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230 results for “Infectious Disease”
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>
The relationship between vector species richness and the risk of vector-borne infectious diseases
<p>Infectious diseases can impact human welfare and impede wildlife management. Much recent research explores whether biodiversity increases or decreases infectious disease risk. Here we theoretically study the relationship between vector species richness and the risk of vector-borne diseases by an epidemiological model of a single host and multiple vectors. The model considers that vectors are involved in interspecific feeding interference that causes transmission interference and in interspecific recruitment competition that mediates susceptible vector regulation. The model reveals three possible shapes of the vector richness-disease risk relationship: monotonic amplification, hump-shaped, and monotonic dilution patterns. Monotonic amplification pattern occurs across a wide parameter region. Hump-shaped or monotonic dilution patterns are found when transmission interference is strong and recruitment competition is weak. Unexpectedly, susceptible vector regulation does not only promote dilution but can strengthen amplification if coupled with strong transmission interference. Our results suggest that vector richness might be more likely to cause amplification rather than dilution, and shifts in the community mean trait values of vectors could also affect disease risk along the vector richness gradient.</p>
Data from: Comparing the efficacy of control strategies for infectious disease outbreaks using field and simulation studies
<p>Diseases characterized by long distance inoculum dispersal (LDD) are among the fastest spreading epidemics in both natural and managed landscapes. Management of such epidemics is extremely challenging because of asymptomatic infection extending at large spatial scales and frequent escape from the newly established disease sources. We compared the efficacy of area- and timing-based disease management strategies in artificially initiated field epidemics of wheat stripe rust and complemented with simulations from an updated version of the spatially explicit model EPIMUL, using model parameters relevant to field epidemics. The model was further used to expand the number of epidemic mitigations beyond that feasible to incorporate in the field. The field experiment was conducted for two years in two locations having different climatic conditions. Culling and protection treatments were applied at different times after epidemic initiation and to different spatial extents surrounding the outbreaks. In each experiment, treatments were replicated four times in plots 33.5 m long and 1.52 m wide with a 0.76 x 0.76 m inoculated focus centered within each plot. Disease gradients were assessed along the center lines of the plots at 1.52 m intervals both upwind and downwind from the focus. Both field and simulation results indicated that control measures applied over the entire population were highly effective in suppressing the epidemics by more than 99% but may not always be logistically and economically feasible at large spatial scales. Comparison between the variable sized treatment areas and application timings suggested that implementing contiguous premises (CP) cull at 1 day after first sporulation in the outbreak focus reduced rust by 52 and 60% in Corvallis and Madras, respectively. However, altering the cull size did not significantly affect the disease epidemic development, which suggested that early timing had a greater influence in suppressing the epidemics than did increased area of application. However, sufficiently large, treated areas may compensate for a delay in application timing to some extent. Results from these replicated treatments may help to devise appropriate management strategies for other LDD pathogens. </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.
Data from: The basic-reproduction number of infectious diseases in spatially structured host populations
<p>The spatial structure of a host population has a profound effect on the dynamics of infectious diseases. The basic reproduction number, a central quantity in the study of epidemic dynamics, is affected by host clustering as well as host density. Several authors have developed methods to quantify the basic reproduction number in a spatially structured host population. The methods used and the expressions derived are however difficult to apply to real life spatial host structures. In this paper we introduce an explicit expression for the basic reproduction number using the O-ring statistic, developed in spatial statistics, that quantifies the host density as a function of the distance from a randomly selected host individual. The O-ring statistic is frequently used in the study of the ecology of spatially structured plant populations, being a convenient summary of the properties of a landscape by way of a single function. The connection we develop between spatial statistics and epidemic dynamics can be used to study the effect of host spatial pattern on the basic reproduction number of infectious diseases. As well as showing how explicit expressions for the basic reproduction number can be derived for landscapes with standard structures, our expression for the basic reproduction number is tested against a simulation model. The model structure in our simulation is motivated by the spread of a plant disease epidemic, although it is applicable more broadly. The agreement between our analytic expression for the basic reproduction number and the corresponding numeric quantity extracted from simulations is close to perfect across a wide range of landscape structures and model parameterisations, and including cases in which more than one species of host is at risk of infection.</p>
Supporting data for "Taking connected mobile-health diagnostics of infectious diseases to the field"
