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1,751 results for “transmission”
Results complementing the European Union summary report on surveillance for the presence of transmissible spongiform encephalopathies (TSE) - Luxembourg
<p>This dataset contains TSE surveillance results in cattle, sheep, goats, cervids and other species, and genotyping in sheep, pursuant to Regulation (EC) 999/2001.</p> <p><strong>Reporting authorities contributing to each data collection:</strong></p> <ul> <li>TSE_2023_LU: Veterinary Services Administration (ASV)</li> <li>TSE_2022_LU: Veterinary Services Administration (ASV)</li> <li>TSE_2021_LU: Veterinary Services Administration (ASV)</li> <li>TSE_2020_LU: Veterinary Services Administration (ASV)</li> <li>TSE_2019_LU: Veterinary Services Administration (ASV)</li> </ul>
Results complementing the European Union summary report on surveillance for the presence of transmissible spongiform encephalopathies (TSE) - Bosnia and Herzegovina
<p>This dataset contains TSE surveillance results in cattle, sheep, goats, cervids and other species, and genotyping in sheep, pursuant to Regulation (EC) 999/2001.</p> <p><strong>Reporting authorities contributing to each data collection</strong>:</p> <ul> <li>TSE_2023_BA: Food Safety Agency of Bosnia and Herzegovina (FSA)</li> <li>TSE_2022_BA: Food Safety Agency of Bosnia and Herzegovina (FSA)</li> <li>TSE_2021_BA: Food Safety Agency of Bosnia and Herzegovina (FSA)</li> <li>TSE_2020_BA: Food Safety Agency of Bosnia and Herzegovina (FSA)</li> </ul> <p>For Bosnia and Herzegovina, 2020 is the first year reporting TSE surveillance results.</p>
Data for: The transmission ability in a population of elite tetraploid potatoes
<p>Data set for the research article: Assessing the transmission potential of elite tetraploid potatoes.</p> <p>The dataset includes pedigree and phenotypic data of 5013 clones of an F1 population derived from an incomplete diallel cross of 18 parents of either established cultivars or elite breeding material from Danespo A/S across three market segments (starch, processing, and table). A total of 10 phenotypes are included, namely dry matter content, yield, senescence, skin finish, flesh color, length/width ratio, length, diameter, tubers/plant, and eye depth. In addition, GBS genotype data for a set of 93,170 biallelic SNPs filtered to MAF > 1 %, coverage > 5 and < 60, and missing rate < 70 %. Pedigree (A), genomic (G), and single-step combined (H) matrices are included. A README file is included with descriptions of all provided data files. </p>
Unraveling Dengue Serotype 3 Transmission in Brazil: Evidence for Multiple Introductions of the 3III_B.3.2 Lineage
<p>Dengue, caused by DENV 1-4, remains a global public health concern, with Brazil experiencing some of the largest epidemics. The reemergence of DENV-3 in Brazil between 2023 and 2024 has raised concerns about new outbreaks due to the absence of sustained circulation of this serotype in recent years. This study investigates the dynamics of DENV-3 in Brazil, focusing on the spread of the 3III_B.3.2 lineage within genotype 3III and its introduction routes. We analyzed 1,536 DENV-3 genomes, all classified as genotype 3III, the dominant DENV-3 genotype in Brazil since 2001. Phylogenetic analysis identified the 3III_B.3.2 lineage in all recent Brazilian cases, with detections also reported in Central America, the United States, and Europe. At least six independent introduction events of this lineage into Brazil were identified, with the Caribbean region and Costa Rica as the primary sources. The earliest introduction likely occurred in late 2022 in Roraima, followed by introductions in Sao Paulo, Minas Gerais, and Para. While one instance of interstate transmission was detected - from Sao Paulo to Minas Gerais - our findings indicate that external introductions, rather than domestic spread, were the primary drivers of DENV-3 circulation during this period. These results underscore the importance of continued genomic surveillance and coordinated public health strategies to monitor and mitigate future outbreaks</p>
Data and Code: Familial transmission of neural representations for mental arithmetic across two generations
<p>Here we provide anonymized behavioral data, individual beta maps and analyses codes used in "Familial transmission of neural representations for mental arithmetic across two generations".</p> <p>Because of anonymization concerns within the framework of EU privacy regulations (<a href="https://gdpr-info.eu/">GDPR</a>), we cannot provide raw MRI data. <br>Therefore, the fMRI data consists of individual beta maps from the first-level analysis, which correspond to the brain activity associated with increases in problem size for each operation (addition and subtraction). Maps are normalized into the MNI template. See paper for details about the preprocessing and first-level analysis.</p> <p>The dataset consists of mother-child dyads. Mothers are assigned codes of 200 or higher. Children are assigned codes below 200. Each child's code is exactly 200 less than their mother's code.</p> <p>The analyses codes require Python version 3.8.8 and Nilearn version 0.8.1.</p> <p>If you have any questions, please send an email to charlotte.constant@inserm.fr. </p> <p> </p>
