This dataset contains counts of COVID-19 cases and deaths in North Carolina from March 2, 2020 to May 31, 2021. The data was extracted from NC Department of Health and Human Services' NC COVID-19 dashboard: Daily Cases and Deaths Metrics. This dataset is an archive - it is not being updated.

Data Source: NCDHHS (2021). Daily Cases and Deaths Metrics (Version 1.3) [Data set]. https://covid19.ncdhhs.gov/dashboard/data-behind-dashboards

Data from the state on statistics & counts of COVID-19 data by zipcode. This data is updated and maintained by the North Carolina GIS Department. It is typically updated manually once a day. Any questions please call the Onslow County GIS Department at 1-910-937-1190, Monday - Friday 8am - 5pm.

North Carolina NC COVID-19 Cases and Deaths by ZIP Code. This base web map was created for the NC COVID-19 web application. Data provided by NCDHHS department. Any questions please call the Onslow County GIS Department at 1-910-937-1190, Monday - Friday 8am - 5pm.

Project Tycho datasets contain case counts for reported disease conditions for countries around the world. The Project Tycho data curation team extracts these case counts from various reputable sources, typically from national or international health authorities, such as the US Centers for Disease Control or the World Health Organization. These original data sources include both open- and restricted-access sources. For restricted-access sources, the Project Tycho team has obtained permission for redistribution from data contributors. All datasets contain case count data that are identical to counts published in the original source and no counts have been modified in any way by the Project Tycho team, except for aggregation of individual case count data into daily counts when that was the best data available for a disease and location. The Project Tycho team has pre-processed datasets by adding new variables, such as standard disease and location identifiers, that improve data interpretability. We also formatted the data into a standard data format. All geographic locations at the country and admin1 level have been represented at the same geographic level as in the data source, provided an ISO code or codes could be identified, unless the data source specifies that the location is listed at an inaccurate geographical level. For more information about decisions made by the curation team, recommended data processing steps, and the data sources used, please see the README that is included in the dataset download ZIP file.

COVID-19 testing and cases in North Carolina June 1, 2020—August 31, 2020.

State comparisons data for COVID-19 cases, deaths, rates per 100,000 population from the Centers for Disease Control and Prevention. US Census Bureau Household Pulse Survey estimates for percent of persons age 18 and over with loss of employment income, expected loss of employment inocme in the next 4 weeks, food scarcity, delayed medical care, and K-12 educational changes related to the COVID-19 pandemic. Data includes a national ranking.

The COVID Tracking Project collects information from 50 US states, the District of Columbia, and 5 other US territories to provide the most comprehensive testing data we can collect for the novel coronavirus, SARS-CoV-2. We attempt to include positive and negative results, pending tests, and total people tested for each state or district currently reporting that data.

Testing is a crucial part of any public health response, and sharing test data is essential to understanding this outbreak. The CDC is currently not publishing complete testing data, so we’re doing our best to collect it from each state and provide it to the public. The information is patchy and inconsistent, so we’re being transparent about what we find and how we handle it—the spreadsheet includes our live comments about changing data and how we’re working with incomplete information.

From here, you can also learn about our methodology, see who makes this, and find out what information states provide and how we handle it.

Data for CDC’s COVID Data Tracker site on Rates of COVID-19 Cases and Deaths by Vaccination Status. Click 'More' for important dataset description and footnotes

Dataset and data visualization details: These data were posted on October 21, 2022, archived on November 18, 2022, and revised on February 22, 2023. These data reflect cases among persons with a positive specimen collection date through September 24, 2022, and deaths among persons with a positive specimen collection date through September 3, 2022.

