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 the first reported coronavirus case in Washington State on Jan. 21, 2020, 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.

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.

On March 10, 2023, the Johns Hopkins Coronavirus Resource Center ceased its collecting and reporting of global COVID-19 data. For updated cases, deaths, and vaccine data please visit: World Health Organization (WHO)For more information, visit the Johns Hopkins Coronavirus Resource Center.COVID-19 Trends MethodologyOur goal is to analyze and present daily updates in the form of recent trends within countries, states, or counties during the COVID-19 global pandemic. The data we are analyzing is taken directly from the Johns Hopkins University Coronavirus COVID-19 Global Cases Dashboard, though we expect to be one day behind the dashboard’s live feeds to allow for quality assurance of the data.DOI: https://doi.org/10.6084/m9.figshare.125529863/7/2022 - Adjusted the rate of active cases calculation in the U.S. to reflect the rates of serious and severe cases due nearly completely dominant Omicron variant.6/24/2020 - Expanded Case Rates discussion to include fix on 6/23 for calculating active cases.6/22/2020 - Added Executive Summary and Subsequent Outbreaks sectionsRevisions on 6/10/2020 based on updated CDC reporting. This affects the estimate of active cases by revising the average duration of cases with hospital stays downward from 30 days to 25 days. The result shifted 76 U.S. counties out of Epidemic to Spreading trend and no change for national level trends.Methodology update on 6/2/2020: This sets the length of the tail of new cases to 6 to a maximum of 14 days, rather than 21 days as determined by the last 1/3 of cases. This was done to align trends and criteria for them with U.S. CDC guidance. The impact is areas transition into Controlled trend sooner for not bearing the burden of new case 15-21 days earlier.Correction on 6/1/2020Discussion of our assertion of an abundance of caution in assigning trends in rural counties added 5/7/2020. Revisions added on 4/30/2020 are highlighted.Revisions added on 4/23/2020 are highlighted.Executive SummaryCOVID-19 Trends is a methodology for characterizing the current trend for places during the COVID-19 global pandemic. Each day we assign one of five trends: Emergent, Spreading, Epidemic, Controlled, or End Stage to geographic areas to geographic areas based on the number of new cases, the number of active cases, the total population, and an algorithm (described below) that contextualize the most recent fourteen days with the overall COVID-19 case history. Currently we analyze the countries of the world and the U.S. Counties. The purpose is to give policymakers, citizens, and analysts a fact-based data driven sense for the direction each place is currently going. When a place has the initial cases, they are assigned Emergent, and if that place controls the rate of new cases, they can move directly to Controlled, and even to End Stage in a short time. However, if the reporting or measures to curtail spread are not adequate and significant numbers of new cases continue, they are assigned to Spreading, and in cases where the spread is clearly uncontrolled, Epidemic trend.We analyze the data reported by Johns Hopkins University to produce the trends, and we report the rates of cases, spikes of new cases, the number of days since the last reported case, and number of deaths. We also make adjustments to the assignments based on population so rural areas are not assigned trends based solely on case rates, which can be quite high relative to local populations.Two key factors are not consistently known or available and should be taken into consideration with the assigned trend. First is the amount of resources, e.g., hospital beds, physicians, etc.that are currently available in each area. Second is the number of recoveries, which are often not tested or reported. On the latter, we provide a probable number of active cases based on CDC guidance for the typical duration of mild to severe cases.Reasons for undertaking this work in March of 2020:The popular online maps and dashboards show counts of confirmed cases, deaths, and recoveries by country or administrative sub-region. Comparing the counts of one country to another can only provide a basis for comparison during the initial stages of the outbreak when counts were low and the number of local outbreaks in each country was low. By late March 2020, countries with small populations were being left out of the mainstream news because it was not easy to recognize they had high per capita rates of cases (Switzerland, Luxembourg, Iceland, etc.). Additionally, comparing countries that have had confirmed COVID-19 cases for high numbers of days to countries where the outbreak occurred recently is also a poor basis for comparison.The graphs of confirmed cases and daily increases in cases were fit into a standard size rectangle, though