TSE results complementing the European Union summary report on surveillance for the presence of transmissible spongiform encephalopathies (TSE) - the United Kingdom
<p>This dataset contains TSE surveillance results in cattle, sheep, goats, cervids and other species, and genotyping in sheep, pursuant to Regulation (EC) 999/2001.</p> <p>REPORTING AUTHORITIES CONTRIBUTING TO EACH DATA COLLECTION: TSE_2020_UK: Animal and Plant Health Agency (APHA)</p>
An epidemiological Study to Assess Household Transmission & Associated Risk Factors for COVID-19 Disease amongst Residents of Delhi, India.
<p><strong><em>Executive summary</em></strong>: Studying the spread and epidemiological characteristics of COVID-19 virus specially in household settings are needed to prepare our self-better in preventing and controlling this epidemic. In this study we proposed a conceptual framework of four level of determinates and tried to understand the transmission dynamics of COVID-19 among household contacts along with clinical, epidemiological and virologic characteristics of the infection. </p> <p><strong>Aims & Objectives:</strong></p> <ol> <li>the proportion of asymptomatic cases and symptomatic cases;</li> <li>the incubation period of COVID-19 and the duration of infectiousness and of detectable shedding;</li> <li>the serial interval of COVID-19 infection; </li> <li>clinical risk factors for COVID-19, and the clinical course and severity of disease; </li> <li>high-risk population subgroups;</li> <li>the secondary infection rate and secondary clinical attack rate of COVID-19 infection among household contacts; and</li> <li>the associations of various factors across four dimensions interaction associated with risk of transmission</li> </ol> <p><strong>Methodology:</strong> This was a case-ascertained study where all susceptible contacts of a laboratory confirmed COVID-19 case were studied prospective for four weeks after their enrolment. It was done in New Delhi, during the end of first wave as well as whole second wave from December 2020 to July 2021. The study team collected the key information by questionnaire along with blood and oro-nasal swab during the household visits. Follow-up was done on day 7, 14 and 28 for observing the disease characteristic and symptomatology along with confirmation by serum and oro-nasal swab testing. Daily characteristics of the infection were noted by the participants on symptoms diary.</p> <p><strong>Results: </strong>We enrolled 99 households, each having one laboratory-confirmed COVID-19 index case along with their 318 susceptible contacts. By the end of the follow-up, secondary infection rate was seen at 55.5%, while seroconversion in 46.6%. Hospitalization and case fatality rate was 3.83% and 1.7% respectively. Among epidemiological characteristics we observed serial interval of 8.0 ± 6.7 days, generation time 3.8 ± 6.4, while secondary attack rate was 54.9%. The predictors of secondary infection among individual contact level were being female (OR:2.13, 95% CI:1.27 - 3.57), age of the household contact (1.01;1.00 - 1.03), symptoms at baseline (3.39; 1.61- 7.12) and during follow-up (3.18; 1.64 - 6.19), while only symptoms during follow-up (3.81: 1.43 - 10.14) and being RT-PCR positive (8.32; 3.22 -21.54) was significantly and independently associated with seroconversion among household contacts. Among index case-level age of the primary case (1.03; 1.01 -1.04) and any symptoms during follow-up (6.29; 1.83-21.63) significantly and independently associated with secondary infection while any symptoms during follow-up was associated with seroconversion among household contacts. Among household-level characteristics having more rooms (4.44; 2.16 - 9.13) independently associated with secondary infection, while more rooms (3.98; 1.23 -12.90) along with overcrowding (0.37; 0.16 - 0.82) associated with seroconversion. Among contact pattern only taking care of the index case (2.02;1.21- 3.38) was significantly and independently associated with secondary infection, while none was associated with seroconversion.</p> <p><strong>Conclusion: </strong>A high secondary cases and secondary attack rate was seen in our study. This highlights the need to adopts strict measure and advocate COVID appropriate behaviours in order to break the transmission chain at household level. The targeted approach at household contacts with higher risk would be efficient in limiting the development of infection among susceptible contacts. </p>
Fig. 4 in The Role Of Different Mollusk Species In Maintaining The Transmission Of Polyhostal Trematode Species In Ukrainian Polissya Waters: The Specificity Of Trematode Parthenogenetic Generations To Mollusk Hosts
Fig. 4. The distribution of olygoxenic three-host trematode species in the parthenitae host species of mollusks: A — E. stantschinskii; B — P. ovata; C — C. cornutus; D — T. clavata.
