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30 results for “zoonotic disease”
A scoping review on bovine tuberculosis highlights the need for novel data streams and analytical approaches to curb zoonotic diseases
<p>The following data and scripts are part of the manuscript titled 'A scoping review on bovine tuberculosis highlights the need for novel data streams and analytical approaches to curb zoonotic diseases' which is currently going through the peer-review process and has already been published as a preprint. Please read the README.txt file for information on the files uploaded.</p>
Data from: Multisectoral approach in zoonotic disease surveillance
<p>Zoonoses are naturally transmissible between humans and animals. Globally, they account for more than 60% of human infections, 75% of emerging infections, 2.7 million human deaths, and 10% of the total DALYs lost yearly in Africa. In the last three decades, Kenya has had sporadic outbreaks of zoonoses. To increase the speed of reporting and efficiencies in detection and control, a multi-sectoral collaboration in zoonotic disease surveillance (MZDS) between human and animal health workers is essential. In an effort, Zoonotic disease unit (ZDU) in Kenya has been established at national and county levels. A cross sectional study was carried out to determine the level of utilization of multisectoral collaboration and its associated determinants in zoonotic disease surveillance among animal and human healthcare workers in Nakuru County. Quantitative data was gathered from 102 participants and quantitative data from 5 key informants. To test for significant differences, Chi-square and independent t-test were used. MZDS utilization level was 16% and the factors associated with higher utilization include; knowing what MZDS entails, education level, sector affiliation, trainings, supportive infrastructure and data storage. Lack of financing and poor coordination are hindrances to MZDS. In conclusion, there is need to finance MZDS activities, strengthen coordination mechanisms, carry out more sensitization and trainings among animal and human healthcare.</p>
Fig. 5 in Helminth parasites of howler and spider monkeys in Mexico: Insights into molecular diagnostic methods and their importance for zoonotic diseases and host conservation
Fig. 5. Phylogenetic trees based on 28S (left) and cox1 (right) sequences of Strongyloides eggs. Sequences obtained from the eggs are bold type and indicated with an *. Numbers at the nodes represent posterior probabilities from Bayesian inference.
Fig. 2 in Helminth parasites of howler and spider monkeys in Mexico: Insights into molecular diagnostic methods and their importance for zoonotic diseases and host conservation
Fig. 2. Egg morphotypes found in the faeces of Mexican primates. A) Trypanoxyuris sp., B) Controrchis biliophilus, arrow pointing to the two eyespot remnants; C) trematode, diagnosed as C. biliophilus by molecular data; D) unidentified ancylostomatid; E) Strongyloides sp.; F) unidentified ascarid. Scale bar is equal to 15 Mm.
Fig. 4 in Helminth parasites of howler and spider monkeys in Mexico: Insights into molecular diagnostic methods and their importance for zoonotic diseases and host conservation
Fig. 4. Phylogenetic tree based on 28S sequences of Controrchis biliophilus. Sequences obtained from the eggs are bold type and indicated with an *. Numbers at the nodes represent posterior probabilities from Bayesian inference. Host species are indicated within parenthesis.
Fig. 1 in Helminth parasites of howler and spider monkeys in Mexico: Insights into molecular diagnostic methods and their importance for zoonotic diseases and host conservation
Fig. 1. Surveyed sites for parasites in Mexican primates. Dots indicate sampling sites, black: Alouatta palliata; white: A. pigra; and grey: Atetes geoffroyi. Polygons indicate the primate distribution range in Mexico, diagonal lines: A. palliata; dashes: A. pigra; and grey: A. geoffroyi.
Fig. 3 in Helminth parasites of howler and spider monkeys in Mexico: Insights into molecular diagnostic methods and their importance for zoonotic diseases and host conservation
Fig. 3. Phylogenetic trees based on 28S (left) and cox1 (right) sequences of Trypanoxyuris sp. Sequences obtained from the eggs are bold type and indicated with an *. Numbers at the nodes represent posterior probabilities from Bayesian inference.
Fig. 1 in The raccoon dog (Nyctereutes procyonoides) as a reservoir of zoonotic diseases in Denmark
Fig. 1. Map of Denmark showing the origin of the collected raccoon dogs by county. The colour coding shows differences in the number of raccoon dogs collected. (For interpretation of the references to colour in this figure legend, the reader is referred to the Web version of this article.)
Fig. 1 in Reptile vector-borne diseases of zoonotic concern
Fig. 1. Arthropod vectors associated to reptiles represented by a Podarcis siculus lizard and Tarentola mauritanica gecko and zoonotic pathogens they may transmit. a) Ixodes ricinus tick larva, b) Ophionyssus natricis mite, c) Sergentomyia minuta sand fly, d) Aedes albopictus mosquito. Red lines represent high importance role of transmission, orange line represents medium importance role of transmission, gray line represents mechanical vector and green line represents transmission of nonpathogenic zoonotic microorganisms. Dashed lines represent neglectable knowledge on actual role of vector. (For interpretation of the references to colour in this figure legend, the reader is referred to the Web version of this article.)
