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21 results for “Modeling: epidemiological”
Adjoint-based Data Assimilation of an Epidemiology Model for the Covid-19 Pandemic in 2020 --- Data Files
<p>New data on github:</p> <p>https://github.com/sesterhenn/Corona-DataAssimilation</p> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p> <p>doi://10.5281/zenodo.3732292</p> <p>https://zenodo.org/record/3733244</p>
Data for: Feedback between coevolution and epidemiology can help or hinder the maintenance of genetic variation in host-parasite models
<p>Antagonistic coevolution has long been suggested to help maintain host genetic variation. While, ecological and epidemiological feedbacks are known to have important consequences on coevolutionary allele frequency dynamics, their effects on the maintenance of genetic variation remains poorly understood.Here, we extend our previous work on the maintenance of genetic variation in a classic matching-alleles coevolutionary model by exploring the effects of ecological and epidemiological feedbacks, where both allele frequencies and population sizes are allowed to vary over time. We find that coevolution rarely maintains more host genetic variation than expected under neutral genetic drift alone. When and if coevolution maintains or depletes genetic variation relative to neutral drift is determined, predominantly, by two factors: the deterministic stability of the Red Queen allele frequency cycles and the chance of allele fixation in the pathogen, as this results in directional selection and depletion of genetic variation in the host. Compared to purely coevolutionary models with constant host and pathogen population sizes, ecological and epidemiological feedbacks stabilize Red Queen cycles deterministically, but population fluctuations in the pathogen increase the rate of allele fixation in the pathogen, especially in epidemiological models. Our results illustrate the importance of considering the ecological and epidemiological context in which coevolution occurs when examining the impact of Red Queen cycles on genetic variation.</p>
LNT Model is not an "Assumption": Re-Analysis of Epidemiological Data Empirically Supports LNT
<p><em>Introduction:</em> In “Keeping ICRP Recommendations Fit for Purpose”[1], LNT model is described as “LNT is the most appropriate evidence-based assumption to use for radiological protection purposes (p.10).” According to our critical literature survey on radiological epidemiology[2], some limitations were identified: (1) aggregation of individual level data, (2) model formulation, (3) model estimation, (4) model selection, (5) results interpretation. In this paper we focus (4) model selection and demonstrate LNT was the best model.</p> <p><em>Data and Method:</em> Using “Life Span Study Report 14. Cancer and non-cancer disease mortality data, 1950-2003 [3]”, solid cancer mortality was re-analyzed. In addition to the L, Q, LQ, hadn searched threshold model, kinked–at-2Gy model that assumes LQ for less than 2Gy and L for larger than 2 Gy, and Linear model with threshold as a parameter were estimated. Following [3], Poisson regression model was applied and model fit was compared with AIC and BIC.</p> <p><em>Results</em>: Among estimated models, Linear (BIC=18317.9) and grid search threshold at 20mSv (BIC=18318.1) was selected as the best models. Directly estimated threshold was -23.2 mSv and it was statistically insignificant (z=-0.087,p>0.1). Model fit of kinked-at-2Gy was poorer than these models (BIC=18321.2).</p> <p><em>Conclusion:</em> Based on these results, we can conclude LNT model is the best model for a-bomb survivor solid cancer mortality. According to our literature survey, LNT is supported Description of LNT model in “Keeping ICRP Recommendations Fit for Purpose” should be modified accordingly: “LNT is the scientifically supported model, it is reasonable LNT to use for radiological protection purposes.”</p>
Model sets and data used in the preprint "Evaluating functional dispersal and its eco-epidemiological implications in a nest ectoparasite"
<p>Model sets and data used in the preprint "Evaluating functional dispersal and its eco-epidemiological implications in a nest ectoparasite", reviewed and recommended by Peer Community In Ecology (https://dx.doi.org/10.24072/pci.ecology.100013). See preprint and supplementary materials.</p>
Source data for "Date of introduction and epidemiologic patterns of SARS-CoV-2 in Mogadishu, Somalia: estimates from transmission modelling of satellite-based excess mortality data in 2020"
<p>Source data for the model fitting code at https://doi.org/10.5281/zenodo.5525349, accompanying the article "<em>Date of introduction and epidemiologic patterns of SARS-CoV-2 in Mogadishu, Somalia: estimates from transmission modelling of satellite-based excess mortality data in 2020</em>"</p>
Quantifying the potential epidemiological impact of a two-year active case finding for tuberculosis in rural Nepal: A model-based analysis
<p>Includes data and codes for the manuscript entitled: <strong>Quantifying the potential epidemiological impact of a two-year active case finding for tuberculosis in rural Nepal: A model-based analysis</strong></p> <div> <div> <div> <p>doi:10.1136/ bmjopen-2022-062123</p> </div> </div> </div>
Raw data for model implementation examples in paper "Incorporating Detected/Undetected Cooperative and Uncooperative Individuals, and Dynamic Transmission Probabilities in Epidemiological Models"
<p>This dataset contains the historical daily new case numbers referenced in the paper titled "Incorporating Detected/Undetected Cooperative and Uncooperative Individuals, and Dynamic Transmission Probabilities in Epidemiological Models." These numbers were utilized to estimate the basic reproduction number <em>R<sub>0</sub></em> and the aggregated epidemic control measure <em>K<sub>r</sub>(t)</em> parameters within the model, as shown in the example implementation section of the paper.</p>
Modeling the Epidemiologic Transition Study
ClinicalTrials.gov study NCT02925156. IPD Sharing: NO. Countries: 1. Publications: 12.