Fig. 3 in The Role Of Different Mollusk Species In Maintaining The Transmission Of Polyhostal Trematode Species In Ukrainian Polissya Waters: The Specificity Of Trematode Parthenogenetic Generations To Mollusk Hosts
Fig. 3. The distribution of polyxenic trematode species in the parthenitae host species of mollusks: A — H. conoideum; B — E. recurvatum.
Fig. 6 in The Role Of Different Mollusk Species In Maintaining The Transmission Of Polyhostal Trematode Species In Ukrainian Polissya Waters: The Specificity Of Trematode Parthenogenetic Generations To Mollusk Hosts
Fig. 6. The distribution of olygoxenic two-host trematode species in the parthenitae host species of mollusks: A — P. ichikawai; B — D. subclavatus; C — F. hepatica; D — L. constantinovae; E — A. imitans.
Fig. 2 in The Role Of Different Mollusk Species In Maintaining The Transmission Of Polyhostal Trematode Species In Ukrainian Polissya Waters: The Specificity Of Trematode Parthenogenetic Generations To Mollusk Hosts
Fig. 2. The distribution of olygoxenic three-host trematode species in the parthenitae host species of mollusks: A — H. cylindracea; B — H. variegatus; C — E. aconiatum; D — E. revolutum.
Fig. 5 in The Role Of Different Mollusk Species In Maintaining The Transmission Of Polyhostal Trematode Species In Ukrainian Polissya Waters: The Specificity Of Trematode Parthenogenetic Generations To Mollusk Hosts
Fig. 5. The distribution of polyxenic trematode species in the parthenitae host species of mollusks: A — N. attentuatus; B — L. scotiae.
Fig. 1 in A Case Study Of The Herb-Dwelling Spider Assemblages (Aranei) In A Meadow Under The Power Transmission Lines In Ukrainian Carpathians
Fig. 1. Number of individuals collected at the different distances from high voltage power line near Irliava village, August 2012 (SD — standard deviation).
Fig. 2 in A Case Study Of The Herb-Dwelling Spider Assemblages (Aranei) In A Meadow Under The Power Transmission Lines In Ukrainian Carpathians
Fig. 2. Total eudominants and dominants, recedents and subrecedents relative abundance, Shannon and Pielou indexes values at the different distances from high voltage power line near Irliava village (according to the two year samples).