Fig. 2 in Reptile vector-borne diseases of zoonotic concern
Fig. 2. Arthropod vectors that may feed on reptiles. a) Ixodes ricinus larva on Podarcis siculus lizard being collected with tweezers, b) Neotrombicula autumnalis larvae mites on Podarcis siculus lizard, c) female Sergentomyia minuta phlebotomine sand fly, d) Aedes albopictus mosquito.
Ecological dataset from: 'Rats and the city: implications of urbanization on zoonotic disease risk in Southeast Asia'
<p>This dataset includes the ecological and environmental data, a description of the analysis steps, and the related R code associated with the manuscript: "Rats and the city: implications of urbanization on zoonotic disease risk in Southeast Asia" by Kim R. Blasdell, Serge Morand, Susan G.W. Laurance, Stephen L Doggett, Amy Hahs, David Perera, and Cadhla Firth, available at: https://www.pnas.org/doi/abs/10.1073/pnas.2112341119</p> <p>This dataset also relates to the preprint: "Rats in the city: implications for zoonotic disease risk in an urbanizing world" available at: https://www.biorxiv.org/content/10.1101/2021.03.18.436089v1</p> <p>A detailed description of the files can be found in README.txt</p>
Financial dimensions of global zoonotic disease risks
<p>The CSV files contain cleaned lists of companies and shareholders used in the study "<strong>Financial dimensions of global zoonotic disease risks</strong>". Code for the analysis is available at <a href="https://github.com/juanrocha/finance_tipping">https://github.com/juanrocha/finance_tipping</a> and a preprint of the study at <a href="https://beijer.kva.se/publication/financial-dimensions-of-global-zoonotic-disease-risks/">https://beijer.kva.se/publication/financial-dimensions-of-global-zoonotic-disease-risks/</a></p>
Data from: Multisectoral approach in zoonotic disease surveillance
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Data from: Forecasting potential emergence of zoonotic diseases in Southeast Asia: network analysis identifies key rodent hosts
1. Within complex ecological systems, identifying animal species likely to play a key role in the emergence of infectious zoonotic diseases remains a major challenge. One approach consists of using information on current ecological and parasitological similarities among host species in order to predict the most likely pathways for future pathogen spillover. 2. Using field data acquired from 15 sympatric rodent species in various habitats in Thailand, Cambodia and Laos, we built networks based on shared parasites (17 helminth and 15 microparasite species) and shared habitats among rodent species and humans. We investigated the architectures of bipartite and unipartite networks using modularity, subgroups partitioning or node centrality, to assess the relative epidemiological importance of particular rodent species. 3. Our results showed that Rattus tanezumi, Bandicota savilei and R. exulans were consistently found to be members of subgroups that included humans in unipartite and bipartite networks on zoonotic agents and shared habitats. High values of centrality in shared zoonotic agents were found for the same three rodent species, whereas high values of shared habitats were observed for two of them. Although phylogenetically related rodent species likely shared both habitats and parasites, a lack of habitat specialisation was associated with increased zoonotic parasite sharing. 4. Our results emphasize the disproportionate importance of these three rodent species, through their high degree of connectivity with humans, which may represent a high risk for direct zoonotic spillover. Moreover, due to its high centrality in habitats, R. tanezumi may also play a key role as a bridge host. 5. The recent discovery of new arenaviruses in rodents in Southeast Asia, with associated disease in humans in Cambodia, provides an opportunity to test this empirically. The three rodent species identified using our network approach are some of the potential maintenance hosts for these new emerging arenaviruses. 6. Synthesis and applications. Our results on rodents and their pathogens in Southeast Asia show that network analysis has a high potential to improve the surveillance of emerging zoonotic pathogens by targeting key host species and potential "emerging' pathogen–rodent interactions in complex and heterogeneous landscapes.
Literature search on worldwide surveillance systems targeting transboundary zoonotic and emerging diseases
<p>Surveillance systems for zoonotic and transboundary emerging pathogens that are structured following the holistic principles of joint work efforts from the human health, animal health and environmental health sectors are reviewed to provide a summary of one-health based surveillance systems existing worldwide. A systematic search of available literature was undertaken across various biomedical and scientific literature databases (from 2000 to 2022) and were selected using inclusion/exclusion criteria to filter references presenting systematic surveillance systems applicable to transmissible, transboundary, and zoonotic diseases operating under the One Health approach. A standardized data model and vocabulary were used to extract and classify key information to characterize target surveillance systems. 996 studies were obtained after research (589 after duplicates’ elimination) for inclusion in this review and information was extracted using a data model, which were reduced to 79 items once inclusion and exclusion criteria were applied. From these articles, 80 additional items were found within references.</p>
Tracing the future of epidemics: Coincident niche distribution of host animals and disease incidence revealed climate-correlated risk shifts of main zoonotic diseases in China
<p>This dataset contains host occurrence data from NACRC database and disease incidence data for the paper "Tracing the future of epidemics: Coincident niche distribution of host animals and disease incidence revealed climate-correlated risk shifts of main zoonotic diseases in China".</p>
Data from: Forecasting potential emergence of zoonotic diseases in Southeast Asia: network analysis identifies key rodent hosts
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Data from: The release of non-native gamebirds is associated with amplified zoonotic disease risk
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The global network of domestic mammal hosts and zoonotic bacteria: Implications for disease transmission and detection
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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.
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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.