Vaccination status: A person vaccinated with a primary series had SARS-CoV-2 RNA or antigen detected on a respiratory specimen collected ≥14 days after verifiably completing the primary series of an FDA-authorized or approved COVID-19 vaccine. An unvaccinated person had SARS-CoV-2 RNA or antigen detected on a respiratory specimen and has not been verified to have received COVID-19 vaccine. Excluded were partially vaccinated people who received at least one FDA-authorized vaccine dose but did not complete a primary series ≥14 days before collection of a specimen where SARS-CoV-2 RNA or antigen was detected. Additional or booster dose: A person vaccinated with a primary series and an additional or booster dose had SARS-CoV-2 RNA or antigen detected on a respiratory specimen collected ≥14 days after receipt of an additional or booster dose of any COVID-19 vaccine on or after August 13, 2021. For people ages 18 years and older, data are graphed starting the week including September 24, 2021, when a COVID-19 booster dose was first recommended by CDC for adults 65+ years old and people in certain populations and high risk occupational and institutional settings. For people ages 12-17 years, data are graphed starting the week of December 26, 2021, 2 weeks after the first recommendation for a booster dose for adolescents ages 16-17 years. For people ages 5-11 years, data are included starting the week of June 5, 2022, 2 weeks after the first recommendation for a booster dose for children aged 5-11 years. For people ages 50 years and older, data on second booster doses are graphed starting the week including March 29, 2022, when the recommendation was made for second boosters. Vertical lines represent dates when changes occurred in U.S. policy for COVID-19 vaccination (details provided above). Reporting is by primary series vaccine type rather than additional or booster dose vaccine type. The booster dose vaccine type may be different than the primary series vaccine type. ** Because data on the immune status of cases and associated deaths are unavailable, an additional dose in an immunocompromised person cannot be distinguished from a booster dose. This is a relevant consideration because vaccines can be less effective in this group. Deaths: A COVID-19–associated death occurred in a person with a documented COVID-19 diagnosis who died; health department staff reviewed to make a determination using vital records, public health investigation, or other data sources. Rates of COVID-19 deaths by vaccination status are reported based on when the patient was tested for COVID-19, not the date they died. Deaths usually occur up to 30 days after COVID-19 diagnosis. Participating jurisdictions: Currently, these 31 health departments that regularly link their case surveillance to immunization information system data are included in these incidence rate estimates: Alabama, Arizona, Arkansas, California, Colorado, Connecticut, District of Columbia, Florida, Georgia, Idaho, Indiana, Kansas, Kentucky, Louisiana, Massachusetts, Michigan, Minnesota, Nebraska, New Jersey, New Mexico, New York, New York City (New York), North Carolina, Philadelphia (Pennsylvania), Rhode Island, South Dakota, Tennessee, Texas, Utah, Washington, and West Virginia; 30 jurisdictions also report deaths among vaccinated and unvaccinated people. These jurisdictions represent 72% of the total U.S. population and all ten of the Health and Human Services Regions. Data on cases among people who received additional or booster doses were reported from 31 jurisdictions; 30 jurisdictions also reported data on deaths among people who received one or more additional or booster dose; 28 jurisdictions reported cases among people who received two or more additional or booster doses; and 26 jurisdictions reported deaths among people who received two or more additional or booster doses. This list will be updated as more jurisdictions participate. Incidence rate estimates: Weekly age-specific incidence rates by vaccination status were calculated as the number of cases or deaths divided by the number of people vaccinated with a primary series, overall or with/without a booster dose (cumulative) or unvaccinated (obtained by subtracting the cumulative number of people vaccinated with a primary series and partially vaccinated people from the 2019 U.S. intercensal population estimates) and multiplied by 100,000. Overall incidence rates were age-standardized using the 2000 U.S. Census standard population. To estimate population counts for ages 6 months through 1 year, half of the single-year population counts for ages 0 through 1 year were used. All rates are plotted by positive specimen collection date to reflect when incident infections occurred. For the primary series analysis, age-standardized rates include ages 12 years and older from April 4, 2021 through December 4, 2021, ages 5 years and older from December 5, 2021 through July 30, 2022 and ages 6 months and older from July 31, 2022 onwards. For the booster dose analysis, age-standardized rates include ages 18 years and older from September 19, 2021 through December 25, 2021, ages 12 years and older from December 26, 2021, and ages 5 years and older from June 5, 2022 onwards. Small numbers could contribute to less precision when calculating death rates among some groups. Continuity correction: A continuity correction has been applied to the denominators by capping the percent population coverage at 95%. To do this, we assumed that at least 5% of each age group would always be unvaccinated in each jurisdiction. Adding this correction ensures that there is always a reasonable denominator for the unvaccinated population that would prevent incidence and death rates from growing unrealistically large due to potential overestimates of vaccination coverage. Incidence rate ratios (IRRs): IRRs for the past one month were calculated by dividing the average weekly incidence rates among unvaccinated people by that among people vaccinated with a primary series either overall or with a booster dose. Publications: Scobie HM, Johnson AG, Suthar AB, et al. Monitoring Incidence of COVID-19 Cases, Hospitalizations, and Deaths, by Vaccination Status — 13 U.S. Jurisdictions, April 4–July 17, 2021. MMWR Morb Mortal Wkly Rep 2021;70:1284–1290. Johnson AG, Amin AB, Ali AR, et al. COVID-19 Incidence and Death Rates Among Unvaccinated and Fully Vaccinated Adults with and Without Booster Doses During Periods of Delta and Omicron Variant Emergence — 25 U.S. Jurisdictions, April 4–December 25, 2021. MMWR Morb Mortal Wkly Rep 2022;71:132–138