Dataset for 'Experimental Quantification of Gas Dispersion in 3D-Printed Logpile Structures Using a Noninvasive Infrared Transmission Technique'
<p>This dataset contains the infrared images of tracer flow that were taken in the investigations of transverse dispersion in 3D-printed logpile structures. Accompanying the files (which are labelled according to the convention of the camera software) is a Python script which can be used to link the images to the operating conditions at which they were obtained. Documentation of this script can be found in the file at the very top. <br> This dataset was used as basis for the journal article 'Experimental Quantification of Gas Dispersion in 3D-Printed Logpile Structures Using a Noninvasive Infrared Transmission Technique', published in ACS Engineering Au under DOI:<a href="https://doi.org/10.1021/acsengineeringau.1c00040">10.1021/acsengineeringau.1c00040</a>. This paper can also be found in this repository at https://zenodo.org/record/6517082</p> <p> </p>
The transmission of pottery technology amongst prehistoric European hunter-gatherers: code and data
<p>Included in this paper are the data files which enable the main analytical findings of the paper to be reproduced. Some aspects of the spatial-temporal modelling will heavily depend on the user’s configuration and the digital elevation model available, so intermediate data that support the main conclusions of the paper have been included in the data repository. </p>
Supplementary Data: OpenCOVID model output underlaying Figures 1 and 2 of "Modelling the impact of Omicron and emerging variants on SARS-CoV-2 transmission and public health burden"
<p>Supplementary data files <strong>Figure_1.xlsx</strong> and <strong>Figure_2.xlsx</strong> contain the model simulation outcomes for Figures 1 and 2 of <a href="https://www.medrxiv.org/content/10.1101/2021.12.12.21267673v2">Le Rutte, Shattock <em>et al</em></a> "<strong>Modelling the impact of Omicron and emerging variants on SARS-CoV-2 transmission and public health burden</strong>" (2022)</p> <ul> <li><strong>Figure 1</strong>: Peak daily hospital occupancy (number of beds per 100,000 population over the six-month simulation period) for three variant properties; infectivity (relative to Delta), immune evading capacity (%), and severity (relative to Delta)<br> </li> <li><strong>Figure 2</strong>: Percentage of COVID-19 infections and deaths averted by third-dose vaccines for adults and vaccinating 5-11-year-olds with doses one and two.<br> </li> <li>Open access source-codes of the associated plotting functions are published <a href="http://zenodo.org/record/6532404#.Yqw7cezMKdb">here</a> on Zenodo.<br> </li> <li>Open access source-codes for the OpenCOVID model of all analyses as presented in <a href="https://www.medrxiv.org/content/10.1101/2021.12.12.21267673v2">Le Rutte, Shattock <em>et al.</em> (2022)</a> are publicly available at <a href="https://github.com/SwissTPH/OpenCOVID/tree/manuscript_december_2021/src">https://github.com/SwissTPH/OpenCOVID/tree/manuscript_december_2021/src</a>.<br> </li> <li>Detailed model descriptions and model equations of individual-based transmission model <strong>OpenCOVID</strong> are described in <a href="https://pubmed.ncbi.nlm.nih.gov/34923396/">Shattock <em>et al</em>. (2022)</a> and <a href="https://www.medrxiv.org/content/10.1101/2021.12.12.21267673v2">Le Rutte, Shattock <em>et al.</em> (2022).</a></li> </ul>
Transmission vectors of essential Tsimane knowledge and skills: dataset and code.
<p>In our research report <strong><em>Cultural transmission vectors of essential knowledge and skills among Tsimane forager-farmers</em></strong>, we examine reported patterns of culture transmission contributing to 92 essential skills among a sample of 421 Tsimane forager-horticulturalists. We collected data for the study using a <em>Skills Survey </em>to identify vectors and types of influence responsible for the transmission of 92 skills important among Tsimane (Schniter et al. 2015). Here we provide the anonymized data for Schniter et al.’s 2022 study (as both a .csv file and as a .sav file) as well as both code and outputs (as an .spv file) for statistical analyses performed using IBM SPSS Version 24.</p> <p>For additional details about the <em>Skills Survey </em>see</p> <p>Schniter, E., Gurven, M., Kaplan, H. S., Wilcox, N. T., & Hooper, P. L. (2015). Skill ontogeny among Tsimane forager‐horticulturalists. <em>American journal of physical anthropology</em>, <em>158</em>(1), 3-18.</p> <p>Attached:</p> <p>TransmissionData.csv</p> <p>TransmissionData.sav</p> <p>Regressions&Frenquencies.spv</p>
Alternative Covid-19 mitigation measures in school classrooms: Analysis using an agent-based model of SARS-CoV-2 transmission
