How to Read the map.This map allows you to visualize the trends over time and cases, recoveries, deaths and testing at the regional health unit. The Map shows the relative state of the COVID-19 outbreak in each region. Colour (red to green) shows the time since a new reported case.

7 Day Hot Spots

The map highlights regions with an active outbreak with a "glowing ball". The size of the ball reflects the average number of new cases in the past 7 days as a rate per 100K population.

High

Low

Important InformationNot all data is reported for all regional health units. Data sources are consulted every 24 hours, however not all organizations report on a daily bases. As this data is cumulative, values carry-forward if updates are not provided. Values can go down due to corrected errors as reported. Data SourcesThe source of the data for each regional health unit is listed in the "SourceURL" field.

Looking for the raw data? You can find it here.

Geostatistics analyzes and predicts the values associated with spatial or spatial-temporal phenomena. It incorporates the spatial (and in some cases temporal) coordinates of the data within the analyses. It is a practical means of describing spatial patterns and interpolating values for locations where samples were not taken (and measures the uncertainty of those values, which is critical to informed decision making). This archive contains results of geostatistical analysis of COVID-19 case counts for all available US counties. Test results were obtained with ArcGIS Pro (ESRI). Sources are state health departments, which are scraped and aggregated by the Johns Hopkins Coronavirus Resource Center and then pre-processed by MappingSupport.com.

This update of the Zenodo dataset (version 6) consists of three compressed archives containing geostatistical analyses of SARS-CoV-2 testing data. This dataset utilizes many of the geostatistical techniques used in previous versions of this Zenodo archive, but has been significantly expanded to include analyses of up-to-date U.S. COVID-19 case data (from March 24th to September 8^th, 2020):

Archive #1: “1.Geostat. Space-Time analysis of SARS-CoV-2 in the US (Mar24-Sept6).zip” – results of a geostatistical analysis of COVID-19 cases incorporating spatially-weighted hotspots that are conserved over one-week timespans. Results are reported starting from when U.S. COVID-19 case data first became available (March 24^th, 2020) for 25 consecutive 1-week intervals (March 24th through to September 6th, 2020). Hotspots, where found, are reported in each individual state, rather than the entire continental United States.

Archive #2: "2.Geostat. Spatial analysis of SARS-CoV-2 in the US (Mar24-Sept8).zip" – the results from geostatistical spatial analyses only of corrected COVID-19 case data for the continental United States, spanning the period from March 24^th through September 8th, 2020. The geostatistical techniques utilized in this archive includes ‘Hot Spot’ analysis and ‘Cluster and Outlier’ analysis.

Archive #3: "3.Kriging and Densification of SARS-CoV-2 in LA and MA.zip" – this dataset provides preliminary kriging and densification analysis of COVID-19 case data for certain dates within the U.S. states of Louisiana and Massachusetts.

These archives consist of map files (as both static images and as animations) and data files (including text files which contain the underlying data of said map files [where applicable]) which were generated when performing the following Geostatistical analyses: Hot Spot analysis (Getis-Ord Gi*) [‘Archive #1’: consecutive weeklong Space-Time Hot Spot analysis; ‘Archive #2’: daily Hot Spot Analysis], Cluster and Outlier analysis (Anselin Local Moran's I) [‘Archive #2’], Spatial Autocorrelation (Global Moran's I) [‘Archive #2’], and point-to-point comparisons with Kriging and Densification analysis [‘Archive #3’].

The Word document provided ("Description-of-Archive.Updated-Geostatistical-Analysis-of-SARS-CoV-2 (version 6).docx") details the contents of each file and folder within these three archives and gives general interpretations of these results.

The following report outlines the workflow used to optimize your Find Hot Spots result:Initial Data Assessment.There were 2933 valid input features.There were 3108 valid input aggregation areas.There were 3108 valid input aggregation areas.There were 66 outlier locations; these will not be used to compute the optimal fixed distance band.Incident AggregationAnalysis was based on the number of points in each polygon cell.Analysis was performed on all aggregation areas.The aggregation process resulted in 3108 weighted areas.Incident Count Properties:Min0.0000Max0.0015Mean0.0001Std. Dev.0.0001Scale of AnalysisThe optimal fixed distance band was based on the average distance to 30 nearest neighbors: 150682.0000 Meters.Hot Spot AnalysisThere are 865 output features statistically significant based on a FDR correction for multiple testing and spatial dependence.OutputRed output features represent hot spots where high incident counts cluster.Blue output features represent cold spots where low incident counts cluster.