COVID-2019: Number of Cases: To Date: NC: Republic of Northern Osetia Alania data was reported at 47,139.000 Person in 31 Oct 2023. This records an increase from the previous number of 47,117.000 Person for 24 Oct 2023. COVID-2019: Number of Cases: To Date: NC: Republic of Northern Osetia Alania data is updated daily, averaging 27,432.500 Person from Apr 2020 (Median) to 31 Oct 2023, with 1136 observations. The data reached an all-time high of 47,139.000 Person in 31 Oct 2023 and a record low of 9.000 Person in 08 Apr 2020. COVID-2019: Number of Cases: To Date: NC: Republic of Northern Osetia Alania data remains active status in CEIC and is reported by Ministry of Health of the Russian Federation. The data is categorized under High Frequency Database’s Disease Outbreaks – Table RU.GF001: Disease Outbreaks: COVID-19.

COVID-2019: Number of Cases: To Date: NC: Republic of Ingushetia data was reported at 43,715.000 Person in 31 Oct 2023. This records an increase from the previous number of 43,713.000 Person for 24 Oct 2023. COVID-2019: Number of Cases: To Date: NC: Republic of Ingushetia data is updated daily, averaging 25,355.500 Person from Apr 2020 (Median) to 31 Oct 2023, with 1132 observations. The data reached an all-time high of 43,715.000 Person in 31 Oct 2023 and a record low of 7.000 Person in 08 Apr 2020. COVID-2019: Number of Cases: To Date: NC: Republic of Ingushetia data remains active status in CEIC and is reported by Ministry of Health of the Russian Federation. The data is categorized under High Frequency Database’s Disease Outbreaks – Table RU.GF001: Disease Outbreaks: COVID-19.

COVID-2019: Number of Cases: To Date: NC: Republic of Kabardino Balkaria data was reported at 76,044.000 Person in 31 Oct 2023. This records an increase from the previous number of 75,956.000 Person for 24 Oct 2023. COVID-2019: Number of Cases: To Date: NC: Republic of Kabardino Balkaria data is updated daily, averaging 37,826.500 Person from Apr 2020 (Median) to 31 Oct 2023, with 1132 observations. The data reached an all-time high of 76,044.000 Person in 31 Oct 2023 and a record low of 13.000 Person in 08 Apr 2020. COVID-2019: Number of Cases: To Date: NC: Republic of Kabardino Balkaria data remains active status in CEIC and is reported by Ministry of Health of the Russian Federation. The data is categorized under High Frequency Database’s Disease Outbreaks – Table RU.GF001: Disease Outbreaks: COVID-19.

Context

Cumulative incidences and transmissibility indicators

The results presented in this COVID-19 Panel are obtained from the declaration of COVID-19 cases to the National Epidemiological Surveillance Network (RENAVE) through the SiViES (Surveillance System of Spain) computer platform via the Web. ) managed by the National Epidemiology Center (CNE). This information comes from the epidemiological case survey that each Autonomous Community carries out when a COVID-19 case is identified.

The COVID-19 Panel presents geographic information on cumulative incidence rates at 14 days and 7 days, for the general population and for those 65+ years of age, and indicators of the evolution of the pandemic's transmissibility. For the calculation of all the parameters, the date of onset of symptoms is used or, failing that, the date of diagnosis minus 6 days (from the start of the pandemic until May 10, 2020) or minus 3 days (from of May 11); for asymptomatic cases, the date of diagnosis is used. In those cases in which there is no date of onset of symptoms or diagnosis, the key date is used (date for statistics [It was lost to the autonomous communities to define the Key date as the date of onset of symptoms and in its absence the date of declaration to the AC, until May 10, 2020. From May 11 onwards, the Key date is the earliest of the dates of consultation or diagnosis. Occasionally it can be replaced by the date of sampling] ). Until May 10, 2020, cases diagnosed by a positive diagnostic test for active infection are included, as well as all those cases hospitalized, admitted to the ICU and deaths; As of May 11, cases confirmed by PCR, or by emergency tests, are included. The population used to calculate the incidence rates comes from the official population figures resulting from the revision of the municipal census as of January 1 of the National Institute of Statistics of 2020.