<p>The SARS-CoV-2 epidemic continues to have major impacts on children's education, with schools required to implement infection control measures that have led to long periods of absence and classroom closures. We have developed an agent-based epidemiological model of SARS-CoV-2 transmission that allows us to quantify projected infection patterns within primary school classrooms, and related uncertainties; the basis of our approach is a contact model constructed using random networks, informed by structured expert judgment. The effectiveness of mitigation strategies is considered in terms of effectiveness at suppressing infection outbreaks and limiting pupil absence. Covid-19 infections in schools in the UK in Autumn 2020 are re-examined and the model used for forecasting infection levels in autumn 2021, as the more infectious Delta-variant was emerging and school transmission was thought likely to play a major role in an incipient new wave of the epidemic. Our results are in good agreement with available data and indicate that testing-based surveillance of infections in the classroom population with isolation of positive cases is a more effective mitigation measure than bubble quarantine both for reducing transmission in primary schools and for avoiding pupil absence, even accounting for the insensitivity of self-administered tests. Bubble quarantine entails large numbers of pupils being absent from school, with only a modest impact on classroom infection levels. However, maintaining a reduced contact rate within the classroom can have a major beneficial impact on managing Covid-19 in school settings.</p>
Data and Software Archive for "Likely community transmission of COVID-19 infections between neighboring, persistent hotspots in Ontario, Canada"
<p>This is the Zenodo archive for the manuscript "Likely community transmission of COVID-19 infections between neighboring, persistent hotspots in Ontario, Canada" (Mucaki EJ, Shirley BC and Rogan PK. <em>F1000Research</em> 2021, <strong>10</strong>:1312, DOI: <a href="http://dx.doi.org/10.12688/f1000research.75891.1">10.12688/f1000research.75891.1</a>). This study aimed to produce community-level geo-spatial mapping of patterns and clusters of symptoms, and of confirmed COVID-19 cases, in near real-time in order to support decision-making. This was accomplished by area-to-area geostatistical analysis, space-time integration, and spatial interpolation of COVID-19 positive individuals. This archive will contain data and image files from this study, which were too numerous to be included in the manuscript for this study. It also provides all program files pertaining to the <em>Geostatistical Epidemiology Toolbox </em>(Geostatistical analysis software package to be used in ArcGIS), as well as all other scripts described in this manuscript and other software developed (cluster, outlier, streak identification and pairing)..</p> <p>We also provide a guide which provides a general description of the contents of the four sections in this archive (<em>Documentation_for_Sections_of_Zenodo_Archive.docx</em>). If you have any intent to utilize the data provided in Section 3, we greatly advise you to review this document as it describes the output of all geostatistical analyses performed in this study in detail.</p> <p><strong>Data Files:</strong></p> <p><strong>Section 1. "Section_1.Tables_S1_S7.Figures_S1_S11.zip"</strong></p> <p>This section contains all additional tables and figures described in the manuscript "Likely community transmission of COVID-19 infections between neighboring, persistent hotspots in Ontario, Canada". Additional tables S1 to S7 are presented in an Excel document. These 7 tables provide summary statistics of various geostatistical tests described in the study (“Section 1 – Tables S1-S4”) and lists all identified single and paired high-case cluster streaks (“Section 1 – Tables S5-S7”). This section also contains 11 additional figures referred to in the manuscript (“Section 1 – Figures S1-S11”) both individually and within a Word document which describes them.</p> <p><strong>Section 2. "Section_2.Localized_Hotspot_Lists.zip"</strong></p> <p>All localized hotspots (identified through kriging analysis) were catalogued for each municipality evaluated (Hamilton, Kitchener/Waterloo, London, Ottawa, Toronto, Windsor/Essex). These files indicate the FSA in which the hotspot was identified, the date in which it was identified (utilizing 3-day case data at the postal code level), the amount of cases which occurred within the FSA within these 3 dates, the range of cases interpolated by kriging analysis (between 5-10, 10-15, 15-20, 20-25, 25-30, 30-35, 35-40, 40-50, >50), and whether or not the FSA was deemed a hotspot by Gi* relative to the rest of Ontario on any of the three dates evaluated. Please see Section 4 for map images of these localized hotspots.