The New York Times is releasing a series of data files with cumulative counts of coronavirus cases in the United States, at the state and county level, over time. We are compiling this time series data from state and local governments and health departments in an attempt to provide a complete record of the ongoing outbreak.

Since late January, The Times has tracked cases of coronavirus in real time as they were identified after testing. Because of the widespread shortage of testing, however, the data is necessarily limited in the picture it presents of the outbreak.

We have used this data to power our maps and reporting tracking the outbreak, and it is now being made available to the public in response to requests from researchers, scientists and government officials who would like access to the data to better understand the outbreak.

The data begins with the first reported coronavirus case in Washington State on Jan. 21, 2020. We will publish regular updates to the data in this repository.

In summer 2020, SARS-CoV-2 was detected on mink farms in Utah. An interagency One Health response was initiated to assess the extent of the outbreak and included sampling animals from or near affected mink farms and testing them for SARS-CoV-2 and non-SARS coronaviruses. Among the 365 animals sampled, including domestic cats, mink, rodents, raccoons, and skunks, 261 (72%) of the animals harbored at least one coronavirus at the time. Among the samples which could be further characterized, 126 alphacoronaviruses and 88 betacoronaviruses (including 74 detections of SARS-CoV-2) were identified. Moreover, at least 10% (n=27) of the corona-virus-positive animals were found to be co-infected with more than one coronavirus. Our findings indicate an unexpectedly high prevalence of coronavirus among the domestic and wild animals tested on mink farms and raise the possibility that commercial animal husbandry operations could be potential hot spots for future trans-species viral spillover and the emergence of new pandemic coronaviruses. Figure 1. Phylogenetic relationships of the identified coronaviruses from mink and other animals from mink farms in Utah. The four genera of coronaviruses are highlighted in different colors. AlphaCoV, alkphacoronavirus; BetaCoV, betacoronavirus; DeltaCoV, deltacoronaviruses; and GammaCoV, gammacoronavirus. Type species for the currently recognized subgenera are annotated according to the nomenclature scheme used in this manuscript with the addition of the ICTV subgenus. Additional viruses, including the closest GenBank entry as identified by the BLAST tool, were included to help delineate relationship. Red circles are viruses identified in this study. Panel A. Full phylogenetic tree (A full-size image is included in Supplementary Figure 1). Red arrows designate the group of nearly identical Utah mink coronavirus strains collapsed into the colored triangle in Panel B. Table 1. Coronavirus distribution among species tested. The species are listed by their common names; Total, the total number of animals of each species tested; Negative, number of each species with no coronavirus detected among the tissues tested; Positive, number of animals positive for coronavirus in at least one tissue; % Pos, percentage of coronavirus positives in each species. Table 2. Detailed tissue panel tested for SARS-CoV-2. The distribution of SARS-CoV-2 RNA detection in the first 96 animals is listed. Tissue, tissue or tissue pools received; Total, total number tested in each category; Negative, number of N1 RT-PCR negatives; Posi-tives, number of N1 RT-PCR positives; % Pos, percentage of tissues positive for corona-virus. Table 3. Summary of coronaviruses identified. The distribution of coronaviruses detected and characterized according to their host is listed. Species, common name of animal species tested; AlphaCoV, number of alphacoronaviruses identified; BetaCoV, number of betacoronaviruses identified; Sequenced, number of viruses identified by sequencing, Unchar, number of coronavirus-positive samples not further characterized. Table 4. SARS-CoV-2 coinfections identified in Utah mammals. The individual animals that are both SARS-CoV-2 positive and infected with a second coronavirus are listed. Animal ID, Unique animal identification number; Common name, common name of animal; Scientific name, scientific name of animal; Sex, F, female, M, male. Unk, un-known; Age, A adult, J juvenile, Unk, unknown; SARS-CoV-2, Neg-N1 RT-PCR nega-tive, Pos-N1 RT-PCR positive, Second strain, genus and common name of the coronavirus, Pan-CoV RT-PCR Equivocal, sample is PCR positive but not further characterized. Supplementary Figure 1. Phylogenetic relationships of the identified coronaviruses from mink farms in Utah. The four genera of coronaviruses are highlighted in different colors. AlphaCoV, alkphacoronavirus; BetaCoV, betacoronavirus; DeltaCoV, deltacoronaviruses; and GammaCoV, gammacoronavirus. Type species for the currently recognized subgenera are annotated according to the nomenclature scheme used in this manuscript with the addition of the ICTV subgenus. Additional viruses, including the closest GenBank entry as identified by the BLAST tool were included to help delineate relationship. Red circles are viruses identified in this study. Supplementary Table 1. List of animals and tissues sampled and RT-PCR test results. Animal ID, unique identifier for each animal; Specimen ID, unique identifier for each tissue; Common name, common name of the animal species; Scientific name, scientific name of the animal species, Sex, F-female, M-male, UNK-unknown; Age, J-juvenile, A-adult, UNK-unknown; Tissue, organ or organ pools tested; Tissue study, X denotes the animals and tissues used in the tissue distribution sub-study; N1 PCR, Ct values from the CDC N1 assay; Pan-CoV PCR, Neg, negative, Pos, positive, Equiv, equivocal; * wild mink. Supplementary Table 2. Summary of coronavirus test results. Animal ID, unique identifier for each animal; Common name, common name of the animal species; Scientific name, scientific name of the animal species, Sex, F-female, M-male, UNK-unknown; Age, J-juvenile, A-adult, UNK-unknown; CoV, Neg-negative, Pos-positive on either one or both RT-PCR tests; SARS-CoV-2, animals positive in the CDC N1 test; AlphaCoV, the tissues positive for alphacoronavirus for each animal is listed; BetaCoV, the tissues positive for betacoronavirus for each animal is listed; C-colon, C/R-colon/rectum pool, H-heart, L-lung, L/S-live/spleen pool, S int-small intestine; Co-infections, Y-yes; PCR only, Y-yes; Virus identified by sequencing, brief name of virus identified.