A regular update of the COVID-19 situation in Spain is carried out, after an extraction from the SiViES database from 3:00 p.m. to 4:00 p.m.

All of the data in this dataset has been sourced from https://cnecovid.isciii.es/covid19/ Should you choose to use said dataset, please cite the National Epidemiological Surveillance Network (RENAVE) and the SiViES (Surveillance System of Spain)

Data

casos_diag_ccaadecl.csv: Number of cases by diagnostic technique and Autonomous Communities (declaration) - ccaa_iso: Autonomous Communities ISO code of declaration - fecha:The date of the diagnosis. In cases prior to May 11, the date of diagnosis is used, in his absence the date of declaration to the community and, in his absence, the key date (date used for statistics by the Autonomous Communities). In the cases after May 10, in the absence of a diagnosis date, the key date - num_casos:Number of reported cases confirmed with a diagnostic test positive for active infection (PDIA) as established in the Strategy for early detection, surveillance and control of COVID-19 and also cases notified before May 11 that required hospitalization, admission in the ICU or died with a clinical diagnosis of COVID-19, according to the case definitions in force at any given time. - num_casos_prueba_pcr: Number of cases with PCR laboratory test or molecular techniques - num_casos_prueba_test_ac: Number of cases with laboratory rapid antibody test - num_casos_prueba_ag: Number of cases with laboratory antigen detection test - num_casos_prueba_elisa: Number of cases with high resolution serology laboratory test (ELISA/ECLIA/CLIA) - num_casos_prueba_desconocida: Number of cases without information on the laboratory test

casos_hosp_uci_def_sexo_edad_provres.csv: Number of cases, hospitalizations, ICU admissions and deaths by sex, age and province of residence - provincia_iso: ISO code of the province of residence. NC (not stated) - sexo: Sex of the cases: H (man), M (woman), NC (not stated) - grupo_edad: Age group to which the case belongs: 0-9, 10-19, 20-29, 30-39, 40-49, 50-59, 60-69, 70-79, ≥80 years. NC: not stated. - fecha: Date of registry. Cases: In cases prior to May 11, the date of diagnosis is used, in its absence the date of declaration to the community and, in its absence, the key date (date used for statistics by the CCAA). In cases after May 10, in the absence of diagnosis date the key3 date is used. Hospitalizations, ICU admissions, deaths: hospitalized cases are represented by date of hospitalization (if not, the date of diagnosis, and in failing that, the key date, the ICU cases by date of admission to the ICU (failing that, the date of diagnosis, and failing that, the key date) and deaths by date of death (if not, the date of diagnosis, and if not, the key date.). - num_casos: Number of confirmed reported cases with a positive diagnostic test for active infection (PDIA) as established in the Early Detection Strategy,...

The COVID-19 is highly heterogeneous, ranging from cases with mild disease with an almost asymptomatic carrier to severe cases in which the disease evolves rapidly. A better understanding of monocyte response during SARS-Cov-2 infection would highlight potential biomarkers and establish other possible approaches for severe cases. Here, the promising finding was that blood NC/CL subset was skewed toward NChighCLlow and NClowCLhigh clusters among the severe COVID-19 patients. The NChighCLlow cluster in severe COVID-19 displayed a distinct clinic phenotype, implying a higher 7-day disease progression rate (P=0.019) and a worse 28-day survival (P=0.026). As supported, regarding cytokine profile in context of SARS-Cov-2 infection, it was identified that circulating NC cells are proinflammatory cells most related to regulatory cells, while CL subset displayed an effective capacity to virus. These findings have implications towards optimizing evaluation in severe COVID-19, and developing strategies that target altered balance of NC/CL cell subsets.

