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1,248 results for “Epidemiology”
Genomic Epidemiology Dataset for Important Nosocomial Pathogenic Bacteria Acinetobacter baumannii
<p>The<strong> </strong>infections caused by various bacterial pathogens both in clinical and community settings represent a significant threat to public healthcare worldwide. The growing resistance to antimicrobial drugs acquired by bacterial species causing healthcare-associated infections has already become a life-threatening danger noticed by the World Health Organization. Several groups or lineages of bacterial isolates usually called 'the clones of high risk' often drive the spread of resistance within particular species. </p><p>Thus, it is vitally important to reveal and track the spread of such clones and the mechanisms by which they acquire antibiotic resistance and enhance their survival skills. Currently, the analysis of whole genome sequences for bacterial isolates of interest is increasingly used for these purposes, including epidemiological surveillance and developing of spread prevention measures. However, the availability and uniformity of the data derived from the genomic sequences often represents a bottleneck for such investigations. </p><p>In this dataset, we present the results of a comprehensive genomic epidemiology analysis of 17,546 genomes of a dangerous bacterial pathogen <i>Acinetobacter baumannii</i>. Important typing information including multilocus sequence typing (MLST)-based sequence types (STs), intrinsic<i> blaOXA-51-like</i> gene variants, capsular (KL) and oligosaccharide (OCL) types, CRISPR-Cas systems, and cgMLST profiles are presented, as well as the assignment of particular isolates to nine known international clones of high risk. The presence of antimicrobial resistance genes within the genomes is also reported. </p><p>These data will be useful for researchers in the field of <i>A. baumannii</i> genomic epidemiology, resistance analysis and prevention measure development.</p>
Epidemiological and clinical characteristics predictive of ICU mortality of traumatic brain injury patients treated at a trauma reference hospital – A cohort study - Dataset
<p><strong>Dataset of a cohort whose summary is described below.</strong></p> <p><strong>ABSTRACT</strong></p> <p><strong>Background</strong>: Traumatic brain injury (TBI) has substantial physical, psychological, social and economic impacts, with high rates of morbidity and mortality. Considering its high incidence, the aim of this study was to identify epidemiological and clinical characteristics that predict mortality in patients hospitalized for TBI in intensive care units (ICUs). <strong>Methods</strong>: A retrospective cohort study was carried out with patients over 18 years old with TBI admitted to an ICU of a Brazilian trauma referral hospital between January 2012 and August 2019. TBI was compared with other traumas in terms of clinical characteristics of ICU admission and outcome. Univariate and multivariate analyses were used to estimate the odds ratio for mortality. <strong>Results</strong>: Of the 4816 patients included, 1114 had TBI, with a predominance of males (85.1%). Compared with patients with other traumas, patients with TBI had a lower mean age (45.3 ± 19.1 versus 57.1 ± 24.1 years, p < 0.001), higher median APACHE II (19 versus 15, p <0.001) and SOFA (6 versus 3, p < 0.001) scores, lower median Glasgow Coma Scale (GCS) score (10 versus 15, p < 0.001), higher median length of stay (7 days versus 4 days, p < 0.001) and higher mortality (27.6% versus 13.3%, p < 0.001). In the multivariate analysis, the predictors of mortality were older age (OR: 1.008 [1.002-1.015], p = 0.016), higher APACHE II score (OR: 1.180 [1.155-1.204], p < 0.001), lower GCS score for the first 24 hours (OR: 0.730 [0.700-0.760], p < 0.001), and greater number of brain injuries and presence of associated chest trauma (OR: 1.727 [1.192-2.501], p < 0.001). <strong>Conclusion</strong>: Patients admitted to the ICU for TBI were younger and had worse prognostic scores, longer hospital stays and higher mortality than those admitted to the ICU for other traumas. The independent predictors of mortality were advanced age, APACHE II score, first 24-hour GCS score, number of brain injuries and chest trauma.</p>
CHIKVnext: Molecular epidemiology of Chikungunya virus
<p>CHIKVnext is an interactive resource to study the evolution and global spread of Chikungunya virus (CHIKV), built on the Nextstrain platform.</p>
Supplementary dataset to publication: "Genomic epidemiology of Campylobacter fetus subsp. venerealis from Germany"
<p>Supplementary dataset to publication: "Genomic epidemiology of Campylobacter fetus subsp. venerealis from Germany" by Abdel-Glil Mostafa Y., Hotzel Helmut, Tomaso Herbert, Didelot Xavier, Brandt Christian, Seyboldt Christian, Linde Jörg, Schwarz Stefan, Neubauer Heinrich, El-Adawy Hosny. Genomic epidemiology of Campylobacter fetus subsp. venerealis from Germany. Frontiers in Veterinary Science. volume 9 (https://www.frontiersin.org/articles/10.3389/fvets.2022.1069062) <br> </p>
Python code for "Evolutionary epidemiology consequences of trait-dependent control of heterogeneous parasites"
<p>The file contains the Python code used to run the agent-based simulation of the selection-mutation model presented in "Evolutionary epidemiology consequences of trait-dependent control of heterogeneous parasites"</p>
Phylogenetic and epidemiologic data relating to age-specific HIV incidence and transmission in Rakai, Uganda, 2003-2018.