Identifying and locating areas – hot spots – that present high concentration of observations in a high-dimensional data set is crucial in many data processing and analysis methods and techniques, since observations that belong to the same hot spot share information and behave in a similar way. A useful tool towards that aim is the reduction of the data dimensionality and the graphical representation of them. In the present paper, a new method to identify and locate hot spots is proposed, based on the Andrews curves. Simulations results demonstrate the performance of the proposed method, which is also applied to a high-dimensional data set, regarding caregiver distress related to symptoms of people with neurocognitive disorder and to the mental effects of the recent outbreak of the COVID-19 pandemic.

We examined the effect of social distancing on changes in visits to urban hotspot points of interest. In a pandemic situation, urban hotspots could be potential superspreader areas as visits to urban hotspots can increase the risk of contact and transmission of a disease among a population. We mapped origin-destination networks from census block groups to points of interest (POIs), such as restaurants, museums, and schools, in sixteen cities in the United States. We adopted a coarse-grain approach to examine patterns of visits to POIs among hotspots and non-hotspots from January to May 2020. Also, we conducted chi-square tests to identify POIs with significant flux-in changes during the analysis period. The results showed disparate patterns across cities in terms of reduction in hotspot POI visits. Sixteen cities are divided into two categories. In one category, which includes the cities of, San Francisco, Seattle, and Chicago, we observe a considerable decrease in hotspot POI visits, while in another category, including the cites of, Austin, Houston, and San Diego, the visits to hotspots did not greatly decrease. While all the cities exhibited overall decreasing visits to POIs, one category maintained the proportion of visits to hotspot POIs. The proportion of visits to some POIs (e.g., Restaurants) remained stable during the social distancing period, while some POIs had an increased proportion of visits (e.g., Grocery Stores). We also identified POIs with significant flux-in changes, showing that related businesses were greatly affected by social distancing.

