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48 results for “reinfection”
Data for SARS-CoV-2 Reinfection Trends in South Africa: Monthly Report (2022-12-07)
<p>This version contains a single file, with time series data for the most recent <a href="https://www.nicd.ac.za/diseases-a-z-index/disease-index-covid-19/surveillance-reports/sarscov2-reinfection-trends-in-south-africa-monthly-report/">monthly report on SARS­-CoV-­2 Reinfection Trends in South Africa</a>:</p> <ul> <li><code>ts_data.csv</code> - national daily time series of newly detected putative primary infections (<code>cnt</code>), suspected second infections (<code>reinf</code>), suspected third infections (<code>third</code>), and suspected fourth infections (<code>fourth</code>) by specimen receipt date (<code>date</code>)</li> </ul> <p>Note: There may be some inconsistencies with the numbers of infections through time in earlier versions of this data set due to back-filling of late-arriving data.</p> <p> </p> <p>Note: Earlier versions of this data set included data files for Pulliam, JRC, C van Schalkwyk, B Lombard, N Govender, A von Gottberg, C Cohen, MJ Groome, J Dushoff, K Mlisana, and H Moultrie. <a href="https://www.science.org/doi/10.1126/science.abn4947">Increased risk of SARS-CoV-2 reinfection associated with emergence of Omicron in South Africa</a>. DOI: 0.1126/science.abn4947</p> <p>For code and more details see: <a href="https://github.com/jrcpulliam/reinfections/releases/tag/v3.0">https://github.com/jrcpulliam/reinfections/releases/tag/v3.0</a> or <a href="https://zenodo.org/record/6108448">10.5281/zenodo.6108448</a></p> <p>The version of this data set associated with the publication (available via the links above) included the following files:</p> <ul> <li><code>ts_data.csv</code> - national daily time series of newly detected putative primary infections (<code>cnt</code>), suspected second infections (<code>reinf</code>), suspected third infections (<code>third</code>), and suspected fourth infections (<code>fourth</code>) by specimen receipt date (<code>date</code>)</li> <li><code>demog_data.csv</code> - counts of individuals eligible for reinfection (<code>total</code>), who have 0 suspected reinfections (<code>no_reinf</code>) or >0 suspected reinfections (<code>reinf</code>) by province (<code>province</code>), age group (5-year bands, <code>agegrp5</code>), and sex (M = Male, F = Female, U = Unknown, <code>sex</code>)</li> <li><code>posterior_90_null.RData</code> - posterior samples from the MCMC fitting procedure (as used in the manuscript)</li> <li><code>sim_90_null.RDS</code> - simulation results (as used in the manuscript)</li> <li><code>emp_haz_sens_an.RDS</code> - output of sensitivity analysis of relative empirical hazard estimation to assumed observation probabilities (as used in the manuscript)</li> </ul>
Datasets for ``Quadratic growth during the COVID-19 pandemic: merging hotspots and reinfections''
<pre>This directory contains an index.html file with links to the run directories for Figs.8-11 and idl plotting routines with secondary data for the other figures for the paper "Quadratic growth during the COVID-19 pandemic: merging hotspots and reinfections" by Axel Brandenburg (Nordita); see https://arxiv.org/abs/2206.15459. </pre>
Incidence of SARS-CoV-2 reinfection in a pediatric cohort in Kuwait
<p><strong><span>Objective: </span></strong><span>Subsequent protection from severe acute respiratory syndrome-related coronavirus 2 (SARS-COV-2) infection in pediatrics is not well reported in the literature. We aimed to describe the clinical characteristics and dynamics of SARS-CoV-2 PCR repositivity in children. </span></p> <p><strong><span>Design:</span></strong><span> This is a population-level retrospective cohort study</span></p> <p><strong><span>Setting: </span></strong><span>Patients were identified through multiple national-level electronic coronavirus disease 2019 (COVID-19) databases covering all Kuwait's primary, secondary and tertiary centers. </span></p> <p class="MsoNormal"><strong><span>Participants: The </span></strong><span>study included children 12 years and younger over an 11-month period between 2020 and 2021. SARS-CoV-2 reinfection was defined as having two or more positive SARS-CoV-2 PCR done on a respiratory sample, at least 45 days apart. Clinical data were obtained from the Pediatric COVID-19 Registry in Kuwait (PCR-Q8). </span></p> <p class="MsoNormal"><strong><span>Primary and secondary outcome measures:</span></strong><span> The primary measure is to estimate the SARS-CoV-2 PCR repositivity rate. The secondary objective was to establish average duration between first and subsequent SARS-CoV-2 infection.</span><span> </span><span>Descriptive statistics was used to present clinical data for each infection episode. Also, incidence-sensitivity analysis was performed to evaluate 60- and 90-day PCR repositivity intervals.</span></p> <p><strong><span>Results:</span></strong><span> Thirty pediatric COVID-19 patients had SARS-CoV-2 reinfection at an incidence of 1.02 (95% CI 0.71-1.45) infection per 100,000 person-days and a median time to reinfection of 83 days (IQR 62-128.75). <span>The incidence of reinfection decreased to 0.78 (95% CI 0.52-1.17) and 0.47 (95% CI 0.28-0.79) per person-days when the minimum interval between PCR repositivity was increased to 60 and 90 days, respectively. </span>The mean age of reinfected subjects was 8.5 years (IQR 3.7-10.3) and the majority (70%) were females. Most children (55.2%) had asymptomatic reinfection. Fever was the most common presentation in symptomatic patients. One immunocompromised experienced two reinfection episodes.</span></p> <p><strong><span>Conclusion: </span></strong><span>SARS-CoV-2 reinfection is uncommon in children. Previous confirmed COVID-19 in children seems to result in milder reinfection.</span></p>