Data for: Feedback between coevolution and epidemiology can help or hinder the maintenance of genetic variation in host-parasite models
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The impact of the program for medical male circumcision on HIV in South Africa: analysis using three epidemiological models
<p class="Body"><b>Background</b>: South Africa began offering medical male circumcision (MMC) in 2010. We evaluated the current and future impact of this program to see if it is effective in preventing new HIV infections.</p> <p class="Body"><b>Methods</b>: The Thembisa, Goals and EMOD HIV transmission models were calibrated to South Africa's HIV epidemic, fitting to household survey data on HIV prevalence, risk behaviors, and proportions of men circumcised, and to programmatic data on intervention roll-out including program-reported MMCs over 2009-2017. We compared the actual program accomplishments through 2017 and program targets through 2021 with a counterfactual scenario of no MMC program.</p> <p class="Body"><strong>Results:</strong> The MMC program averted 71,000-83,000 new HIV infections from 2010 to 2017. The future benefit of the circumcision already conducted will grow to 496,000-518,000 infections (6-7% of all new infections) by 2030. If program targets are met by 2021 the benefits will increase to 723,000-760,000 infections averted by 2030. The cost would be $1,070-1,220 per infection averted relative to no MMC. The savings from averted treatment needs would become larger than the costs of the MMC program around 2034-2039. In the Thembisa model, when modelling South Africa's 9 provinces individually, the 9-provinces-aggregate results were similar to those of the single national model. Across provinces, projected long-term impacts were largest in Free State, KwaZulu-Natal and Mpumalanga (23-27% reduction over 2017-2030), reflecting these provinces' greater MMC scale-up.</p> <p class="Body"><b>Conclusions</b>: MMC has already had a modest impact on HIV incidence in South Africa and can substantially impact South Africa's HIV epidemic in the coming years.</p>
Developing epidemiological preparedness for a probable plant disease invasion: modelling citrus huánglóngbìng in the European Union
<p>Video 1: Spread of the vector in a single simulation in Region A (Valencia). Corresponds to Fig S10 in supplementary material. Maps show the measure of vector density within each cell and light grey shows initial exposure.</p> <p>Video 2: Spread of the pathogen in a single simulation in Region A (Valencia). Corresponds to Fig 3 in main text. Both vector and pathogen are introduced simultaneously at t=0 into a single 1km x 1km cell. Maps showing the density of infected citrus host units (E+C+I) within each cell at different times after introduction.</p> <p>Video 3: Spread of the pathogen in a single simulation in Region A (Valencia) using baseline parameters for detection and control. Corresponds to Fig 6 in main text. Maps show densities of infected citrus (E+C+I) in each 1km x 1km cell</p> <p>Video 4: Spread of the vector in a single simulation in Region B (Andalusia). Corresponds to Fig S13 in supplementary material. Maps show the measure of vector density within each cell and light grey shows initial exposure.</p> <p>Video 5: Spread of the pathogen in a single simulation in Region B (Andalusia). Corresponds to Fig S14 in supplementary material. Both vector and pathogen are introduced simultaneously at t=0 into a single 1km x 1km cell. Maps showing the density of infected citrus host units (E+C+I) within each cell at different times after introduction.</p> <p>Video 6: Spread of the pathogen in a single simulation in Region B (Andalusia) using baseline parameters for detection and control. Corresponds to Fig S17 in supplementary material. Maps show densities of infected citrus (E+C+I) in each 1km x 1km cell</p>
Epidemiological Analysis of Shoulder Injuries Among Greek CrossFit Participants and Predictive Modeling for Shoulder Injury Incidence.
ClinicalTrials.gov study NCT05909592. IPD Sharing: Not stated. Countries: 1. Publications: 7.
The impact of the program for medical male circumcision on HIV in South Africa: analysis using three epidemiological models
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Data from: Epidemiological implications of host biodiversity and vector biology: key insights from simple models
Models used to investigate the relationship between biodiversity change and vector-borne disease risk often do not explicitly include the vector; they instead rely on a frequency-dependent transmission function to represent vector dynamics. However, differences between classes of vector (e.g., ticks and insects) can cause discrepancies in epidemiological responses to environmental change. Using a pair of disease models (mosquito- and tick-borne), we simulated substitutive and additive biodiversity change (where noncompetent hosts replaced or were added to competent hosts, respectively), while considering different relationships between vector and host densities. We found important differences between classes of vector, including an increased likelihood of amplified disease risk under additive biodiversity change in mosquito models, driven by higher vector biting rates. We also draw attention to more general phenomena, such as a negative relationship between initial infection prevalence in vectors and likelihood of dilution, and the potential for a rise in density of infected vectors to occur simultaneously with a decline in proportion of infected hosts. This has important implications; the density of infected vectors is the most valid metric for primarily zoonotic infections, while the proportion of infected hosts is more relevant for infections where humans are a primary host.
Data from: Epidemiological models to control the spread of information in marine mammals
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Data for: Calibration of individual-based models to epidemiological data: a systematic review
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Data from: Epidemiological implications of host biodiversity and vector biology: key insights from simple models
Open the record for dataset details and reuse information.
Epidemiological Characteristics of Elderly Trauma Patients in Zhejiang Province and Development of Geriatric Trauma Short-term Mortality Prediction Model
ClinicalTrials.gov study NCT04954768. IPD Sharing: UNDECIDED. Countries: 1. Publications: 0.
Advanced Modeling of the Evolution of the Epidemiological Outbreak of SARS-CoV-2 Pandemic
ClinicalTrials.gov study NCT06070896. IPD Sharing: NO. Countries: 1. Publications: 0.
Epidemiology of Chronic Hepatitis C and Disease Modelling
ClinicalTrials.gov study NCT03566563. IPD Sharing: NO. Countries: 1. Publications: 0.
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Allen Brain Atlas
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International Brain Laboratory public data
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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.