspectives of teaching staff regarding online teaching and learning.</p> <p>To protect the identity of the respondents and to obtain accurate responses, all data collected from teachers was anonymous. We did not collect any personal information whatsoever. This aspect was made clear to the respondents in the description of the questionnaire.</p>
Graphic Illustration of Rick White's Talk: Preventing the next viral pandemic via elucidation of mammalian reservoirs
<p><a href="https://lib.ku.edu/people/courtney-foat" target="_blank" rel="noopener">Courtney Foat</a>, Advisor for Strategic Initiatives & Organizational Engagement at the University of Kansas, graphically recorded this invited talk by Rick White at an NSF-supported Workshop: Digital Collections Data and Tracking Disease.</p>
Graphic Illustration of Cody Thompson's Talk: A Museum Perspective Preserve a Voucher Specimen! Tools for Pandemic Preparedness
<p><a href="https://lib.ku.edu/people/courtney-foat" target="_blank" rel="noopener">Courtney Foat</a>, Advisor for Strategic Initiatives & Organizational Engagement at the University of Kansas, graphically recorded this invited talk by Cody Thompson at an NSF-supported Workshop: Digital Collections Data and Tracking Disease.</p>
Evolutionary trends in the compositional structure of the SARS-CoV-2 genome during the pandemic
<p>By computing a measure of compositional genome structure in random datasets of coronavirus genomes, we observed a long-term decreasing trend in this measure, accompanied by an increasing evolutionary rate.</p> <p> </p>
Dataset of Psychological Impact of the COVID-19 Pandemic on Families of People with Severe Mental Disorders in Brazil
<p>The objective of the study is to understand how the families of patients with severe mental disorders experienced the beginning of the pandemic in Brazil and how they fared during the period of restricted social contact.</p>
Epidemiological geography at work. An exploratory review about the overall findings of spatial analysis applied to the study of CoViD-19 propagation along the first pandemic year (DATASET)
<p><strong>Literature review dataset</strong></p> <p>This table lists the surveyed papers concerning the application of spatial analysis, GIS (Geographic Information Systems) as well as general geographic approaches and geostatistics, to the assessment of CoViD-19 dynamics. The period of survey is from January 1<sup>st</sup>, 2020 to December 15<sup>th</sup>, 2020. The first column lists the reference. The second lists the date of publication (preferably, the date of online publication). The third column lists the Country or the Countries and/or the subnational entities investigated. The fourth column lists the epidemiological data utilized in each paper. The fifth column lists other types of data utilized for the analysis. The sixth column lists the more traditionally statistically-based methods, if utilized. The seventh column lists the geo-statistical, GIS or geographic methods, if utilized. The eight column sums up the findings of each paper. The papers are also classified within seven thematic categories. The full references are available at the end of the table in alphabetical order.</p> <p>This table was the basis for the realization of a comprehensive geographic literature review. It aims to be a useful tool to ease the "due-diligence" activity of all the researchers interested in the spatial analysis of the pandemic.</p> <p>The reference to cite the related paper is the following:</p> <p><strong>Pranzo, A.M.R., Dai Prà, E. & Besana, A. Epidemiological geography at work: An exploratory review about the overall findings of spatial analysis applied to the study of CoViD-19 propagation along the first pandemic year. GeoJournal (2022). https://doi.org/10.1007/s10708-022-10601-y</strong></p> <p>To read the manuscript please follow this link: <strong>https://doi.org/10.1007/s10708-022-10601-y</strong></p> <p> </p>
Pandemic, epidemic, endemic: B cell repertoire analysis reveals unique anti-viral responses to SARS-CoV-2, Ebola and Respiratory Syncytial Virus
<p>VDJ gene usage, and associated amino acid sequences and properties, from healthy controls as well as patients with COVID-19, RSV or Ebola and Yellow fever vaccine recipients.</p>
Factors related to depression in older adults during the COVID 19 pandemic in two coastal regions of Peru: An analytical cross-sectional study
<p><strong>:</strong> To determine the factors associated with depression in older adults in two coastal regions of Peru during the COVID 19 pandemic</p>
Environmental Education During the COVID-19 Pandemic Dataset
