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12 results for “infectious disease transmission”
Social contact patterns relevant for infectious disease transmission in Cambodia
<p>Social contact data from a community-based survey conducted in Cambodia in 2012. </p>
Indirect pathogen transmission underlies an emerging infectious fungal disease outbreak in a wild reptile population
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Counterintuitive scaling between population abundance and local density: implications for modelling transmission of infectious diseases in bat populations
<p>1. Models of host-pathogen interactions help to explain infection dynamics in wildlife populations and to predict and mitigate the risk of zoonotic spillover. Insights from models inherently depend on the way contacts between hosts are modelled, and crucially, how transmission scales with animal density.</p> <p>2. Bats are important reservoirs of zoonotic disease and are among the most gregarious of all mammals. Their population structures can be highly heterogenous, underpinned by ecological processes across different scales, complicating assumptions regarding the nature of contacts and transmission. Although models commonly parameterise transmission using metrics of total abundance, whether this is an ecologically representative approximation of host-pathogen interactions is not routinely evaluated.</p> <p>3. We collected a 13-month dataset of tree-roosting <i>Pteropus </i>spp. from 2,522 spatially referenced trees across eight roosts to empirically evaluate the relationship between total roost abundance and tree-level measures of abundance and density – the scale most likely to be relevant for virus transmission. We also evaluate whether roost features at different scales (roost-level, subplot-level, tree-level) are predictive of these local density dynamics.</p> <p>4. Roost-level features were not representative of tree-level abundance (bats per tree) or tree-level density (bats per m<sup>2</sup> or m<sup>3</sup>), with roost-level models explaining minimal variation in tree-level measures. Total roost abundance itself was either not a significant predictor (tree-level 3-D density) or only weakly predictive (tree-level abundance).</p> <p>5. This indicates that basic measures, such as total abundance of bats in a roost, may not provide adequate approximations for population dynamics at scales relevant for transmission, and that alternative measures are needed to compare transmission potential between roosts. From the best candidate models, the strongest predictor of local population structure was tree density within roosts, where roosts with low tree density had a higher abundance but lower density of bats (more spacing between bats) per tree.</p> <p>6. Together, these data highlight unpredictable and counterintuitive relationships between total abundance and local density. More nuanced modelling of transmission, spread and spillover from bats likely requires alternative approaches to integrating contact structure in host-pathogen models, rather than simply modifying the transmission function.</p>
An open-access database of infectious disease transmission trees to explore superspreader epidemiology
Historically, emerging and reemerging infectious diseases have caused large, deadly, and expensive multinational outbreaks. Often outbreak investigations aim to identify who infected whom by reconstructing the outbreak transmission tree, which visualizes transmission between individuals as a network with nodes representing individuals and branches representing transmission from person to person. We compiled a database, called OutbreakTrees, of 382 published, standardized transmission trees consisting of 16 directly transmitted diseases ranging in size from 2 to 286 cases. For each tree and disease, we calculated several key statistics, such as tree size, average number of secondary infections, the dispersion parameter, and the proportion of cases considered superspreaders, and examined how these statistics varied over the course of each outbreak and under different assumptions about the completeness of outbreak investigations. We demonstrated the potential utility of the database through 2 short analyses addressing questions about superspreader epidemiology for a variety of diseases, including Coronavirus Disease 2019 (COVID-19). First, we found that our transmission trees were consistent with theory predicting that intermediate dispersion parameters give rise to the highest proportion of cases causing superspreading events. Additionally, we investigated patterns in how superspreaders are infected. Across trees with more than 1 superspreader, we found preliminary support for the theory that superspreaders generate other superspreaders. In sum, our findings put the role of superspreading in COVID-19 transmission in perspective with that of other diseases and suggest an approach to further research regarding the generation of superspreaders. These data have been made openly available to encourage reuse and further scientific inquiry.
Counterintuitive scaling between population abundance and local density: implications for modelling transmission of infectious diseases in bat populations
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An open-access database of infectious disease transmission trees to explore superspreader epidemiology
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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.
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>
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>
Data from: Intrinsic and realized generation intervals in infectious-disease transmission
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Data from: Infectious disease transmission and behavioral allometry in wild mammals
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DM-PhyClus: A Bayesian phylogenetic algorithm for infectious disease transmission cluster inference
<p>The files include the simulated datasets and the chain results for the simulation study described in, </p> <p>DM-PhyClus: A Bayesian phylogenetic algorithm for infectious disease transmission cluster inference</p> <p>Those are all compressed R data files (gzip format).</p>
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.