COVID-2019: Number of Cases: To Date: NC: Republic of Dagestan data was reported at 104,340.000 Person in 31 Oct 2023. This records an increase from the previous number of 104,294.000 Person for 24 Oct 2023. COVID-2019: Number of Cases: To Date: NC: Republic of Dagestan data is updated daily, averaging 58,818.000 Person from Apr 2020 (Median) to 31 Oct 2023, with 1139 observations. The data reached an all-time high of 104,340.000 Person in 31 Oct 2023 and a record low of 30.000 Person in 07 Apr 2020. COVID-2019: Number of Cases: To Date: NC: Republic of Dagestan data remains active status in CEIC and is reported by Ministry of Health of the Russian Federation. The data is categorized under High Frequency Database’s Disease Outbreaks – Table RU.GF001: Disease Outbreaks: COVID-19.

This file contains COVID-19 death counts and rates by month and year of death, jurisdiction of residence (U.S., HHS Region) and demographic characteristics (sex, age, race and Hispanic origin, and age/race and Hispanic origin). United States death counts and rates include the 50 states, plus the District of Columbia. Deaths with confirmed or presumed COVID-19, coded to ICD–10 code U07.1. Number of deaths reported in this file are the total number of COVID-19 deaths received and coded as of the date of analysis and may not represent all deaths that occurred in that period. Counts of deaths occurring before or after the reporting period are not included in the file. Data during recent periods are incomplete because of the lag in time between when the death occurred and when the death certificate is completed, submitted to NCHS and processed for reporting purposes. This delay can range from 1 week to 8 weeks or more, depending on the jurisdiction and cause of death. Death counts should not be compared across jurisdictions. Data timeliness varies by state. Some states report deaths on a daily basis, while other states report deaths weekly or monthly. The ten (10) United States Department of Health and Human Services (HHS) regions include the following jurisdictions. Region 1: Connecticut, Maine, Massachusetts, New Hampshire, Rhode Island, Vermont; Region 2: New Jersey, New York; Region 3: Delaware, District of Columbia, Maryland, Pennsylvania, Virginia, West Virginia; Region 4: Alabama, Florida, Georgia, Kentucky, Mississippi, North Carolina, South Carolina, Tennessee; Region 5: Illinois, Indiana, Michigan, Minnesota, Ohio, Wisconsin; Region 6: Arkansas, Louisiana, New Mexico, Oklahoma, Texas; Region 7: Iowa, Kansas, Missouri, Nebraska; Region 8: Colorado, Montana, North Dakota, South Dakota, Utah, Wyoming; Region 9: Arizona, California, Hawaii, Nevada; Region 10: Alaska, Idaho, Oregon, Washington. Rates were calculated using the population estimates for 2021, which are estimated as of July 1, 2021 based on the Blended Base produced by the US Census Bureau in lieu of the April 1, 2020 decennial population count. The Blended Base consists of the blend of Vintage 2020 postcensal population estimates, 2020 Demographic Analysis Estimates, and 2020 Census PL 94-171 Redistricting File (see https://www2.census.gov/programs-surveys/popest/technical-documentation/methodology/2020-2021/methods-statement-v2021.pdf). Rate are based on deaths occurring in the specified week and are age-adjusted to the 2000 standard population using the direct method (see https://www.cdc.gov/nchs/data/nvsr/nvsr70/nvsr70-08-508.pdf). These rates differ from annual age-adjusted rates, typically presented in NCHS publications based on a full year of data and annualized weekly age-adjusted rates which have been adjusted to allow comparison with annual rates. Annualization rates presents deaths per year per 100,000 population that would be expected in a year if the observed period specific (weekly) rate prevailed for a full year. Sub-national death counts between 1-9 are suppressed in accordance with NCHS data confidentiality standards. Rates based on death counts less than 20 are suppressed in accordance with NCHS standards of reliability as specified in NCHS Data Presentation Standards for Proportions (available from: https://www.cdc.gov/nchs/data/series/sr_02/sr02_175.pdf.).

Data Overview

This repository contains spatiotemporal data from many official sources for 2019-Novel Coronavirus beginning 2019 in Hubei, China ("nCoV_2019")

You may not use this data for commercial purposes. If there is a need for commercial use of the data, please contact Metabiota at info@metabiota.com to obtain a commercial use license.

The incidence data are in a CSV file format. One row in an incidence file contains a piece of epidemiological data extracted from the specified source.

The file contains data from multiple sources at multiple spatial resolutions in cumulative and non-cumulative formats by confirmation status. To select a single time series of case or death data, filter the incidence dataset by source, spatial resolution, location, confirmation status, and cumulative flag.