Short-term forecasts of traditional streams from public health reporting (such as cases, hospitalizations, and deaths) are a key input to public health decision-making during a pandemic. Since early 2020, our research group has worked with data partners to collect, curate, and make publicly available numerous real-time COVID-19 indicators, providing multiple views of pandemic activity in the U.S. This paper studies the utility of five such indicators---derived from de-identified medical insurance claims, self-reported symptoms from online surveys, and COVID-related Google search activity---from a forecasting perspective. For each indicator, we ask whether its inclusion in an autoregressive (AR) model leads to improved predictive accuracy relative to the same model excluding it. Such an AR model, without external features, is already competitive with many top COVID-19 forecasting models in use today. Our analysis reveals that (a) inclusion of each of these five indicators improves on the overall predictive accuracy of the AR model; (b) predictive gains are in general most pronounced during times in which COVID cases are trending in "flat" or "down" directions; (c) one indicator, based on Google searches, seems to be particularly helpful during "up" trends.

Complete descriptions as well as code are available from https://github.com/cmu-delphi/covidcast-pnas/ and are permanently accessible at https://doi.org/10.5281/zenodo.5639567.

This material is based on work supported by gifts from Facebook, Google.org, the McCune Foundation, and Optum.

Updates

• Notice of data discontinuation: Since the start of the pandemic, AP has reported case and death counts from data provided by Johns Hopkins University. Johns Hopkins University has announced that they will stop their daily data collection efforts after March 10. As Johns Hopkins stops providing data, the AP will also stop collecting daily numbers for COVID cases and deaths. The HHS and CDC now collect and visualize key metrics for the pandemic. AP advises using those resources when reporting on the pandemic going forward.

  • CDC Weekly case and death counts (national and state level)
  • CDC County level cases and deaths
  • HHS New hospital admissions
  • CDC NowCast COVID variant proportions (national and regional level)

• April 9, 2020

  • The population estimate data for New York County, NY has been updated to include all five New York City counties (Kings County, Queens County, Bronx County, Richmond County and New York County). This has been done to match the Johns Hopkins COVID-19 data, which aggregates counts for the five New York City counties to New York County.

• April 20, 2020

  • Johns Hopkins death totals in the US now include confirmed and probable deaths in accordance with CDC guidelines as of April 14. One significant result of this change was an increase of more than 3,700 deaths in the New York City count. This change will likely result in increases for death counts elsewhere as well. The AP does not alter the Johns Hopkins source data, so probable deaths are included in this dataset as well.

• April 29, 2020

  • The AP is now providing timeseries data for counts of COVID-19 cases and deaths. The raw counts are provided here unaltered, along with a population column with Census ACS-5 estimates and calculated daily case and death rates per 100,000 people. Please read the updated caveats section for more information.

• September 1st, 2020

  • Johns Hopkins is now providing counts for the five New York City counties individually.

• February 12, 2021

  • The Ohio Department of Health recently announced that as many as 4,000 COVID-19 deaths may have been underreported through the state’s reporting system, and that the "daily reported death counts will be high for a two to three-day period."
  • Because deaths data will be anomalous for consecutive days, we have chosen to freeze Ohio's rolling average for daily deaths at the last valid measure until Johns Hopkins is able to back-distribute the data. The raw daily death counts, as reported by Johns Hopkins and including the backlogged death data, will still be present in the new_deaths column.

• February 16, 2021

  - Johns Hopkins has reconciled Ohio's historical deaths data with the state.

  Overview

The AP is using data collected by the Johns Hopkins University Center for Systems Science and Engineering as our source for outbreak caseloads and death counts for the United States and globally.

The Hopkins data is available at the county level in the United States. The AP has paired this data with population figures and county rural/urban designations, and has calculated caseload and death rates per 100,000 people. Be aware that caseloads may reflect the availability of tests -- and the ability to turn around test results quickly -- rather than actual disease spread or true infection rates.

