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22 results for “virus outbreak”
Multiple Introductions of Mpox virus to Ireland during the 2022-2023 International Outbreak
<p>Supplementary datasets for the study titled: '<strong>Multiple Introductions of Mpox virus to Ireland during the 2022-2023 International Outbreak</strong>'</p> <p>The datasets presented here are the list of publicly available sequences from other geographical locations used as reference for analysis of Irish mpox sequences (Supplementary Table 1), the list of sequenced mpox viruses (MPXV) in the Republic of Ireland during the outbreak 2022-2023 (Supplementary Table 2) and the list of MPXV genes with mutations in our dataset of Irish sequences (Supplementary Table 4).</p> <p><strong>Description of the data and file structure</strong></p> <p>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 the MPXV from the 2022-2023 international outbreak encompassing data between May 2022 and November of 2023. The contents of the files are described below:</p> <ul> <li><strong>Supplementary Table 1</strong>: This file contains the source (database as GenBank or GISAID) of the data, the accession number to allow its downloading, the mpox clade assigned by Nextclade (https://clades.nextstrain.org/), reported collection date of the sample, reported country of origin and the geographical region assigned.</li> <li><strong>Supplementary Table 2</strong>: This file contains details on the sequences generated in our study with the GenBank accession number, the GISAID accession number, reported collection date, Nextclade assigned taxonomical clade and the percentage of the genome covered by the sequence. </li> <li><em>(Supplementary Table 3)</em>: This table was omitted as it is small and summarizes the counts of mutations.</li> <li><strong>Supplementary Table 4</strong>: This file contains a distribution of mutated positions over the annotated MPXV genome relative to the reference Gene ID as provided in the annotation for NC_063383.1, with gene affected, start and end of the gene (relative to the reference genome GenBank:NC_063383.1), number of non-coding, non-synonymous and synonymous mutations in each gene, and the total of mutations per gene.</li> </ul> <p><strong>Sharing/Access information</strong></p> <p>The databases used to extract and deposit the data were:</p> <ul> <li><strong>GenBank</strong>: the North American repository of sequences and publicly available at: <strong><a href="https://www.ncbi.nlm.nih.gov/genbank/">https://www.ncbi.nlm.nih.gov/genbank/</a></strong></li> <li><strong>GISAID</strong>: 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: <strong><a href="https://gisaid.org/">https://gisaid.org/</a></strong></li> </ul>
Data for Seropositivity to Dengue virus DENV in three neighborhoods in the periphery of a city with a recent history of outbreaks in Argentina: what can we learn from unreported cases?
<p>This release include the datasets, R scripts and interactive maps generated for the manuscript: Seropositivity to Dengue virus DENV in three neighborhoods in the periphery of a city with a recent history of outbreaks in Argentina: what can we learn from unreported cases? Authors: DA Mendicino, T Ricardo, MA Cristaldi, M Maglianesi, G Guzmán S Claussen, RG Chiaraviglio, CA Ávalos, MA Previtali. (2024).</p>
Fig. 2 a in Circulating dengue virus serotypes and vertical transmission in AEdES larvae during outbreak and inter-outbreak seasons in a high dengue risk area of Sri Lanka
Fig. 2 a Distribution of dengue cases in the Kegalle District and Mawanella MOH area, Sri Lanka from December 2015 to March 2017. b Distribution of DENV serotypes in patients and distribution of Aedes mosquito larvae in and around the residences of dengue patients in Mawanella from December 2015 to March 2017. Abbreviations: DENV1, -2, -3, -4, DENV serotypes 1, 2, 3, 4
Genomic epidemiology unveils the dynamics and spatial corridor behind the Yellow Fever virus outbreak in Southern Brazil