the Y-axis for one country had a maximum value of 50, and for another country 100,000, which potentially misled people interpreting the slope of the curve. Such misleading circumstances affected comparing large population countries to small population counties or countries with low numbers of cases to China which had a large count of cases in the early part of the outbreak. These challenges for interpreting and comparing these graphs represent work each reader must do based on their experience and ability. Thus, we felt it would be a service to attempt to automate the thought process experts would use when visually analyzing these graphs, particularly the most recent tail of the graph, and provide readers with an a resulting synthesis to characterize the state of the pandemic in that country, state, or county.The lack of reliable data for confirmed recoveries and therefore active cases. Merely subtracting deaths from total cases to arrive at this figure progressively loses accuracy after two weeks. The reason is 81% of cases recover after experiencing mild symptoms in 10 to 14 days. Severe cases are 14% and last 15-30 days (based on average days with symptoms of 11 when admitted to hospital plus 12 days median stay, and plus of one week to include a full range of severely affected people who recover). Critical cases are 5% and last 31-56 days. Sources:U.S. CDC. April 3, 2020 Interim Clinical Guidance for Management of Patients with Confirmed Coronavirus Disease (COVID-19). Accessed online. Initial older guidance was also obtained online. Additionally, many people who recover may not be tested, and many who are, may not be tracked due to privacy laws. Thus, the formula used to compute an estimate of active cases is: Active Cases = 100% of new cases in past 14 days + 19% from past 15-25 days + 5% from past 26-49 days - total deaths. On 3/17/2022, the U.S. calculation was adjusted to: Active Cases = 100% of new cases in past 14 days + 6% from past 15-25 days + 3% from past 26-49 days - total deaths. Sources: https://www.cdc.gov/mmwr/volumes/71/wr/mm7104e4.htm https://covid.cdc.gov/covid-data-tracker/#variant-proportions If a new variant arrives and appears to cause higher rates of serious cases, we will roll back this adjustment. We’ve never been inside a pandemic with the ability to learn of new cases as they are confirmed anywhere in the world. After reviewing epidemiological and pandemic scientific literature, three needs arose. We need to specify which portions of the pandemic lifecycle this map cover. The World Health Organization (WHO) specifies six phases. The source data for this map begins just after the beginning of Phase 5: human to human spread and encompasses Phase 6: pandemic phase. Phase six is only characterized in terms of pre- and post-peak. However, these two phases are after-the-fact analyses and cannot ascertained during the event. Instead, we describe (below) a series of five trends for Phase 6 of the COVID-19 pandemic.Choosing terms to describe the five trends was informed by the scientific literature, particularly the use of epidemic, which signifies uncontrolled spread. The five trends are: Emergent, Spreading, Epidemic, Controlled, and End Stage. Not every locale will experience all five, but all will experience at least three: emergent, controlled, and end stage.This layer presents the current trends for the COVID-19 pandemic by country (or appropriate level). There are five trends:Emergent: Early stages of outbreak. Spreading: Early stages and depending on an administrative area’s capacity, this may represent a manageable rate of spread. Epidemic: Uncontrolled spread. Controlled: Very low levels of new casesEnd Stage: No New cases These trends can be applied at several levels of administration: Local: Ex., City, District or County – a.k.a. Admin level 2State: Ex., State or Province – a.k.a. Admin level 1National: Country – a.k.a. Admin level 0Recommend that at least 100,000 persons be represented by a unit; granted this may not be possible, and then the case rate per 100,000 will become more important.Key Concepts and Basis for Methodology: 10 Total Cases minimum threshold: Empirically, there must be enough cases to constitute an outbreak. Ideally, this would be 5.0 per 100,000, but not every area has a population of 100,000 or more. Ten, or fewer, cases are also relatively less difficult to track and trace to sources. 21 Days of Cases minimum threshold: Empirically based on COVID-19 and would need to be adjusted for any other event. 21 days is also the minimum threshold for analyzing the “tail” of the new cases curve, providing seven cases as the basis for a likely trend (note that 21 days in the tail is preferred). This is the minimum needed to encompass the onset and duration of a normal case (5-7 days plus 10-14 days). Specifically, a median of 5.1 days incubation time, and 11.2 days for 97.5% of cases to incubate. This is also driven by pressure to understand trends and could easily be adjusted to 28 days. Source