</p> <p><strong>Section 3. "Section_3.All-Data_Files.Kriging_GiStar_Local_and_GlobalMorans.2020_2021"</strong></p> <p>Section 3 – All output files from the geostatistical tests performed in this study are provided in this section. This includes the output from Ontario-wide FSA-level Gi* and Cluster and Outlier analyses, and PC-level Cluster and Outlier, Spatial Autocorrelation, and kriging analysis of 6 municipal regions. It also includes kriging analysis of 7 other municipal regions adjacent to Toronto (Ajax, Brampton, Markham, Mississauga, Pickering, Richmond Hill and Vaughan). This section also provides data files from our analyses of stratified case data (by age, gender, and at-risk condition). All coordinates presented in these data files are given in “PCS_Lambert_Conformal_Conic” format. Case values between 1-5 were masked (appear as “NA”).</p> <p><strong>Section 4. "Section_4.All_Map_Images_of_Geostat_Analyses.zip"</strong></p> <p>Sets of image files which map the results of our geostatistical analyses onto a map of Ontario or within the municipalities evaluated (Hamilton, Kitchener/Waterloo, London, Ottawa, Toronto, Windsor/Essex) are provided. This includes: Kriging analysis (PC-level), Local Moran's I cluster and outlier analysis (FSA and PC-level), normal and space-time Gi* analysis, and all images for all analyses performed on stratified data (by age, gender and at-risk condition). Kriging contour maps are also included for 7 other municipal regions adjacent to Toronto (Ajax, Brampton, Markham, Mississauga, Pickering, Richmond Hill and Vaughan). </p> <p><strong>Software:</strong></p> <p>This Zenodo archive also provides all program files pertaining to the <em>Geostatistical Epidemiology Toolbox </em>(Geostatistical analysis software package to be used in ArcGIS), as well as all other scripts described in this manuscript. This geostatistical toolbox was developed by CytoGnomix Inc., London ON, Canada and is distributed freely under the terms of the GNU General Public License v3.0. It can be easily modified to accommodate other Canadian provinces and, with some additional effort, other countries. </p> <p>This distribution of the <em>Geostatistical Epidemiology Toolbox </em>does not include postal code (PC) boundary files (which are required for some of the tools included in the toolbox). The PC boundary shapefiles used to test the toolbox were obtained from <a href="https://www.dmtispatial.com/">DMTI</a> (<a href="https://www.google.com/url?q=https://www.dmtispatial.com/canmap/&sa=D&source=hangouts&ust=1637875735980000&usg=AOvVaw2wG3iVnyGyrkTIkN5FQ4NS">https://www.dmtispatial.com/canmap/</a>) through the Scholar's Geoportal at the University of Western Ontario (<a href="http://geo2.scholarsportal.info/">http://geo2.scholarsportal.info/</a>). The distribution of these files (through sharing, sale, donation, transfer, or exchange) is strictly prohibited. However, any equivalent PC boundary shape file should suffice, provided it contains polygon boundaries representing postal code regions (see guide for more details).</p> <p><strong>Software File 1. "Software.GeostatisticalEpidemiologyToolbox.zip"</strong></p> <p>The Geostatistical Epidemiology Toolbox is a set of custom Python-based geoprocessing tools which function as any built-in tool in the ArcGIS system. This toolbox implements data preprocessing, geostatistical analysis and post-processing software developed to evaluate the distribution and progression of COVID-19 cases in Canada. The purpose of developing this toolbox is to allow external users without programming knowledge to utilize the software scripts which generated our analyses and was intended to be used to evaluate Canadian datasets. While the toolbox was developed for evaluating the distribution of COVID-19, it could be utilized for other purposes. </p> <p>The toolbox was developed to evaluate statistically significant distributions of COVID-19 case data at Canadian Forward Sortation Area (FSA) and Postal Code-level in the province of Ontario utilizing geostatistical tools available through the ArcGIS system. These tools include: 1) Standard Gi* analysis (finds areas where cases are significantly spatially clustered), 2) spacetime based Gi* analysis (finds areas where cases are both spatially and temporally clustered), 3) cluster and outlier analysis (determines if high case regions are an regional outlier or part of a case cluster), 4) spatial autocorrelation (determines the cases in a region are clustered overall) and, 5) Empirical Bayesian Kriging analysis (creates contour maps which define the interpolation of COVID-19 cases in measured and unmeasured areas). Post-processing tools are included that import these all of the preceding results into the ArcGIS system and automatically generate PNG images. </p> <p>This archive also includes a guide ("UserManual_GeostatisticalEpidemiologyToolbox_CytoGnomix.pdf") which describes in detail how to set up the toolbox, how to format input case data, and how to use each tool (describing both the relevant input parameters and the structure of the resultant output files).