<p>Despite the considerable morbidity and mortality of yellow fever virus (YFV) infections in Brazil our understanding of disease outbreaks is hampered by limited viral genomic data. Determining the timing and spatial corridors of YFV spread, as well as the geographic hotspots that link the endemic north of the country with epidemic extra-Amazonian regions, are central to predicting and preventing future outbreak and epidemics. Here, we tracked the recent spread of the virus by integrating genome sequences with both epidemiological and vector data. Through a combination of phylogenetic and epidemiological models we reconstructed the recent transmission history of YFV within different epidemic seasons in Brazil. A suitability index based on the highly domesticated <em>Aedes aegypti</em> was able to capture the seasonality of reported human infections. Spatial modelling revealed spatial hotspots with both past reporting and low vaccination coverage, which coincided with many of the largest urban centres in the Southeast. Phylodynamic analysis unravelled the circulation of three distinct YFV lineages, and provided proof of the directionality of a known spatial corridor of viral spread that connects the endemic North with the extra-Amazonian basin. This study illustrates that genomics linked with eco-epidemiology in a One Health framework can provide new insights into the landscape of YFV transmission, augmenting traditional approaches to infectious disease surveillance and control.</p>
Fig. 1 in Circulating dengue virus serotypes and vertical transmission in AEdES larvae during outbreak and inter-outbreak seasons in a high dengue risk area of Sri Lanka
Fig. 1 Map of Sri Lanka showing the location of Mawanella, the study area
Table 2 in Circulating dengue virus serotypes and vertical transmission in AEdES larvae during outbreak and inter-outbreak seasons in a high dengue risk area of Sri Lanka
<p><b>Table 2</b> Distribution of DENV serotypes in patients with suspected dengue and in <i>Aedes</i> mosquito larvae</p><table><tbody><tr><th>Patient no.</th><th><i>Ae. aegypti</i></th><th><i>Ae. albopictus</i></th><th>DENV serotype identified in mosquito pools</th><th>DENV serotype identified in patients</th></tr></tbody><tbody><tr><th>1</th><td>Detected</td><td>ND</td><td>DENV-3</td><td>DENV-3</td></tr><tr><th>2</th><td>ND</td><td>Detected</td><td>DENV-1</td><td>DENV-1</td></tr><tr><th>3</th><td>ND</td><td>Detected</td><td>DENV-3</td><td>DENV-3</td></tr><tr><th>4</th><td>ND</td><td>Detected</td><td>DENV-4</td><td>ND</td></tr><tr><th>5</th><td>ND</td><td>Detected</td><td>DENV-3</td><td>ND</td></tr><tr><th>6</th><td>ND</td><td>Detected</td><td>DENV-1</td><td>ND</td></tr><tr><th>7</th><td>Detected</td><td>ND</td><td>DENV-2</td><td>ND</td></tr><tr><th>8</th><td>Detected</td><td>ND</td><td>DENV-1</td><td>ND</td></tr><tr><th>9</th><td>Detected</td><td>ND</td><td>DENV-1</td><td>ND</td></tr><tr><th>10</th><td>ND</td><td>Detected</td><td>DENV-3</td><td>ND</td></tr><tr><th>11</th><td>ND</td><td>Detected</td><td>DENV-2</td><td>ND</td></tr><tr><th>12</th><td>ND</td><td>Detected</td><td>DENV-2</td><td>DENV-1</td></tr><tr><th>13</th><td>ND</td><td>Detected</td><td>DENV-2</td><td>ND</td></tr><tr><th>14</th><td>ND</td><td>Detected</td><td>DENV-4</td><td>ND</td></tr><tr><th>15</th><td>ND</td><td>Detected</td><td>DENV-1</td><td>ND</td></tr><tr><th>16</th><td>ND</td><td>Detected</td><td>DENV-2</td><td>DENV-2</td></tr></tbody></table><p><i>ND</i> Not detected</p>
Table 1 in Circulating dengue virus serotypes and vertical transmission in AEdES larvae during outbreak and inter-outbreak seasons in a high dengue risk area of Sri Lanka
<p><b>Table 1</b> Distribution of <i>Aedes</i> mosquito larvae in and around residences of patients with suspected dengue in Mawanella from December 2015 to March 2017</p><table><tbody><tr><th>Period</th><th>Month and year of sample collection</th><th>Total no. of vector pools collected in entomological survey</th><th>No. of <i>Aedes</i> mosquito pools