<p>This repository contains the data for the analyses presented in the paper Growing gender inequity in HIV infection in Africa: sources and policy implications by M. Monod, A. Brizzi, R. Galiwango, R. Ssekubugu, Y. Chen, X. Xi et al. available in the pre-print <a href="https://doi.org/10.1101/2023.03.16.23287351">https://doi.org/10.1101/2023.03.16.23287351</a> </p> <p>We thank all contributors, program staff and participants to the Rakai Community Cohort Study; all members of the PANGEA-HIV consortium, the <a href="https://www.rhsp.org/index.php">Rakai Health Sciences Program</a>, and CDC Uganda for comments on an earlier version of the manuscript.</p> <p>We also extend our gratitude to the <a href="https://doi.org/10.14469/hpc/2232">Imperial College Research Computing Service</a> and the <a href="https://www.bdi.ox.ac.uk/about/biomedical-research-computing">Biomedical Research Computing Cluster</a> at the University of Oxford for providing the computational resources to perform this study. Additionally, we thank the Office of Cyberinfrastructure and Computational Biology at the <a href="https://www.niaid.nih.gov/">National Institute for Allergy and Infectious Diseases</a> for data management support; and Zulip for sponsoring team communications through the Zulip Cloud Standard chat app. </p> <p>All analysis code is available from <a href="https://github.com/MLGlobalHealth/phyloSI-RakaiAgeGender">https://github.com/MLGlobalHealth/phyloSI-RakaiAgeGender</a>.</p>
Adjoint-based Data Assimilation of an Epidemiology Model for the Covid-19 Pandemic in 2020 --- Data Files
<p>New data on github:</p> <p>https://github.com/sesterhenn/Corona-DataAssimilation</p> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p> <p>doi://10.5281/zenodo.3732292</p> <p>https://zenodo.org/record/3733244</p>
A Multi-Site Investigation into the Epidemiology of Chikungunya Virus in Neglected Regions of Indonesia
<p>Supporting datasets for phylogenetic analysis of Indonesian chikungunya virus sequences using BEAST v1.10.4.</p> <p> </p>
African Swine Fever Worldwide Epidemiology Data - OIE Webscrape example - Geocoded using Google API and Manual
<p>Example African Swine Fever dataset generated by programs described in following publication </p> <p>Title: Web-scraping programmatic techniques in aggregating difficult to access OIE WAHIS animal disease outbreak information; using African Swine Fever in Europe as an example.</p> <p>Short running title: Methods for web-scraping OIE WAHIS data.</p> <p>Abstract: This study describes and makes available new methods for acquiring difficult to access, publicly available, disease surveillance data. It uses World Organisation for Animal Heath (OIE) data on African Swine Fever (ASF) outbreaks in Belarus and its neighbouring European countries to showcase the importance of adequate disease surveillance data to inform decision-making. The data acquired from these methods allow for large-scale, geospatial outbreak mapping and summary statistics of any terrestrial disease listed on the OIE World Animal Health Information System (WAHIS) database. These techniques will make important epidemiological data more accessible to the scientific community and aid in gaining further insight into the occurrence and spread of OIE listed diseases in a timely manner, fulfilling an important function of disease surveillance.</p>
An epidemiological overview of the equine influenza epidemic in Great Britain during 2019: Dataset
<p>This repository contains datasets and code used for the manuscript as titled. All details regarding the data source and considerations that should be noted are discussed in the manuscript.</p> <p><strong>Referencing this dataset</strong></p> <p>Fleur Whitlock, John Grewar & J. Richard Newton (2022) An epidemiological overview of the equine influenza epidemic in Great Britain during 2019 [Dataset]. University of Cambridge. <a href="https://doi.org/10.5281/zenodo.5886153">https://doi.org/10.5281/zenodo.7010228</a></p> <p> </p>
Trajetorias dataset: environmental, epidemiological, and economic indicators for the Brazilian Amazon
<p>The Trajetorias dataset is a harmonized set of environmental, epidemiological, and poverty indicators for all municipalities of the Brazilian Legal Amazon (BLA). This dataset is the result of a scientific synthesis research initiative conducted by scientists from several natural and social sciences fields, consolidating multidisciplinary indicators into a coherent dataset for integrated and interdisciplinary studies of the Brazilian Amazon. The Trajetorias dataset is organized in dimensions describing: environmental degradation, land use and land cover, human mobility, climate anomalies, the burden of vector-borne diseases, and poverty indices for rural and urban populations for each of the BLA municipalities. Characterizing the environmental, epidemiological, and socioeconomic profile of the municipalities. These indicators were designed to unveil the specificities of the Amazon region, so that the relationships between these dimensions can be explored regarding past and current enacted policies. The Trajetorias dataset relies on four surveys - the two demographic censuses conducted in 2000 and 2010, and the two agrarian censuses conducted in 2006 and 2017, from which were defined fixed timestamps for analysis. The demographic censuses are the source of data for the multidimensional poverty indices. Environmental data come from satellite images collected by several national and international programs, such as the Amazon Deforestation Monitoring Program (PRODES), DEGRAD, and DETER, accounting for changes in landscape that took place between each demographic census and the subsequent agrarian census. Lastly, disease control data was obtained from the National Disease Notification System and summarized for the 5-year period centered in the agrarian censuses.</p>