<p>Raw and intermediate data used to create figures 1 and 4 of Wood, C.,<em> et al., "</em>Taking connected mobile-health diagnostics of infectious diseases to the field", <strong>Nature</strong> (2019).</p>
Replication files for: Integrative modeling of the spread of serious infectious diseases and corresponding wastewater dynamics
<p>This repository contains the inputs used and outputs produced by the urban water management modelling software ++SYSTEMS for the paper Integrative Modeling of the Spread of Serious Infectious Diseases and Corresponding Wastewater Dynamics. It includes the following files:</p> <ol> <li>simulation_output.zip:</li> <ol> <li>In the subfolder infection_model, .csv and .txt files containing the agent-based model outputs for 250 simulations with homogeneous infection initialisation (2024_09_17) or localised infection initialisation (2024_10_15). These files were used as inputs for ++SYSTEMS.</li> <li>In the subfolder wastewater_model, .txt files containing the flow rates by pipe and the viral concentrations by sampling location for each combination of ABM simulation and rain/decay scenario. These files were the outputs of ++SYSTEMS.</li> </ol> <li>S1_INSIDe_Demonstrator_AreaList.txt: .txt file defining the area types and number of inhabitants for each surface area within the synthetic neighbourhood used in the paper. This file was also used as an input for ++SYSTEMS.<span> </span></li> <li>S2_systems_model_files.zip: .csv files defining the characteristics of the sewage system for the synthetic neighbourhood as well as the rain scenarios used in the paper. These file were also used as inputs for ++SYSTEMS.<span> </span></li> </ol>
Spatiotemporal analyses reveal infectious disease-driven selection in a free-ranging ungulate
<p>Infectious diseases play an important role in wildlife population dynamics by altering individual fitness, but detecting disease-driven natural selection in free-ranging populations is difficult due to complex disease-host relationships. Chronic wasting disease (CWD) is a fatal infectious prion disease in cervids for which mutations in a single gene have been mechanistically linked to disease outcomes, providing a rare opportunity to study disease-driven selection in wildlife. In Wyoming, USA, CWD has gradually spread across mule deer (<i>Odocoileus hemionus</i>) populations, producing natural variation in disease history to evaluate selection pressure. We used spatial variation and a novel temporal comparison to investigate the relationship between CWD and a mutation at codon 225 of the mule deer prion protein gene that slows disease progression. We found that individuals with the "slow" 225F allele were less likely to test positive for CWD, and the 225F allele was more common in herds exposed to CWD longer. We also found that in the past two decades, the 225F allele frequency increased more in herds with higher CWD prevalence. This study expanded on previous research by analyzing spatiotemporal patterns of individual- and herd-based disease data to present multiple lines of evidence for disease-driven selection in free-ranging wildlife.</p>
wild_expression: gene expression data on wildlife hosts of emerging infectious diseases
<p>Publicly accessible data and code supporting the manuscript:</p> <p>"Host gene expression in wildlife disease: making sense of species-level responses"</p>
Modeling the Sequence Dependence of Differential Antibody Binding in the Immune Response to Infectious Disease
<p>Raw peptide microarray data of fluorescence intensities representing relative binding of antibodies in sera samples collected from different cohorts of patients diagnosed with a number of viral infections. Healthy controls are also included.</p> <p>Columns:</p> <p>Sequence - peptide sequence</p> <p>HCV - Hepatitis Virus C</p> <p>Dengue - Dengue virus</p> <p>WNV - West Nile Virus</p> <p>HBV - Hepatitis Virus B</p> <p>Chagas - Chagas disease</p> <p>ND - negative/healthy donor</p> <p>LowCV - low coefficient of variation</p> <p>HighCV - high coefficient of variation</p>
Describing Chinese Herbal Medicine Telehealth Care for Symptoms Related to Infectious Diseases Such as COVID-19
ClinicalTrials.gov study NCT04380870. IPD Sharing: YES. Countries: 1. Publications: 30.
Antithrombin III in Infectious Disease Caused by COVID-19
ClinicalTrials.gov study NCT04899232. IPD Sharing: NO. Countries: 1. Publications: 1.
Precision Diagnosis of Acute Infectious Diseases; Neuroinflammatory Cohort
ClinicalTrials.gov study NCT02910037. IPD Sharing: YES. Countries: 1. Publications: 9.
A Study to Learn How Well Nifurtimox Works and How Safe it is in Children Aged 0 to 17 Years With Chagas' Disease, an Inflammatory, Infectious Disease Caused by the Parasite Trypanosoma Cruzi
ClinicalTrials.gov study NCT02625974. IPD Sharing: Not stated. Countries: 3. Publications: 5.
Wildfire alters the disturbance impacts of an emerging infectious disease via changes to host occurrence and demographic structure
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Data from: Infectious disease dynamics inferred from genetic data via sequential Monte Carlo
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The relationship between vector species richness and the risk of vector-borne infectious diseases
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Data from: The role of infectious disease in the evolution of females: evidence from anther-smut disease on a gynodioecious alpine carnation
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Counterintuitive scaling between population abundance and local density: implications for modelling transmission of infectious diseases in bat populations
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The extent of gender and race/ethnicity imbalance in infectious disease dynamics research
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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.
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DANDI Archive for NWB datasets
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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.