identified</th></tr><tr><th><i>Ae. aegypti</i></th><th><i>Ae. albopictus</i></th></tr></tbody><tbody><tr><th>Epidemic</th><td>12/2015</td><td>18</td><td>3</td><td>15</td></tr><tr><th></th><td>1/2016</td><td>22</td><td>8</td><td>14</td></tr><tr><th>Inter-epidemic</th><td>2/2016</td><td>5</td><td>0</td><td>5</td></tr><tr><th></th><td>3/2016</td><td>3</td><td>1</td><td>2</td></tr><tr><th></th><td>4/2016</td><td>4</td><td>1</td><td>3</td></tr><tr><th></th><td>5/2016</td><td>12</td><td>1</td><td>11</td></tr><tr><th></th><td>6/2016</td><td>15</td><td>9</td><td>6</td></tr><tr><th>Epidemic</th><td>7/2016</td><td>7</td><td>1</td><td>6</td></tr><tr><th></th><td>8/2016</td><td>5</td><td>2</td><td>3</td></tr><tr><th></th><td>9/2016</td><td>5</td><td>2</td><td>3</td></tr><tr><th>Inter-epidemic</th><td>10/2016</td><td>6</td><td>1</td><td>5</td></tr><tr><th></th><td>11/2016</td><td>6</td><td>1</td><td>5</td></tr><tr><th>Epidemic</th><td>12/2016</td><td>14</td><td>3</td><td>11</td></tr><tr><th></th><td>1/2017</td><td>23</td><td>8</td><td>15</td></tr><tr><th>Inter-epidemic</th><td>2/2017</td><td>1</td><td>0</td><td>1</td></tr><tr><th></th><td>3/2017</td><td>25</td><td>8</td><td>17</td></tr><tr><th>Total</th><td></td><td>171</td><td>49</td><td>122</td></tr></tbody></table>
Mechanical transmission of Dengue Virus by Aedes aegypti may influence disease transmission dynamics during outbreaks data
<strong><span>Summary</span></strong> <p class="MsoNormal"><em><span>Background</span></em><span>: Dengue virus outbreaks are increasing in number and severity worldwide. Viral transmission is assumed to require a minimum time period of viral replication within the mosquito midgut. It is unknown if alternative transmission periods not requiring replication are possible.</span></p> <p class="MsoNormal"><em><span>Methods</span></em><span>: We used a mouse model of dengue virus transmission to investigate the potential of mechanical transmission of dengue virus. We investigated minimal viral titres necessary for development of symptoms in bitten mice and used resulting parameters to inform a new model of dengue virus transmission within a susceptible population.</span></p> <p class="MsoNormal"><em><span>Findings</span></em><span>: Naïve mice bitten by mosquitoes immediately after they took partial blood meals from dengue infected mice showed symptoms of dengue virus, followed by mortality. Incorporation of mechanical transmission into mathematical models of dengue virus transmission suggests that this supplemental transmission route could result in larger outbreaks which peak sooner.</span></p> <p class="MsoNormal"><em><span>Interpretation</span></em><span>: The potential of dengue transmission routes independent of midgut viral replication has implications for vector control strategies that target mosquito lifespan and suggest the possibility of similar mechanical transmission routes in other disease-carrying mosquitoes.</span></p>
Limited outbreak of highly pathogenic influenza A(H5N1) virus in a Herring Gull colony, Canada, 2022
<p class="MsoNormal">In summer 2022, highly pathogenic influenza A(H5N1) virus reached the Herring Gull (<em>Larus argentatus</em>) breeding colony on Kent Island, New Brunswick, Canada. Real-time monitoring revealed a self-limiting outbreak with low mortality. Proactive seabird surveillance is crucial for monitoring such limited outbreaks, protecting vulnerable seabirds, and tracing potential zoonotic transmission routes.</p>
Mechanical transmission of Dengue Virus by Aedes aegypti may influence disease transmission dynamics during outbreaks data
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Limited outbreak of highly pathogenic influenza A(H5N1) virus in a Herring Gull colony, Canada, 2022
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History of coronavirus naming during the three zoonotic outbreaks in relation to virus taxonomy and diseases caused by these viruses. According to the current international classification of diseases49, MERS and SARS are classified as 1D64 and 1D65, respectively. in The species Severe acute respiratory syndromerelated coronavirus: classifying 2019-nCoV and naming it SARS-CoV-2
History of coronavirus naming during the three zoonotic outbreaks in relation to virus taxonomy and diseases caused by these viruses. According to the current international classification of diseases49, MERS and SARS are classified as 1D64 and 1D65, respectively.