CD4+ T cells re-wire granuloma cellularity and regulatory networks, promoting immunomodulation following Mtb reinfection
<p>Here we include the necessary .ipynb, .h5ad, and .rds (used for cell-cell interaction analyses) files used in our work: "Immunomodulatory re-wiring of granuloma cellularity and regulatory networks by CD4 T cells following Mtb reinfection"</p>
Data for "Reinfections and cross-protection in the 1918/19 influenza pandemic: Revisiting a survey among male and female factory workers"
<p>Dataset underlying the analysis of the paper: "Reinfections and cross-protection in the 1918/19 influenza pandemic: Revisiting a survey among male and female factory workers"</p> <p>The dataset includes the following variables:</p> <ul> <li>Sex - male, female</li> <li>Age2 - age in full years</li> <li>Grippe - influenza (oui = yes, non=no)</li> <li>Times_grippe - how often did the influenza occur (0-3)</li> <li>Reinfection - 0=no, 1=yes</li> <li>Reinfection_forte - was the reinfection stronger or the same (1) or weaker (0) as the first infection?</li> <li>Severeness - 1=mild, 2 and 3 = strong</li> <li>reinf_v1 - reinfection first wave 0=no, 1=yes</li> <li>vage1 - infection first wave 0=no, 1=yes</li> <li>reinf_v2 - reinfection second wave 0=no, 1=yes</li> <li>vage2 - infection second wave 0=no, 1=yes</li> <li>reinf_v3 - reinfection third wave 0=no, 1=yes</li> <li>vage3 - infection third wave 0=no, 1=yes</li> <li>reinf_y1919 - reinfection winter 1919 0=no, 1=yes</li> <li>vage_1919 - infection winter 1919 0=no, 1=yes</li> <li>Grippe1890 - illness 1890 0=no, 1=yes</li> </ul> <p> </p> <p> </p>
Simulated data and code for: The reinfection threshold, revisited
<p>One mode by which infection-derived immunity fails is when recovery leads to a reduced but nonzero risk of reinfection. This type of partial protection is called leaky immunity, with the degree of leakiness quantified by the relative probability a previously infected individual will get infected upon exposure compared to a naively susceptible individual. Previous authors have defined the reinfection threshold, which occurs when the basic reproduction number equals the inverse of the leakiness, however, there has been some debate about whether or not this is a real threshold. Here we show how the reinfection threshold relates to two important occurrences: (1) the point at which the endemic equilibrium changes from being a stable spiral to a stable node, and (2) the point at which the rate of change of the prevalence increases the most relative to leakiness. When the recovery period is short relative to the average lifetime then both occurrences are close to the reinfection threshold. We show how these results are related to the reinfection threshold found in other models of imperfect immunity. To further demonstrate the significance of this threshold in modeling, we conducted a simulation study to evaluate some of the consequences the reinfection threshold might have in parameter estimation and modeling. Using specific parameter values chosen to reflect an acute infection, we found that the basic reproduction number values larger than that of the reinfection threshold value were less identifiable than those below the threshold.</p>
Data for: A catalytic model for SARS-CoV-2 reinfections: Performing simulation-based validation and extending the model to include nth infections
<p>For code and more details see: </p> <ul> <li><code>inf_for_sbv.RDS</code> - simluated timeseries of primary infections used in the simulation-based validation of reinfections. </li> <li><code>inf_for_sbv_third.RDS</code> - simluated timeseries of primary infections used in the simulation-based validation of third infections. </li> <li><code>3_posterior_90_null_correctdata.RData</code> - posterior samples from the MCMC fitting procedure (as used in the manuscript) when not considering a second lambda parameter (to third infections)</li> <li><code>3_posterior_90_null_l2_correctdata.RData</code> - posterior samples from the MCMC fitting procedure (as used in the manuscript) when considering a second lambda parameter (to third infections)</li> <li><code>3_sim_90_null_correctdata.RDS</code> - simulation results when not considering a second lambda parameter for third infections (as used in the manuscript)</li> <li><code>3_sim_90_null_l2_correctdata.RDS</code> - simulation results when considering a second lambda parameter for third infections (as used in the manuscript)</li> </ul> <p> </p>
COVID-19 Antibody and Reinfection Study
ClinicalTrials.gov study NCT05365750. IPD Sharing: NO. Countries: 1. Publications: 1.
