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230 results for “infectious diseases”
Spatiotemporal analyses reveal infectious disease-driven selection in a free-ranging ungulate
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Data from: Theory of infectious disease spillover at an ecological boundary: Impacts of seasonality and cross-boundary movement
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Data from: The basic-reproduction number of infectious diseases in spatially structured host populations
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An open-access database of infectious disease transmission trees to explore superspreader epidemiology
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Ungrazed seminatural habitats around farms benefit bird conservation without enhancing infectious disease risks
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Impact of COVID-19 on the awareness and interest in infectious disease specialization among Japanese medical students
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Data from: Comparing the efficacy of control strategies for infectious disease outbreaks using field and simulation studies
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Data for: Infectious disease and sickness behaviour: tumour progression affects interaction patterns and social network structure in wild Tasmanian devils
<p>Infectious diseases, including transmissible cancers, can have a broad range of impacts on host behaviour, particularly in the latter stages of disease progression. However, the difficulty of early diagnoses makes the study of behavioural influences of disease in wild animals a challenging task. Tasmanian devils (<i>Sarcophilus harrisii</i>) are affected by a transmissible cancer, devil facial tumour disease (DFTD), in which tumours are externally visible as they progress. Using telemetry and mark-recapture data sets, we quantify the impacts of cancer progression on the behaviour of wild devils by assessing how interaction patterns within the social network of a population change with increasing tumour load. DFTD negatively influences devils' likelihood of interaction within their network, an effect which increases with increasing tumour load. Infected devils were more active within their network late in the mating season, a pattern with repercussions for DFTD transmission. Our study provides a rare opportunity to quantify and understand the behavioural feedbacks of disease in wildlife and how they may affect transmission and population dynamics in general.</p>
Data from: Infectious disease transmission and behavioral allometry in wild mammals
1. Animal social and movement behaviors can impact the transmission dynamics of infectious diseases, especially for pathogens transmitted through close contact between hosts or through contact with infectious stages in the environment. 2. Estimating pathogen transmission rates and R0 from natural systems can be challenging. Because host behavioral traits that underlie the transmission process vary predictably with body size, one of the best-studied traits among animals, body size might therefore also predict variation in parasite transmission dynamics. 3. Here, we examine how two host behaviors, social group living and the intensity of habitat use, scale allometrically using comparative data from wild primate, carnivore and ungulate species. We use these empirical relationships to parameterize classical compartment models for infectious micro- and macroparasitic diseases, and examine how the risk of pathogen invasion changes as a function of host behavior and body size. We then test model predictions using comparative data on parasite prevalence and richness from wild mammals. 4. We report a general pattern suggesting that smaller-bodied mammal species utilizing home ranges more intensively experience greater risk for invasion by environmentally-transmitted macroparasites. Conversely, larger-bodied hosts exhibiting a high degree of social group living could be more readily invaded by directly-transmitted microparasites. These trends were supported through comparison of micro- and macroparasite species richness across a large number of carnivore, primate and ungulate species, but empirical data on carnivore macroparasite prevalence showed mixed results. 5. Collectively, our study demonstrates that combining host behavioral traits with dynamical models of infectious disease scaled against host body size can generate testable predictions for variation in parasite risk across species; a similar approach might be useful in future work focused on predicting parasite distributions in local host communities.
Data from: Ecological and evolutionary effects of fragmentation on infectious disease
Ecological theory predicts that disease incidence increases with increasing density of host networks, yet evolutionary theory suggests that host resistance increases accordingly. To test the combined effects of ecological and evolutionary forces on host-pathogen systems, we analyzed the spatiotemporal dynamics of a plant (Plantago lanceolata)–fungal pathogen (Podosphaera plantaginis)relationship for 12 years in over 4000 host populations. Disease prevalence at the metapopulation level was low, with high annual pathogen extinction rates balanced by frequent (re-)colonizations. Highly connected host populations experienced less pathogen colonization and higher pathogen extinction rates than expected; a laboratory assay confirmed that this phenomenon was caused by higher levels of disease resistance in highly connected host populations.