This data is from the Hopkins dashboard that is updated regularly throughout the day. Like all organizations dealing with data, Hopkins is constantly refining and cleaning up their feed, so there may be brief moments where data does not appear correctly. At this link, you’ll find the Hopkins daily data reports, and a clean version of their feed.

The AP is updating this dataset hourly at 45 minutes past the hour.

To learn more about AP's data journalism capabilities for publishers, corporations and financial institutions, go here or email kromano@ap.org.

Queries

Use AP's queries to filter the data or to join to other datasets we've made available to help cover the coronavirus pandemic

• Filter cases by state here

• Rank states by their status as current hotspots. Calculates the 7-day rolling average of new cases per capita in each state: https://data.world/associatedpress/johns-hopkins-coronavirus-case-tracker/workspace/query?queryid=481e82a4-1b2f-41c2-9ea1-d91aa4b3b1ac

• Find recent hotspots within your state by running a query to calculate the 7-day rolling average of new cases by capita in each county: https://data.world/associatedpress/johns-hopkins-coronavirus-case-tracker/workspace/query?queryid=b566f1db-3231-40fe-8099-311909b7b687&showTemplatePreview=true

• Join county-level case data to an earlier dataset released by AP on local hospital capacity here. To find out more about the hospital capacity dataset, see the full details.

• Pull the 100 counties with the highest per-capita confirmed cases here

• Rank all the counties by the highest per-capita rate of new cases in the past 7 days here. Be aware that because this ranks per-capita caseloads, very small counties may rise to the very top, so take into account raw caseload figures as well.

Interactive

The AP has designed an interactive map to track COVID-19 cases reported by Johns Hopkins.

@(https://datawrapper.dwcdn.net/nRyaf/15/)

Interactive Embed Code

<iframe title="USA counties (2018) choropleth map Mapping COVID-19 cases by county" aria-describedby="" id="datawrapper-chart-nRyaf" src="https://datawrapper.dwcdn.net/nRyaf/10/" scrolling="no" frameborder="0" style="width: 0; min-width: 100% !important;" height="400"></iframe><script type="text/javascript">(function() {'use strict';window.addEventListener('message', function(event) {if (typeof event.data['datawrapper-height'] !== 'undefined') {for (var chartId in event.data['datawrapper-height']) {var iframe = document.getElementById('datawrapper-chart-' + chartId) || document.querySelector("iframe[src*='" + chartId + "']");if (!iframe) {continue;}iframe.style.height = event.data['datawrapper-height'][chartId] + 'px';}}});})();</script>

Caveats

• This data represents the number of cases and deaths reported by each state and has been collected by Johns Hopkins from a number of sources cited on their website.
• In some cases, deaths or cases of people who've crossed state lines -- either to receive treatment or because they became sick and couldn't return home while traveling -- are reported in a state they aren't currently in, because of state reporting rules.
• In some states, there are a number of cases not assigned to a specific county -- for those cases, the county name is "unassigned to a single county"
• This data should be credited to Johns Hopkins University's COVID-19 tracking project. The AP is simply making it available here for ease of use for reporters and members.
• Caseloads may reflect the availability of tests -- and the ability to turn around test results quickly -- rather than actual disease spread or true infection rates.
• Population estimates at the county level are drawn from 2014-18 5-year estimates from the American Community Survey.
• The Urban/Rural classification scheme is from the Center for Disease Control and Preventions's National Center for Health Statistics. It puts each county into one of six categories -- from Large Central Metro to Non-Core -- according to population and other characteristics. More details about the classifications can be found here.

Johns Hopkins timeseries data - Johns Hopkins pulls data regularly to update their dashboard. Once a day, around 8pm EDT, Johns Hopkins adds the counts for all areas they cover to the timeseries file. These counts are snapshots of the latest cumulative counts provided by the source on that day. This can lead to inconsistencies if a source updates their historical data for accuracy, either increasing or decreasing the latest cumulative count. - Johns Hopkins periodically edits their historical timeseries data for accuracy. They provide a file documenting all errors in their timeseries files that they have identified and fixed here

Attribution

This data should be credited to Johns Hopkins University COVID-19 tracking project

Explanatory variable names and average gain for variables used by XGBoost to predict COVID positivity during the time window of interest. The given variable importance scores correspond to the XGBoost fit for data in Week 15 when the full semester is the time window of interest. Engineered features are listed in black, and basic information given from the university are shown in blue.