Clinicaly Manifested Reinfections with Borrelia Burgdorferi Sensu Lato
ClinicalTrials.gov study NCT06835075. IPD Sharing: NO. Countries: 1. Publications: 0.
Incidence of SARS-CoV-2 reinfection in a pediatric cohort in Kuwait
Open the record for dataset details and reuse information.
Simulated data and code for: The reinfection threshold, revisited
Open the record for dataset details and reuse information.
COVID-19 reinfection data on individuals diagnosed with their first SARS-CoV-2 infection
Open the record for dataset details and reuse information.
Data from: Whole genome sequencing shows sleeping sickness relapse is due to parasite regrowth and not reinfection
The trypanosome Trypanosoma brucei gambiense (Tbg) is a cause of human African trypanosomiasis (HAT) endemic to many parts of sub-Saharan Africa. The disease is almost invariably fatal if untreated and there is no vaccine, which makes monitoring and managing drug resistance highly relevant. A recent study of HAT cases from the Democratic Republic of the Congo reported a high incidence of relapses in patients treated with melarsoprol. Of the 19 Tbg strains isolated from patients enrolled in this study, four pairs were obtained from the same patient before treatment and after relapse. We used whole genome sequencing to investigate whether these patients were infected with a new strain, or if the original strain had regrown to pathogenic levels. Clustering analysis of 5938 single nucleotide polymorphisms supports the hypothesis of regrowth of the original strain, as we found that strains isolated before and after treatment from the same patient were more similar to each other than to other isolates. We also identified 23 novel genes that could affect melarsoprol sensitivity, representing a promising new set of targets for future functional studies. This work exemplifies the utility of using evolutionary approaches to provide novel insights and tools for disease control.
Evaluation of COVID-19 Immune Barrier and Reinfection Risk
ClinicalTrials.gov study NCT05774093. IPD Sharing: Not stated. Countries: 1. Publications: 3.
Effect of Liver and Blood-stage Treatment on Subsequent Plasmodium Reinfection and Morbidity
ClinicalTrials.gov study NCT02143934. IPD Sharing: Not stated. Countries: 1. Publications: 7.
Long-term Follow up Helicobacter Pylori Reinfection Rate After Second-Line Treatment: Bismuth-Containing Quadruple Therapy Versus Moxifloxacin-Based Triple Therapy
ClinicalTrials.gov study NCT01792700. IPD Sharing: Not stated. Countries: 1. Publications: 1.
Exogenous Reinfection of Tuberculosis in Taiwan
ClinicalTrials.gov study NCT00173433. IPD Sharing: Not stated. Countries: 1. Publications: 20.
Interventions to Curb Hepatitis C Reinfections Among Men Who Have Sex With Men
ClinicalTrials.gov study NCT04156945. IPD Sharing: NO. Countries: 2. Publications: 4.
Effects of Vitamin A Supplementation on Intestinal Parasitic Reinfections
ClinicalTrials.gov study NCT00936091. IPD Sharing: Not stated. Countries: 1. Publications: 5.
HCV Reinfection After DAA Therapy in PWID in Belgium
ClinicalTrials.gov study NCT04251572. IPD Sharing: UNDECIDED. Countries: 1. Publications: 1.
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
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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.