Data from: Occurrence and phylogenetic analysis of bovine respiratory syncytial virus in outbreaks of respiratory disease in Norway
Background: Bovine respiratory syncytial virus (BRSV) is one of the major pathogens involved in the bovine respiratory disease (BRD) complex. The seroprevalence to BRSV in Norwegian cattle herds is high, but its role in epidemics of respiratory disease is unclear. The aims of the study were to investigate the etiological role of BRSV and other respiratory viruses in epidemics of BRD and to perform phylogenetic analysis of Norwegian BRSV strains. Results: BRSV infection was detected either serologically and/or virologically in 18 (86%) of 21 outbreaks and in most cases as a single viral agent. When serology indicated that bovine coronavirus and/or bovine parainfluenza virus 3 were present, the number of BRSV positive animals in the herd was always higher, supporting the view of BRSV as the main pathogen. Sequencing of the G gene of BRSV positive samples showed that the current circulating Norwegian BRSVs belong to genetic subgroup II, along with other North European isolates. One isolate from an outbreak in Norway in 1976 was also investigated. This strain formed a separate branch in subgroup II, clearly different from the current Scandinavian sequences. The currently circulating BRSV could be divided into two different strains that were present in the same geographical area at the same time. The sequence variations between the two strains were in an antigenic important part of the G protein. Conclusion: The results demonstrated that BRSV is the most important etiological agent of epidemics of BRD in Norway and that it often acts as the only viral agent. The phylogenetic analysis of the Norwegian strains of BRSV and several previously published isolates supported the theory of geographical and temporal clustering of BRSV.
Data from: Occurrence and phylogenetic analysis of bovine respiratory syncytial virus in outbreaks of respiratory disease in Norway
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Data from: Greater sage-grouse survival varies with breeding season events in West Nile virus non-outbreak years
<p>Greater Sage-Grouse (Centrocercus urophasianus) are a species of conservation concern and are highly susceptible to mortality from West Nile virus (WNV). Culex tarsalis, a mosquito species, is the suspected primary vector for transmitting WNV to sage-grouse. We captured, radio-tagged, and monitored female sage-grouse to estimate breeding season (April 15‒September 15) survival 2016-2017. Deceased sage-grouse were tested for active WNV; live captured and hunter harvested sage-grouse were tested for WNV antibody titers. Additionally, we trapped mosquitoes with CO2 baited traps 4 nights per week (542 trap nights) to estimate WNV minimum infection rate (MIR). Eight sage-grouse mortalities occurred during the WNV seasons of 2016 and 2017; 5 had recoverable tissue, and one of 5 tested positive for WNV infection. Survival varied temporally with sage-grouse biological seasons, not WNV seasonality. Survival was 0.68 (95% CI= 0.56–0.78; n=74) during the reproductive season (April 1−September 15). Mammalian predators were the leading suspected cause of mortality (40%), followed by unknown cause (25%), avian predation (15%), unknown predation (15%), and WNV (5%). These results indicate WNV was not a significant driver of adult sage-grouse survival during this study. Three sage-grouse (1.9%; 95% CI=0.5−5.9%) contained WNV antibodies. We captured 12,472 mosquitoes of which 3,933 (32%) were Culex tarsalis. Estimated WNV MIR of Culex tarsalis during 2016 and 2017 was 3.3 and 1.6, respectively. Our results suggest sage-grouse in South Dakota have limited exposure to WNV, and WNV was not a significant source of sage-grouse mortality in South Dakota during 2016 and 2017. Based on our finding that a majority of sage-grouse in South Dakota are susceptible to WNV infection, WNV could potentially have an impact on the population during an epizootic event; however, when WNV is at or near endemic levels, it appears to have little impact on sage-grouse<br> survival. </p>
Data from: Greater sage-grouse survival varies with breeding season events in West Nile virus non-outbreak years
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Virological characterization of the 2022 outbreak-causing monkeypox virus using human keratinocytes and colon organoids
GEO Series GSE219036. Homo sapiens. 24 samples. Type: Expression profiling by high throughput sequencing.
Ebola virus glycoprotein variant with increased infectivity for human cells dominated the 2013-2016 outbreak
GEO Series GSE84865. Homo sapiens. 26 samples. Type: Expression profiling by high throughput sequencing.
Social Media Effect on Knowledge Dissemination During COVID-19 Virus Outbreak
ClinicalTrials.gov study NCT04319315. IPD Sharing: NO. Countries: 1. Publications: 0.
Cross Sectional Survey on the Burden, Impacts and Causes of Hepatitis C Virus (HCV) Outbreak in South West Region in Burkina Faso
ClinicalTrials.gov study NCT04136405. IPD Sharing: Not stated. Countries: 1. Publications: 0.
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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)
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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.