Data are collected, structured, and validated by Metabiota’s digital surveillance experts. The data structuring process is designed to produce the most reliable estimates of reported cases and deaths over space and time. The data are cleaned and provided in a uniform format such that information can be compared across multiple sources. Data are collected at the time of publication in the highest geographic and temporal resolutions available in the original report.

This repository is intended to provide a single access point for data from a wide range of data sources. Data will be updated periodically with the latest epidemiological data. Metabiota maintains a database of epidemiological information for over two thousand high-priority infectious disease events. Please contact us (info@metabiota.com) if you are interested in licensing the complete dataset.

Cumulative vs. Non-Cumulative Incidence

Reporting sources provide either cumulative incidence, non-cumulative incidence, or both. If the source only provides a non-cumulative incidence value, the cumulative values are inferred using prior reports from the same source. Use the CUMULATIVE FLAG variable to subset the data to cumulative (TRUE) or non-cumulative (FALSE) values.

Case Confirmation Status

The incidence datasets include the confirmation status of cases and deaths when this information is provided by the reporting source. Subset the data by the CONFIRMATION_STATUS variable to either TOTAL, CONFIRMED, SUSPECTED, or PROBABLE to obtain the data of your choice.

Total incidence values include confirmed, suspected, and probable incidence values. If a source only provides suspected, probable, or confirmed incidence, the total incidence is inferred to be the sum of the provided values. If the report does not specify confirmation status, the value is included in the "total" confirmation status value.

The data provided under the "Metabiota Composite Source" often does not include suspected incidence due to inconsistencies in reporting cases and deaths with this confirmation status.

Outcome - Cases vs. Deaths

The incidence datasets include cases and deaths. Subset the data to either CASE or DEATH using the OUTCOME variable. It should be noted that deaths are included in case counts.

Spatial Resolution

Data are provided at multiple spatial resolutions. Data should be subset to a single spatial resolution of interest using the SPATIAL_RESOLUTION variable.

Information is included at the finest spatial resolution provided to the original epidemic report. We also aggregate incidence to coarser geographic resolutions. For example, if a source only provides data at the province-level, then province-level data are included in the dataset as well as country-level totals. Users should avoid summing all cases or deaths in a given country for a given date without specifying the SPATIAL_RESOLUTION value. For example, subset the data to SPATIAL_RESOLUTION equal to “AL0” in order to view only the aggregated country level data.

There are differences in administrative division naming practices by country. Administrative levels in this dataset are defined using the Google Geolocation API (https://developers.google.com/maps/documentation/geolocation/). For example, the data for the 2019-nCoV from one source provides information for the city of Beijing, which Google Geolocations indicates is a “locality.” Beijing is also the name of the municipality where the city Beijing is located. Thus, the 2019-nCoV dataset includes rows of data for both the city Beijing, as well as the municipality of the same name. If additional cities in the Beijing municipality reported data, those data would be aggregated with the city Beijing data to form the municipality Beijing data.

Sources

Data sources in this repository were selected to provide comprehensive spatiotemporal data for each outbreak. Data from a specific source can be selected using the SOURCE variable.

In addition to the original reporting sources, Metabiota compiles multiple sources to generate the most comprehensive view of an outbreak. This compilation is stored in the database under the source name “Metabiota Composite Source.” The purpose of generating this new view of the outbreak is to provide the most accurate and precise spatiotemporal data for the outbreak. At this time, Metabiota does not incorporate unofficial - including media - sources into the “Metabiota Composite Source” dataset.

Quality Assurance

Data are collected by a team of digital surveillance experts and undergo many quality assurance tests. After data are collected, they are independently verified by at least one additional analyst. The data also pass an automated validation program to ensure data consistency and integrity.

NonCommercial Use License

• Creative Commons License Attribution-NonCommercial-ShareAlike 3.0 Unported (CC BY-NC-SA 3.0)

• This is a human-readable summary of the Legal Code.

• You are free:

  to Share — to copy, distribute and transmit the work to Remix — to adapt the work

• Under the following conditions:

  Attribution — You must attribute the work in the manner specified by the author or licensor (but not in any way that suggests that they endorse you or your use of the work).

  Noncommercial — You may not use this work for commercial purposes.

  Share Alike — If you alter, transform, or build upon this work, you may distribute the resulting work only under the same or similar license to this one.