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. F1000Research 2021, 10:1312, 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 Geostatistical Epidemiology Toolbox (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 (Documentation_for_Sections_of_Zenodo_Archive.docx). 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.

Data Files:

Section 1. "Section_1.Tables_S1_S7.Figures_S1_S11.zip"

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.

Section 2. "Section_2.Localized_Hotspot_Lists.zip"

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.

Section 3. "Section_3.All-Data_Files.Kriging_GiStar_Local_and_GlobalMorans.2020_2021"

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”).

Section 4. "Section_4.All_Map_Images_of_Geostat_Analyses.zip"

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).

Software:

This Zenodo archive also provides all program files pertaining to the Geostatistical Epidemiology Toolbox (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 Geostatistical Epidemiology Toolbox 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).

Software File 1. "Software.GeostatisticalEpidemiologyToolbox.zip"

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).

Software File 2: “Software.Additional_Programs_for_Cluster_Outlier_Streak_Idendification_and_Pairing.zip"

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:

“Ontario_City_Closest_Postal_Code_Identification.pl”

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

As of January 1, 2025, Rome (Lazio) was the Italian province which registered the highest number of coronavirus (COVID-19) cases in the country. Milan (Lombardy) came second in this ranking, while Naples (Campania) and Turin (Piedmont) followed. These four areas are also the four most populated provinces in Italy. The region of Lombardy was the mostly hit by the spread of the virus, recording almost one sixth of all coronavirus cases in the country. The provinces of Milan and Brescia accounted for a large part of this figure. For a global overview, visit Statista's webpage exclusively dedicated to coronavirus, its development, and its impact.

As of March 10, 2023, the state with the highest rate of COVID-19 cases was Rhode Island followed by Alaska. Around 103.9 million cases have been reported across the United States, with the states of California, Texas, and Florida reporting the highest numbers of infections.

From an epidemic to a pandemic The World Health Organization declared the COVID-19 outbreak as a pandemic on March 11, 2020. The term pandemic refers to multiple outbreaks of an infectious illness threatening multiple parts of the world at the same time; when the transmission is this widespread, it can no longer be traced back to the country where it originated. The number of COVID-19 cases worldwide is roughly 683 million, and it has affected almost every country in the world.

The symptoms and those who are most at risk Most people who contract the virus will suffer only mild symptoms, such as a cough, a cold, or a high temperature. However, in more severe cases, the infection can cause breathing difficulties and even pneumonia. Those at higher risk include older persons and people with pre-existing medical conditions, including diabetes, heart disease, and lung disease. Those aged 85 years and older have accounted for around 27 percent of all COVID deaths in the United States, although this age group makes up just two percent of the total population

As of March 10, 2023, the state with the highest number of COVID-19 cases was California. Almost 104 million cases have been reported across the United States, with the states of California, Texas, and Florida reporting the highest numbers.

From an epidemic to a pandemic The World Health Organization declared the COVID-19 outbreak a pandemic on March 11, 2020. The term pandemic refers to multiple outbreaks of an infectious illness threatening multiple parts of the world at the same time. When the transmission is this widespread, it can no longer be traced back to the country where it originated. The number of COVID-19 cases worldwide has now reached over 669 million.

The symptoms and those who are most at risk Most people who contract the virus will suffer only mild symptoms, such as a cough, a cold, or a high temperature. However, in more severe cases, the infection can cause breathing difficulties and even pneumonia. Those at higher risk include older persons and people with pre-existing medical conditions, including diabetes, heart disease, and lung disease. People aged 85 years and older have accounted for around 27 percent of all COVID-19 deaths in the United States, although this age group makes up just two percent of the U.S. population

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.

In response to the impacts of COVID-19, Drive-In WiFi Hotspots provide free temporary, emergency internet access for Washingtonians who do not have broadband service to their homes.