<p>Data files that support the capstone thesis <em>Environmental Education during the COVID-19 Pandemic</em> by Margaret Janz.</p> <p> </p> <p>The COVID-19 pandemic that hit the United States in March 2020 quickly caused the entire nation to rethink how we work and how we approach education. Informal environmental education in particular faced unique challenges during this time yet found ways to adapt their educational programming. This thesis will explore the questions: How did organizations alter and adapt their programming to meet public health guidelines during the pandemic? and What successes and challenges did organizations face in offering these programs? To answer these questions, a survey was distributed to organizations that offer environmental education programs. Results echo and expand on the existing literature on pandemic education. Organizations offered many virtual programs both synchronously and asynchronously and also found new ways to offer in person events. Programs that involved some interaction with other people and those that took place in person or outdoors were most successful, while asynchronous virtual materials were considered less so. Organizations faced many challenges in offering programming including financial barriers, technology troubles, and difficulty staying current on public health recommendations. Despite the difficulties and learning losses, respondents felt there may have been some benefits to the changes in their programming and many will continue to offer their adapted programs in the future.</p>
University students' adherence and vaccination attitude during the COVID-19 pandemic
<p>Data on university students' adherence and vaccination attitudes, accompanying the manuscript a manuscript (in press). </p> <p>Data collection took place between November 23rd and December 21st, 2020.</p> <p>The sample consists of German-speaking university students, recruited at Innsbruck Universities.</p> <p> </p> <p>Scale Sources: <br> Reactance - COSMO COVID-19 Snapshot Monitoring <br> Betsch, C., Korn, L., Felgendreff, L., Eitze, S., Schmid, P., Sprengholz, P., Wieler, L., Schmich, P., Stollorz, V., Ramharter, M., Bosnjak, M., Omer, S., Thaiss, H., Bock, F. de, & Rüden, U. von (2020). <em>Covid-19 Snapshot Monitoring (COSMO Germany) - Welle 27</em>. PsychArchives. https://doi.org/10.23668/PSYCHARCHIVES.4381</p>
Data for "Reinfections and cross-protection in the 1918/19 influenza pandemic: Revisiting a survey among male and female factory workers"
<p>Dataset underlying the analysis of the paper: "Reinfections and cross-protection in the 1918/19 influenza pandemic: Revisiting a survey among male and female factory workers"</p> <p>The dataset includes the following variables:</p> <ul> <li>Sex - male, female</li> <li>Age2 - age in full years</li> <li>Grippe - influenza (oui = yes, non=no)</li> <li>Times_grippe - how often did the influenza occur (0-3)</li> <li>Reinfection - 0=no, 1=yes</li> <li>Reinfection_forte - was the reinfection stronger or the same (1) or weaker (0) as the first infection?</li> <li>Severeness - 1=mild, 2 and 3 = strong</li> <li>reinf_v1 - reinfection first wave 0=no, 1=yes</li> <li>vage1 - infection first wave 0=no, 1=yes</li> <li>reinf_v2 - reinfection second wave 0=no, 1=yes</li> <li>vage2 - infection second wave 0=no, 1=yes</li> <li>reinf_v3 - reinfection third wave 0=no, 1=yes</li> <li>vage3 - infection third wave 0=no, 1=yes</li> <li>reinf_y1919 - reinfection winter 1919 0=no, 1=yes</li> <li>vage_1919 - infection winter 1919 0=no, 1=yes</li> <li>Grippe1890 - illness 1890 0=no, 1=yes</li> </ul> <p> </p> <p> </p>
Comparative analysis of health authorities spokesperson and health influencer during the COVID-19 pandemic: A case in Indonesia
<p><span><strong>Background</strong>: </span><span>Concerns over an infodemic following a surge in health misinformation circulating on social media sets out the government's priority for Indonesia. Given the urgent work on the coronavirus disease 2019 (COVID-19) response, the government collaborated with health-related spokespersons and influencers with a medical background by starting a COVID-19 public education campaign on social media. A collaborative initiative involved health spokespersons from government and non-government to clarify misinformation about COVID-19.</span></p> <p><span><strong>Methods</strong>: </span><span>The primary purpose of this research is to compare government and non-government spokespersons by examining their role in educating about the COVID-19 vaccine and health services. This study employed comparative factor analysis and non-participatory observation toward the media activity of spokespersons in Indonesia. Using a questionnaire, this study examines the dimensions of public campaigns, risk communication, health and emergency, leadership, and communication from Indonesian spokespersons. The data collection was conducted in two stages. The first stage was a pilot study that collected data from 102 respondents, the second stage collected data from 276 respondents.</span></p> <p><span><strong>Results</strong>: </span><span>Findings show that utilizing the spokesperson is important due to its capabilities of reaching diverse audiences, and improving public engagement, trustworthiness, and credibility.</span></p> <p><span><strong>Conclusions</strong>: </span><span>With the combination of health authorities spokespersons and health influencers in Indonesia, this study provides valuable insights for communication management in developing and supporting the role of health authorities from the government, non-government as well as medical sectors.</span></p>