• With the understanding that:

  Waiver — Any of the above conditions can be waived if you get permission from the copyright holder.

  Public Domain — Where the work or any of its elements is in the public domain under applicable law, that status is in no way affected by the license.

  Other Rights — In no way are any of the following rights affected by the license: Your fair dealing or fair use rights, or other applicable copyright exceptions and limitations; The author's moral rights; Rights other persons may have either in the work itself or in how the work is used, such as publicity or privacy rights. Notice — For any reuse or distribution, you must make clear to others the license terms of this work. The best way to do this is with a link to this web page.

For details and the full license text, see http://creativecommons.org/licenses/by-nc-sa/3.0/

Liability

Metabiota shall in no event be liable for any decision taken by the user based on the data made available. Under no circumstances, shall Metabiota be liable for any damages (whatsoever) arising out of the use or inability to use the database. The entire risk arising out of the use of the database remains with the user.

Characteristics of NC wastewater monitoring network sites.

South African COVID-19 Provincial Data

Timeline of Confirmed Cases, Deaths, Recoveries and Testing Rates

By [source]

About this dataset

This dataset provides a detailed look into the ongoing COVID-19 pandemic in South Africa. It contains data on the number of confirmed cases, deaths, recoveries, and testing rates at both a provincial and national level. With this data set, users are able to gain insight into the current state and trends of the pandemic in South Africa. This provides essential information necessary to help fight the epidemic and make informed decisions surrounding its prevention. Using this set as a resource will allow users to monitor how this devastating virus has impacted communities, plans for containment and treatment strategies all while taking into account cultural, socioeconomic factors that can influence these metrics. This dataset is an invaluable tool for understanding not only South Africa’s specific current challenge with COVID-19 but is relevant on a global scale whenit comes to fighting back against this virus that continues to wreak havoc aroundthe worldl

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How to use the dataset

How to use This Dataset

This Kaggle dataset provides an overview of the South African COVID-19 pandemic situation. It contains data regarding the number of confirmed cases, deaths, recoveries, and testing rates for each province at both the provincial and national level. In order to understand this dataset effectively, it is important to know what each column represents in this dataset. The following is a description of all column names that are included:

Column Names

• EC: Number of confirmed cases in Eastern Cape province
• FS: Number of confirmed cases in Free State province
• GP: Number of confirmed cases in Gauteng province
• KZN: Number of confirmed cases in KwaZulu Natal province
• LP: Number of confirmed cases in Limpopo province
• MP: Number of confirmed cases in Mpumalanga Province

• NC: Number total number orconfirmed casews in Northern Cape Province

  • NW :Number total numberurceof confirmes ed cacasesin North WestProvince

  • WC :Number totaconsfirme dcasescinWestern CapProvincee

  • UNKNOWN :Number totalnumberorconfirmesdacsesinsUnknown locations

  • Total :Totalnumberofconfrmecase sacrosseSouthAfrica

  • Source :Sourecodataset fedzile_Dbi ejweleputswaMangaungXharie thabo_MofutsanyanaRecoveriesDeathsYYMMDD

Research Ideas

• Creating an interactive map to show the spread of COVID-19 over time, with up date information about confirmed cases, deaths, recoveries and testing rates for each province or district.
• Constructing a machine learning model to predict the likely number of future cases in each province based on previous data activities.
• Comparing different districts and provinces within South Africa and drawing out trends among them with comparative graphical representations or independent analyses

Acknowledgements

If you use this dataset in your research, please credit the original authors. Data Source

License

License: CC0 1.0 Universal (CC0 1.0) - Public Domain Dedication No Copyright - You can copy, modify, distribute and perform the work, even for commercial purposes, all without asking permission. See Other Information.

Columns

File: covid19za_provincial_cumulative_timeline_recoveries.csv | Column name | Description | |:--------------|:---------------------------------------------------------------| | date | Date of the data entry. (Date) | | YYYYMMDD | Date in YYYYMMDD format. (String) | | EC | Number of confirmed cases in Eastern Cape Province. (Integer) | | FS | Number of confirmed cases in Free State Province. (Integer) | | GP | Number of confirmed cases in Gauteng Province. (Integer) | | KZN | Number of confirmed cases in Kwazulu Natal Province. (Integer) | | LP | Number of confirmed cases in Limpopo Province. (Integer) | | MP | Number of confirmed cases in Mpumalanga Province. (Integer) | | NC | Number of confirmed cases in Northern Cape Province. (Integer) | | ...