Epidemiology and Disease Burden of Neurocritical Disorders: A Cohort Study - Data Sharing
<p>Dataset of a Neurocritical Brazil cohort study whose summary is described below.</p> <p><strong>Abstract</strong></p> <p><strong>Objective:</strong> To describe a cohort of neurocritical patients and their differences based on primary neurological diagnoses and identify predictors of mortality and unfavorable outcome along with the disease burden of each neurological condition on intensive care unit (ICU) admission. <strong>Methods:</strong> Prospective cohort study including patients admitted to 36 ICUs in Brazil and followed up for 30 days. <strong>Results:</strong> Of 4245 patients admitted to the participating ICUs during the study period, 1194 (28.1%) were neurocritical patients and were included in the study. Neurocritical patients had a mean mortality rate 1.7 times higher than non-neurocritical patients admitted to the same ICUs (17.21% versus 10.1%, respectively). The most frequent primary neurological diagnoses on ICU admission were postoperative care of elective neurosurgery, traumatic brain injury, ischemic stroke, and encephalopathy. The estimated total disability-adjusted life-years (DALYs) were 4482.94 in the overall cohort, and the diagnosis with the highest DALYs was traumatic brain injury (1634.42). DALYs were significantly impacted by the patients’ primary neurological diagnosis, sex, age group, and number of secondary neurological injuries. <strong>Conclusion:</strong> We accurately described the epidemiology of neurocritical patients and estimated their overall and relative disease burden. The findings of this study are important to direct policies regarding education, prevention, and treatment of severe neurocritical diseases.</p> <p> </p>
Epidemiology of onchocerciasis-associated epilepsy in the Mbam and Sanaga river valleys of Cameroon: impact of more than 13 years of ivermectin
<p>Dataset contains information collected during door-to-door epilepsy surveys in onchocerciasis-endemic villages of Cameroon.</p>
Global Macroeconomic Scenarios of the COVID-19 Pandemic: Epidemiological Assumptions
<p>Epidemiological Assumptions used for modelling the Global Macroeconomic Scenarios of the COVID-19 Pandemic</p>
Epidemiology, risk factors and clinical course of SARS-CoV-2 infected patients in a Swiss university hospital: an observational retrospective study
<p>This is the dataset of the study called "Epidemiology, risk factors and clinical course of SARS-CoV-2 infected patients in a Swiss university hospital: an observational retrospective study". <br> <br> <strong>Abstract: </strong></p> <p>Background<br> Coronavirus disease 2019 (COVID-19) is now a global pandemic with Europe and the USA at its epicenter. Little is known about risk factors for progression to severe disease in Europe. This study aims to describe the epidemiology of COVID-19 patients in a Swiss university hospital.</p> <p>Methods<br> This retrospective observational study included all adult patients hospitalized with a laboratory confirmed SARS-CoV-2 infection from March 1 to March 25, 2020. We extracted data from electronic health records. The primary outcome was the need to mechanical ventilation at day 14. We used multivariate logistic regression to identify risk factors for mechanical ventilation. Follow-up was of at least 14 days. <br> <br> Results<br> 200 patients were included, of whom 37 (18·5%) needed mechanical ventilation at 14 days. The median time from symptoms onset to mechanical ventilation was 9·5 days (IQR 7.00, 12.75). Multivariable regression showed increased odds of mechanical ventilation in males (3.26, 1.21-9.8; p=0.025), in patients who presented with a qSOFA score ≥2 (6.02, 2.09-18.82; p=0.001), with bilateral infiltrate (5.75, 1.91-21.06; p=0.004) or with a CRP of 40 mg/l or greater (4.73, 1.51-18.58; p=0.013). <br> <br> Conclusions<br> This study gives some insight in the epidemiology and clinical course of patients admitted in a European tertiary hospital with SARS-CoV-2 infection. Male sex, high qSOFA score, CRP of 40 mg/l or greater and a bilateral radiological infiltrate could help clinicians identify patients at high risk for mechanical ventilation.</p>
Figure 3. from: Visual Parkinson's Disease Rating Scale: A Universal Iconic Questionnaire for Epidemiological Studies in India - Research Ideas and Outcomes 2: e8834 (03 May 2016) https://doi.org/10.3897/rio.2.e8834
Figure 3. - TimelineThis Gantt chart provides an estimate of the relative timing and duration for achieving each of the Aims.