</p> <p><strong>Software File 2: “Software.Additional_Programs_for_Cluster_Outlier_Streak_Idendification_and_Pairing.zip"</strong></p> <p>In the manuscript associated with this archive, Perl scripts were utilized to evaluate postal code-level Cluster and Outlier analysis to identify significantly, highly clustered postal codes over consecutive periods (i.e., high-case cluster “streaks”). The identified streaks are then paired to those in close proximity, based on the neighbors of each postal code from PC centroid data ("paired streaks"). Multinomial logistic regression models were then derived in the R programming language to measure the correlation between the number of cases reported in each paired streak, the interval of time separating each streak, and the physical distance between the two postal codes. Here, we provide the 3 Perl scripts and the R markdown file which perform these tasks:</p> <p><em>“Ontario_City_Closest_Postal_Code_Identification.pl”</em></p> <p>Using an input file with postal code coordinates (by centroid), this program identifies the nearest neighbors to all postal codes for a given municipal region (the name of this region is entered on the command line). Postal code centroids were calculated in ArcGIS using the “Calculate Geometry” function against DMTI postal code boundary files (not provided). Input from other sources could be used, however, as long as the input includes a list of coordinates with a unique label associated with a particular municipality.</p> <p>The output of this program (for the same municipal region being evaluated) is required for the following two Perl scripts:</p> <p><em>“Local_Morans_Analysis.Recurrent_Clustered_PC_Identifier.pl”</em></p> <p>This program uses the output of postal code-level Cluster and Outlier analysis for a municipality (these files are available in a second Zenodo archive: <a href="http://doi.org/10.5281/zenodo.5585812">doi.org/10.5281/zenodo.5585812</a>) and the output from <em>“Ontario_City_Closest_Postal_Code_Identification.pl” </em>(for the same municipal region) as input to identify high-case clustered postal codes that occur consecutively over a course of several dates (referred to as high-case cluster “streaks”). The script allows for a single day in which the PC was either not clustered or did not meet the minimum case count threshold of ≥ 6 cases within the 3-day sliding window (i.e. if clustered for 3 days, then not significant for one, then clustered for 3 more days, it will considered a 7 day streak). This script also lists any neighbors that are also identified to have streaks during these same dates.</p> <p><em>“Local_Morans_Analysis.Clustered_Streak_Pairing_Program.pl”</em></p> <p>This program uses the output from “<em>Local_Morans_Analysis.Recurrent_Clustered_PC_Identifier.pl</em>” to pair streaks that were identified in two closely situated postal codes spatially (requires output from <em>“Ontario_City_Closest_Postal_Code_Identification.pl”)</em>. The output of this script provides the postal codes of the streaks which are paired, describe the interval of each streak (and whether they occur concurrently), the number of cases which occurred during these streaks, and how these streaks are separated (both distance [in meters] and temporally [in days]).</p> <p>"<em>Streak_Analysis_using_Multinomial_Logistic_Regression_Models.Rmd</em>"</p> <p>This R Markdown file contains the code which derived multinomial logistic regression models to describe the relation between the number of COVID-19 case counts, physical distance (in meters), and the time interval between paired streaks (in days). The script then performs a Wald two-tailed z-test to identify which factors are significantly correlated (relative to total case counts between streaks [i.e., the response variable]). The p-values computed from the Wald test are then reported. This script requires the 'multinom' function of the 'nnet' package in R.</p> <p>Two data files in which these models were derived (a list of all consecutive Toronto paired streaks for COVID-19 wave 2 and wave 3) are also included. </p>
ScienceDex guides
Understand access before you commit
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.