Data from: Stochasticity and infectious disease dynamics: density and weather effects on a fungal insect pathogen
In deterministic models of epidemics, there is a host abundance threshold, above which the introduction of a few infected individuals leads to a severe epidemic. Studies of weather-driven animal pathogens often assume that abundance thresholds will be overwhelmed by weather-driven stochasticity, but tests of this assumption are lacking. We collected observational and experimental data for a fungal pathogen, {\it Entomophaga maimaiga}, that infects the gypsy moth, {\it Lymantria dispar}. We used an advanced statistical-computing algorithm to fit mechanistic models to our data, such that different models made different assumptions about the effects of host density and weather on {\it E. maimaiga} epizootics (epidemics in animals). We then used AIC analysis to choose the best model. In the best model, epizootics are driven by a combination of weather and host density, and the model does an excellent job of explaining the data, whereas models that allow only for weather effects, or only density-dependence effects, do a poor job of explaining the data. Density-dependent transmission in our best model produces a host-density threshold, but this threshold is strongly blurred by the stochastic effects of weather. Our work shows that host-abundance thresholds may be important even if weather strongly affects transmission, suggesting that epidemiological models that allow for weather have an important role to play in understanding animal pathogens. The success of our model means that it could be useful for managing the gypsy moth, an important pest of hardwood forests in North America.
Data from: Megafauna decline have reduced pathogen dispersal which may have increased emergent infectious diseases
<p>The Late Quaternary extinctions of megafauna (defined as animal species > 44.5 kg) reduced the dispersal of seeds and nutrients, and likely also microbes and parasites. Here we use body-mass based scaling and range maps for extinct and extant mammal species to show that these extinctions led to an almost seven-fold reduction in the movement of gut-transported microbes, such as Escherichia coli (3.3–0.5 km 2 d − 1 ). Similarly, the extinctions led to a seven-fold reduction in the mean home ranges of vector-borne pathogens (7.8–1.1km 2 ). To understand the impact of this, we created an individualbased model where an order of magnitude decrease in home range increased maximum aggregated microbial mutations 4-fold after 20 000 yr. We hypothesize that pathogen speciation and hence endemism increased with isolation, as global dispersal distances decreased through a mechanism similar to the theory of island biogeography. To investigate if such an effect could be found, we analysed where 145 zoonotic diseases have emerged in human populations and found quantitative estimates of reduced dispersal of ectoparasites and fecal pathogens significantly improved our ability to predict the locations of outbreaks (increasing variance explained by 8%). There are limitations to this analysis which we discuss in detail, but if further studies support these results, they broadly suggest that reduced pathogen dispersal following megafauna extinctions may have increased the emergence of zoonotic pathogens moving into human populations.</p>
Data from: Identifying future zoonotic disease threats: where are the gaps in our understanding of primate infectious diseases?
Background and objectives: Emerging infectious diseases often originate in wildlife, making it important to identify infectious agents in wild populations. It is widely acknowledged that wild animals are incompletely sampled for infectious agents, especially in developing countries, but it is unclear how much more sampling is needed, and where that effort should focus in terms of host species and geographic locations. Here we identify these gaps in primate parasites, many of which have already emerged as threats to human health. Methodology: We obtained primate host-parasite records and other variables from existing databases. We then investigated sampling effort within primates relative to their geographic range size, and within countries relative to their primate species richness. We used generalized linear models, controlling for phylogenetic or spatial autocorrelation, to model variation in sampling effort across primates and countries. Finally, we used species richness estimators to extrapolate parasite species richness. Results: We found uneven sampling effort within all primate groups and continents. Sampling effort among primates was influenced by their geographic range size and substrate use, with terrestrial species receiving more sampling. Our parasite species richness estimates suggested that, among the best-sampled primates and countries, almost half of primate parasites remain to be sampled; for most primate hosts, the situation is much worse. Conclusions and implications: Sampling effort for primate parasites is uneven and low. The sobering message is that we know little about even the best studied primates, and even less regarding the spatial and temporal distribution of parasitism within species.