Eurofound's e-survey 'Living, working and COVID-19' captures how the pandemic impacts living and working in Europe. The survey looks at quality of life and well-being, with questions ranging from life satisfaction, happiness and optimism, to health and levels of trust in institutions. Respondents are also asked about their work situation, their work–life balance and level of teleworking during COVID-19. The survey also assesses the impact of the pandemic on people’s living conditions and financial situation.

Note: This dataset is no longer being maintained and will not be updated going forward.

The weekly and cumulative number of residents with confirmed COVID-19 and with COVID-19 associated deaths is obtained from data self-reported by individual assisted living facilities to the Long Term Care Mutual Aid Plan web-based reporting system (www.mutualaidplan.org/ct). Both confirmed and suspect deaths are included.

Confirmed deaths include those among persons who tested positive for COVID-19. Suspected deaths include those among persons with signs and symptoms suggestive of COVID-19 but who did not have a laboratory positive COVID-19 test. Due to differing data collection and processing methods between LTC-MAP and the death data sources used previously, cumulative death data for residents was re-baselined on July 14, 2020. The resident death data before and after July 14, 2020 should not be added due to the differing definitions of COVID-19 associated deaths used and the possibility of duplication of deaths among prior and current data.

The cumulative number of deaths among assisted living residents is based upon data reported by the Office of the Chief Medical Examiner. For public health surveillance, COVID-19-associated deaths include persons who tested positive for COVID-19 around the time of death (laboratory-confirmed) and persons whose death certificate lists COVID-19 disease as a cause of death or a significant condition contributing to death (probable). As of 7/15/20 deaths reported by the Office of the Chief Medical Examiner are no longer being updated on a weekly basis.

The COVID-19 pandemic that spread across the world at the beginning of 2020 was not only a big threat to public health, but also to the entire sports industry. Many professional leagues closed their doors to spectators or postponed their seasons entirely. During an April 2020 survey in the United States, some ** percent of respondents stated that they would only feel comfortable attending a live sporting event in person again after *************.

The COVID-19 pandemic that spread across the world at the beginning of 2020 was not only a big threat to public health, but also to the entire sports industry. Many professional and amateur leagues and events were canceled and the public was advised to not spend time in large groups or in public areas. During an April 2020 survey in the United States, 13 percent of respondents strongly agreed with the sentiment that, although they were not really sports fans, they were planning on watching some sporting events when live sports start broadcasting again.

Summary This layer has been DEPRECATED (last updated 12/1/2021). Was formerly a weekly update. The Outbreak-Associated Cases in Congregate Living data dashboard on coronavirus.maryland.gov was redesigned on 11/17/21 to align with other outbreak reporting. Visit https://opendata.maryland.gov/dataset/MD-COVID-19-Congregate-Outbreak/ey5n-qn5s to view Outbreak-Associated Cases in Congregate Living data as reported after 11/17/21. Confirmed COVID-19 deaths among Maryland residents who live and work in congregate living facilities in Maryland for the reporting period. Description The MD COVID-19 - Total Deaths in Congregate Facility Settings data layer is a total of deaths confirmed by a positive COVID-19 test result that have been reported to MDH in nursing homes, assisted living facilities, group homes of 10 or more and state and local facilities for the reporting period. Data are reported to MDH by local health departments, the Department of Public Safety and Correctional Services and the Department of Juvenile Services. To appear on the list, facilities report at least one confirmed case of COVID-19 over the prior 14 days. Facilities are removed from the list when health officials determine 14 days have passed with no new cases and no tests pending. The list provides a point-in-time picture of COVID-19 case activity among these facilities. Numbers reported for each facility listed reflect totals ever reported for deaths. Data are updated once weekly. Terms of Use The Spatial Data, and the information therein, (collectively the "Data") is provided "as is" without warranty of any kind, either expressed, implied, or statutory. The user assumes the entire risk as to quality and performance of the Data. No guarantee of accuracy is granted, nor is any responsibility for reliance thereon assumed. In no event shall the State of Maryland be liable for direct, indirect, incidental, consequential or special damages of any kind. The State of Maryland does not accept liability for any damages or misrepresentation caused by inaccuracies in the Data or as a result to changes to the Data, nor is there responsibility assumed to maintain the Data in any manner or form. The Data can be freely distributed as long as the metadata entry is not modified or deleted. Any data derived from the Data must acknowledge the State of Maryland in the metadata.