Figure 1. from: Visual Parkinson's Disease Rating Scale: A Universal Iconic Questionnaire for Epidemiological Studies in India - Research Ideas and Outcomes 2: e8834 (03 May 2016) https://doi.org/10.3897/rio.2.e8834
Figure 1. - First pass at a VPDRS static graphicFigure 1 corresponds to the first self-administered MDS-UPDRS question:1.7 SLEEP PROBLEMS.Over the past week, have you had trouble going to sleep at night or staying asleep through the night? Consider how rested you felt after waking up in the morning.0: Normal: No problems.1: Slight: Sleep problems are present but usually do not cause trouble getting a full night of sleep.2: Mild: Sleep problems usually cause some difficulties getting a full night of sleep.3: Moderate: Sleep problems cause a lot of difficulties getting a full night of sleep, but I still usually sleep for more than half the night.4: Severe: I usually do not sleep for most of the night."
Figure 2. from: Visual Parkinson's Disease Rating Scale: A Universal Iconic Questionnaire for Epidemiological Studies in India - Research Ideas and Outcomes 2: e8834 (03 May 2016) https://doi.org/10.3897/rio.2.e8834
Figure 2. - Prototype for the mobile phone appThis screen shows a pre-release version of Node, which will support the VPDRS/UPDRS modules. Here we present a means by which a person administering a questionnaire can securely log into and manipulate patient information locally and through cloud services and lastly an example clinician-administered UPDRS question.
The genomic and epidemiological virulence patterns of Salmonella enterica serovars in the United States
<p>The serovars of <i>Salmonella enterica </i>display dramatic differences in pathogenesis and host preferences. We developed a process (patent pending) for grouping <i>Salmonella</i> isolates and serovars by their public health risk. We collated a curated set of 12,337 <i>S. enterica</i> isolate genomes from human, beef, and bovine sources in the US. After annotating a virulence gene catalog for each isolate, we used unsupervised random forest methods to estimate the proximity (similarity) between isolates based upon the genomic presentation of putative virulence traits We then grouped isolates (virulence clusters) using hierarchical clustering (Ward's method), used non-parametric bootstrapping to assess cluster stability, and externally validated the clusters against epidemiological virulence measures from FoodNet, the National Outbreak Reporting System (NORS), and US federal sampling of beef products. We identified five stable virulence clusters of <i>S. enterica</i> serovars. Cluster 1 (higher virulence) serovars yielded an annual incidence rate of domestically acquired sporadic cases roughly one and a half times higher than the other four clusters combined (Clusters 2-5, lower virulence). Compared to other clusters, cluster 1 also had a higher proportion of infections leading to hospitalization and was implicated in more foodborne and beef-associated outbreaks, despite being isolated at a similar frequency from beef products as other clusters. We also identified subpopulations within 11 serovars. Remarkably, we found <i>S.</i> Infantis and<i> S.</i> Typhimurium subpopulations that significantly differed in genome length and clinical case presentation. Further, we found that the presence of the pESI plasmid accounted for the genome length differences between the <i>S. </i>Infantis subpopulations. Our results show that <i>S. enterica</i> strains associated with highest incidence of human infections share a common virulence repertoire. This work could be updated regularly and used in combination with foodborne surveillance information to prioritize serovars of public health concern. </p><p>Files contained in this repository will reproduce elements of figures 3,4,6, and 7 of the accompanying PLOS One manuscript. </p><p> </p>
Fig. 2. Unsporulated T. gondii oocysts, with a in Exploring the epidemiological role of the Eurasian lynx (Lynx lynx) in the life cycle of Toxoplasma gondii
Fig. 2. Unsporulated T. gondii oocysts, with a diameter of 10–12 μm, after flotation from a faecal sample of a juvenile lynx (left) (ID W20_8385). T. gondii development stage (meront, arrow) in a histological section of small intestine of a lynx (right) (ID W21_4446).
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.