Predicting wildlife susceptibility to infectious diseases atglobal scales
<p>Dataset included as supplementary material of the paper entitled https://doi.org/10.5281/zenodo.4914750. It contains phylogenetic, geographical and environmental distance for birds and bats, counts of incidence of <em>Plasmodium relictum</em> on birds, counts of incidence of West Nile Virus on birds and counts of incidence of coronavirus in bats, and susceptibility calculated by the random forest algorithm. Also we include the r scripts to run the models, and the outputs of the models after 1000 runs. </p>
The appendix for dynamic model of respiratory infectious disease transmission by population mobility based on city network
<p>First, a scale-free city network was established, and the shortest path between any two nodes was determined. Second, the movement path of tourists was designed based on the shortest path. Subsequently, every infected person's information, such as the city, infection time, onset, and hospitalisation, was confirmed based on their movement path. Third, the features of the transmission path and time distribution of the epidemic were characterised after summarising the information. Finally, the reliability of the model was verified.</p>
Imunnesenescence of antibody repertoire in individuals from endemic areas for infectious diseases
<p>Heavy Chain Antibody Repertoire data in the AIRR format, clonotyped with YClon, of patients diagnosed with COVID-19 and a control group. The sample labels in the paper and the sample labels in this repository are correspondent according to the following table:</p> <table> <tbody> <tr> <td>Sample</td> <td>Deposite_code</td> </tr> <tr> <td>C_01</td> <td>A04</td> </tr> <tr> <td>C_02</td> <td>A20</td> </tr> <tr> <td>C_03</td> <td>A24</td> </tr> <tr> <td>C_04</td> <td>A65</td> </tr> <tr> <td>C_05</td> <td>A66</td> </tr> <tr> <td>C_06</td> <td>A67</td> </tr> <tr> <td>H_NEA_01</td> <td>ID141</td> </tr> <tr> <td>H_NEA_02</td> <td>ID143</td> </tr> <tr> <td>H_NEA_03</td> <td>ID144</td> </tr> <tr> <td>H_NEA_04</td> <td>ID187</td> </tr> <tr> <td>H_NEA_05</td> <td>ID195</td> </tr> <tr> <td>H_NEA_06</td> <td>ID226</td> </tr> <tr> <td>H_NEA_07</td> <td>ID248</td> </tr> <tr> <td>H_NEA_08</td> <td>ID268</td> </tr> <tr> <td>H_NEA_09</td> <td>ID310</td> </tr> <tr> <td>H_NEA_10</td> <td>ID375</td> </tr> <tr> <td>M_EA_01</td> <td>GV43</td> </tr> <tr> <td>M_EA_02</td> <td>GV68</td> </tr> <tr> <td>M_EA_03</td> <td>GV106</td> </tr> <tr> <td>M_EA_04</td> <td>GV144</td> </tr> <tr> <td>M_EA_05</td> <td>GV146</td> </tr> <tr> <td>M_EA_06</td> <td>GV47</td> </tr> <tr> <td>M_EA_07</td> <td>GV50</td> </tr> <tr> <td>M_EA_08</td> <td>GV51</td> </tr> <tr> <td>M_EA_09</td> <td>GV54</td> </tr> <tr> <td>M_EA_10</td> <td>GV92</td> </tr> <tr> <td>M_NEA_01</td> <td>ID094</td> </tr> <tr> <td>M_NEA_02</td> <td>ID117</td> </tr> <tr> <td>M_NEA_03</td> <td>ID124</td> </tr> <tr> <td>M_NEA_04</td> <td>ID131</td> </tr> <tr> <td>M_NEA_05</td> <td>ID132</td> </tr> <tr> <td>M_NEA_06</td> <td>ID155</td> </tr> <tr> <td>M_NEA_07</td> <td>ID240</td> </tr> <tr> <td>M_NEA_08</td> <td>ID244</td> </tr> </tbody> </table>
Quantifying social contact dynamics in South Korea for infectious disease transmission
<p>Social contact data from a self-reportred survey conducted in South Korea. The survey was conducted from July to september 2023. </p>
Deep learning and knowledge graph powered drug combination discovery against infectious diseases
<p>The Datasets and source codes for paper "<strong>Deep learning and knowledge graph powered drug combination discovery against infectious diseases</strong>".</p>
Data for: Can pharmaceutical pollution alter the spread of infectious disease? A case study using fluoxetine.
<p>Human activity is changing global environments at an unprecedented rate, imposing new ecological and evolutionary ramifications on wildlife dynamics, including host-parasite interactions. Here we investigate how an emerging concern of modern human activity, pharmaceutical pollution, influences the spread of disease in a population, using the water flea <em>Daphnia</em> <em>magna</em> and the bacterial pathogen <em>Pasteuria</em> <em>ramosa</em> as a model system. We found that exposure to different concentrations of fluoxetine—a widely prescribed psychoactive drug and widespread contaminant of aquatic ecosystems—affected the severity of disease experienced by an individual in a non-monotonic manner. The direction and magnitude of any effect, however, varied with both the infection outcome measured, as well as the genotype of the pathogen. In contrast, the characteristics of unexposed animals, and thus the growth and density of susceptible hosts, were robust to fluoxetine. Using our data to parameterise an epidemiology model, we show that fluoxetine is unlikely to lead to a net increase or decrease in the likelihood of an infectious disease outbreak, as measured by a pathogen's transmission rate or basic reproductive number. Instead, any given pathogen genotype may experience a two-fold change in likely fitness, but often in opposing directions. Our study demonstrates that changes in pharmaceutical pollution give rise to complex genotype-by-environment interactions in its influence on disease dynamics, with repercussions on pathogen genetic diversity and evolution.</p>
RVF and Other Emerging Infectious Diseases in East and Central Africa
ClinicalTrials.gov study NCT05139524. IPD Sharing: NO. Countries: 4. Publications: 0.
ScienceDex guides
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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.