Global-scale modeling of early factors and country-specific trajectories of COVID-19 incidence: a cross-sectional study of the first 6 months of the pandemic
<p><span>Studies examining factors responsible for COVID-19 incidence </span><span>are</span><span> mostly focused at the national or sub-national level. A global-level characterization of contributing factors and temporal trajectories of disease incidence is lacking. Here we conducted a global-scale analysis of COVID-19 infections to identify key factors associated with early disease incidence. Additionally, we compared longitudinal trends of COVID-19 incidence at a per-country level and classified countries based on COVID-19 incidence trajectories and effects of lockdown responses. </span><span>Univariate analysis identified</span> <span>eleven variables as independently associated with COVID-19 infections at a global level (p<1e-05). Multivariable analysis identified a 4-variable model as optimal for explaining global variations in COVID-19 (p<0.01). COVID-19 case trajectories for most countries were best captured by a log-logistic model, as determined by AIC estimates. Six predominant country clusters were identified when characterizing the effects of lockdown intervals on variations in COVID-19 new cases per country.</span> <span>Globally, economic and meteorological factors are important determinants of early COVID-19 incidence. Analysis of longitudinal trends and lockdown effects on COVID-19 highlights important nuances in country-specific responses to infections. These results </span><span>provide valuable insights into disease incidence at a per-country level, possibly allowing for more informed decision making by individual governments in future disease outbreaks.</span></p>
dhaw/fluCodeImperial: Using real-time data to guide decision-making during an influenza pandemic: a modelling analysis
<p><strong>All codes and data used for "Using real-time data to guide decision-making during an influenza pandemic: a modelling analysis" are included in this folder. The file "runExamples.m" contains a step-by-step method for reproducing figures and running model fits. Ensure that all data and code files are in the same directory, then a single execution of “runExamples” on the command line will generate all main and supplementary figures in the manuscript. There is one line of code per figure, clearly marked, that can be commented out as desired. In order to run the MCMC adaptive algorithm, a single line of code, also clearly marked, must be commented back in. Instructions to change the single-state example are given at the top of the file “runExamples.m”. The saved state selection of California (“state=1”) is consistent with all results presented in the manuscript. </strong></p> <p><strong> </strong></p> <p><strong>Plots make use of files from the following sources, with some modifications:</strong></p> <p><strong>Holger Hoffmann (2022). Violin Plot (https://www.mathworks.com/matlabcentral/fileexchange/45134-violin-plot);</strong></p> <p><strong>Evan (2022). Plot Groups of Stacked Bars (https://www.mathworks.com/matlabcentral/fileexchange/32884-plot-groups-of-stacked-bars);</strong></p> <p><strong>John Onofrey (2022). Shaded Plots and Statistical Distribution Visualizations (https://www.mathworks.com/matlabcentral/fileexchange/69203-shaded-plots-and-statistical-distribution-visualizations)</strong></p>
Data for "From pandemic to endemic: Spatial-temporal patterns of influenza-like-illness incidence in a Swiss canton, 1918-1924"
<p>Datasets underlying the analysis of the paper: "From pandemic to endemic: Spatial-temporal patterns of influenza incidence in a Swiss canton, 1918-1924"</p> <ul> <li><strong>Cofactors_1918.xlsx</strong> : Ecological cofactors for each bernese municipility</li> <li><strong>Data_Influenza_Bern.xlsx: </strong>Influenza data (1920-1924)</li> <li><strong>Data_Spanishflu.xlsx: </strong>"Spanish flu" data (1918-1919)</li> <li><strong>Data_Population.xlsx: </strong>Population for each municipality</li> <li><strong>Tb_death.xlsx : </strong>Number of tuberculosis death</li> <li><strong>Fabrik_Statistik_1929.xlsx : </strong>Information about the factories</li> </ul>
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OpenNeuro
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