Access is available to all residents with specific emphasis on remote learning for students. Additionally, this service can be used for job searches, telehealth, telework, unemployment filing, and census participation.

The locations listed on this map represent new Drive-In WiFi Hotspot sites located at Washington State University Extension locations, as well as new and existing Washington State Library Drive-In WiFi Hotspots.

Launching primarily as parking lot hotspots in response to the COVID-19 pandemic, the free community Wi-Fi is accessible regardless of how users arrive at the locations. Some sites also offer indoor public access during business hours. Everyone using the sites – outside or inside – must practice social distancing and hygiene precautions, including staying in your vehicle or at least six feet from other users and wearing a mask if necessary.

Each hotspot will have its own security protocol. Some will be open and others will have Children’s Internet Protection Act (CIPA) safe security installed.

Broadband equity is not just a rural challenge. The drive-In Wi-Fi hotspot project addresses underserved and economically disadvantaged communities in urban and suburban areas as well.

More information can be found: https://www.commerce.wa.gov/building-infrastructure/washington-state-drive-in-wifi-hotspots-location-finder/

This feature contain several data layers: 1 depicts the up-to-date COVID-19 cases for Nigeria by states and the 2 shows population density of Nigeria by Local Government Areas; and these were superimposed on each other for easy comparison; 3 is a map of the statistically significant population hot spot and cold spot in Nigeria. All these datasets constitute this well presented COVID-19 dashboard for monitoring Nigeria cases. Data sources include NCDC, WHO, and Africa Geoportal. The COVID-19 data is updated at least once per day, following NCDC update timeline. This layer is created and maintained by DR. NKEKI F. N. and his team (Eugene .A. Atakpiri and Akinde .N. Kolawole) to Support NCDC to fight against the spread of COVID-19 in Nigeria. This layer is opened to the public and free to share. Contact Info: Phone: +23408063131159Email: nkekifndidi@gmail.com

As of May 2, 2023, Brazil was the country with the highest number of confirmed cases of COVID-19 in Latin America and the fifth highest in the world, reaching over 37 million patients. By state, São Paulo ranked first, with more than 6.6 million confirmed cases of the disease as of September 21, 2023. Minas Gerais followed, with over 4.2 million confirmed cases of coronavirus. For further information about the coronavirus (COVID-19) pandemic, please visit our dedicated Facts and Figures page.

Contains the following information:COVID cases, case prevalence over different time spans, current COVID hotspots, and number of tests for the ABQ metro area at zip code level. Social vulnerability factors for the ABQ metro area at zip code level. COVID deaths at the small area level. The location of testing sites (updated regularly as new sites and information are found)The spread of COVID, testing, deaths, and PPE supply information by nursing homes (updated regularly)The locations of summer meal sites. This dashboard runs in this app: https://nmcdc.maps.arcgis.com/apps/MapSeries/index.html?appid=1ff0aa71c0ae427cbb5753d08ae19eabThis dashboard runs the following maps:Social Vulnerability Index, Albuquerque Metro Area, Census Tracts & Zip Codes, 2018 - https://nmcdc.maps.arcgis.com/home/item.html?id=850e8f2e7c394fb99041b94f813cb5faCOVID-19 Testing Locations - New Mexico - https://nmcdc.maps.arcgis.com/home/item.html?id=aace827af8fa4d2d9037ce5c7fb0e880COVID Deaths, NM Small Areas - CABQ - https://nmcdc.maps.arcgis.com/home/item.html?id=a56dab27204b4573a7f8d1663bc95844COVID-19 TESTING & CASES by TIME PERIODS, ZIP CODES - v1 - https://nmcdc.maps.arcgis.com/home/item.html?id=14e05ddda38d40cb9746750072d00c80Summer Meal Sites - CABQ - https://nmcdc.maps.arcgis.com/home/item.html?id=5fb8f3e689df4f03ab8be107d04fcd30Nursing Homes, COVID-19 Cases and Deaths, New Mexico and USA - https://nmcdc.maps.arcgis.com/home/item.html?id=8e74a05a32324aa3bcc07e2b1545d446

Sum of variable importances for all levels of a given categorical type.