DOI: 10.5281/zenodo.6510012
title: Data and Software Archive for "Likely community transmission of COVID-19 infections between neighboring, persistent hotspots in Ontario, Canada"

description: 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: 10.12688/f1000research.75891.1). 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).. 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. <strong>Data Files:</strong> <strong>Section 1. "Section_1.Tables_S1_S7.Figures_S1_S11.zip"</strong> 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. <strong>Section 2. "Section_2.Localized_Hotspot_Lists.zip"</strong> 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, &gt;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. <strong>Section 3. "Section_3.All-Data_Files.Kriging_GiStar_Local_and_GlobalMorans.2020_2021"</strong> 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”). <strong>Section 4. "Section_4.All_Map_Images_of_Geostat_Analyses.zip"</strong> 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). <strong>Software:</strong> 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. 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 DMTI (https://www.dmtispatial.com/canmap/) through the Scholar's Geoportal at the University of Western Ontario (http://geo2.scholarsportal.info/). 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). <strong>Software File 1. "Software.GeostatisticalEpidemiologyToolbox.zip"</strong> 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. 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. 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). <strong>Software File 2: “Software.Additional_Programs_for_Cluster_Outlier_Streak_Idendification_and_Pairing.zip"</strong> 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: <em>“Ontario_City_Closest_Postal_Code_Identification.pl”</em> 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. The output of this program (for the same municipal region being evaluated) is required for the following two Perl scripts: <em>“Local_Morans_Analysis.Recurrent_Clustered_PC_Identifier.pl”</em> 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: doi.org/10.5281/zenodo.5585812) 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. <em>“Local_Morans_Analysis.Clustered_Streak_Pairing_Program.pl”</em> 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]). "<em>Streak_Analysis_using_Multinomial_Logistic_Regression_Models.Rmd</em>" 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. 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.