Demographic and epidemiological characteristics of laboratory-confirmed COVID-19 cases in Vietnam.

COVID-Net Open Source Initiative - COVIDx CT-3 Dataset

Update 2022-06-02: We released the COVIDx CT-3A and CT-3B datasets, comprising 425,024 CT slices from 5,312 patients and 431,205 CT slices from 6,068 patients, respectively.

Update 2022-03-10: The COVID-Net CT-2 paper was published in Frontiers in Medicine.

Update 2021-01-26: We released the COVID-Net CT-2 models and COVIDx CT-2A and CT-2B datasets, comprising 194,922 CT slices from 3,745 patients and 201,103 CT slices from 4,501 patients respectively. The models and dataset are described in this preprint.

Update 2020-12-23: The COVID-Net CT paper was published in Frontiers in Medicine.

Update 2020-12-03: We released the COVIDx CT-1 dataset on Kaggle.

Update 2020-09-13: We released a preprint of the COVID-Net CT paper.

COVIDx CT-3, an open access benchmark dataset that we generated from several open datasets, comprises 194,922 CT slices from 3,745 patients. We will be adding images over time to improve the dataset.

This dataset is being used to train and validate our models for COVID-19 detection from CT images. Useful dataset code and manipulation tools are available in the COVID-Net CT repository.

Different versions of the dataset may be accessed via the version history, or from the following links: * COVIDx CT-1 * COVIDx CT-2 * COVIDx CT-3

Notably, the "B" variant of the dataset is not provided here due to a more restrictive no-derivatives license. Instructions and scripts for generating the "B" variant of the dataset are available here.

COVIDx CT-3 is released under a CC BY-NC-SA 4.0 license in accordance with the licenses of its constituent datasets. Some subsets of the data have less restrictive licenses (see Data Sources below).

If you find our work useful for your research, please cite: @article{Gunraj2020, author={Gunraj, Hayden and Wang, Linda and Wong, Alexander}, title={COVIDNet-CT: A Tailored Deep Convolutional Neural Network Design for Detection of COVID-19 Cases From Chest CT Images}, journal={Frontiers in Medicine}, volume={7}, pages={1025}, year={2020}, url={https://www.frontiersin.org/article/10.3389/fmed.2020.608525}, doi={10.3389/fmed.2020.608525}, issn={2296-858X} } @article{Gunraj2022, author={Gunraj, Hayden and Sabri, Ali and Koff, David and Wong, Alexander}, title={COVID-Net CT-2: Enhanced Deep Neural Networks for Detection of COVID-19 From Chest CT Images Through Bigger, More Diverse Learning}, journal={Frontiers in Medicine}, volume={8}, pages={729287}, year={2022}, url={https://www.frontiersin.org/articles/10.3389/fmed.2021.729287}, doi={10.3389/fmed.2021.729287}, issn={2296-858X} } Links to the constituent datasets and their respective licenses and citations may be found below under the Data Sources heading.

Core COVID-Net Team

• DarwinAI Corp., Canada and Vision and Image Processing Research Group, University of Waterloo, Canada
  • Linda Wang
  • Alexander Wong
  • Zhong Qiu Lin
  • Paul McInnis
  • Audrey Chung
  • Melissa Rinch
  • Maya Pavlova
  • Naomi Terhljan
  • Siddharth Surana
  • Andy Zhao
  • Hayden Gunraj, COVID-Net for CT
  • Jeffer Peng, COVIDNet UI
• Vision and Image Processing Research Group, University of Waterloo, Canada
  • James Lee
  • Hossein Aboutalebi
  • Alex MacLean
  • Saad Abbasi
• Ashkan Ebadi and Pengcheng Xi (National Research Council Canada)
• Kim-Ann Git (Selayang Hospital)
• Abdul Al-Haimi, COVID-19 ShuffleNet Chest X-Ray Model
• Dr. Ali Sabri (Department of Radiology, Niagara Health, McMaster University, Canada)

Data Structure

COVIDx CT-3 is divided into two variants: "A" and "B". The "A" variant consists of cases with confirmed diagnoses (i.e., RT-PCR, radiologist-confirmed, etc.). The "B" variant ...