The COVID-19 pandemic that spread across the world at the beginning of 2020 was not only a big threat to public health, but also to the entire sports industry. Many professional leagues closed their doors to spectators or postponed their seasons entirely. During an April 2020 survey in the United States, some ** percent of respondents stated that it was very important for cases in their area to decline before they would feel comfortable attending a crowded sporting event.

WageIndicator is interviewing people around the world to discover what makes the Coronavirus lockdown easier (or tougher), and what is the COVID-19 effect on our jobs, lives and mood. WageIndicator shows coronavirus-induced changes in living and working conditions in over 110 countries on the basis of answers on the following questions among others in the Corona survey: Is your work affected by the corona crisis? Are precautionary measures taken at the workplace? Do you have to work from home? Has your workload increased/decreased? Have you lost your job/work/assignments? The survey contains questions about the home situation of respondents as well as about the possible manifestation of the corona disease in members of the household. Also the effect of having a pet in the house in corona-crisis times is included.

A library of reports produced by the Department of Public Health on COVID-19 data in Connecticut, starting on March 21, 2020. Reports are published to https://portal.ct.gov/Coronavirus/COVID-19-Data-Tracker and to https://portal.ct.gov/Coronavirus/Nursing-Homes-and-Assisted-Living-Facilities Note: As of June 1, 2023 regular reporting on COVID-19 ended and will transition to align with the seasonal respiratory surveillance reporting program in October 2023. More details on the changes in reporting and end of the public health emergency can be found here. National data on COVID-19, including state and county-level data for Connecticut can also be found directly via the CDC COVID Data Tracker.

According to a survey conducted with live event firms in the United States in July 2020, around 46 percent had received Paycheck Protection Program (PPP) loans. These loans aimed to support small businesses with staff unable to work during the COVID-19 crisis. Almost a third had received no support at all.

A hub with live displays of news, information, and content related to COVID-19 extracted from Facebook, Reddit and Instagram, from around the world (or in specific regions or country) from local news outlets, regional WHO pages, government agencies, local politicians and more. Each Live Display features post streams sorted by COVID-19 or vaccine related keywords and public accounts to each region.

At the height of the coronavirus pandemic, on the last day of March 2020, Wikipedia in all languages broke a record for most traffic in a single day. Since the breakout of the Covid-19 pandemic at the start of January, tens if not hundreds of millions of people have come to Wikipedia to read - and in some cases also contribute - knowledge, information and data about the virus to an ever-growing pool of articles. Our study focuses on the scientific backbone behind the content people across the world read: which sources informed Wikipedia’s coronavirus content, and how was the scientific research on this field represented on Wikipedia. Using citation as readout we try to map how COVID-19 related research was used in Wikipedia and analyse what happened to it before and during the pandemic. Understanding how scientific and medical information was integrated into Wikipedia, and what were the different sources that informed the Covid-19 content, is key to understanding the digital knowledge echosphere during the pandemic. To delimitate the corpus of Wikipedia articles containing Digital Object Identifier (DOI), we applied two different strategies. First we scraped every Wikipedia pages form the COVID-19 Wikipedia project (about 3000 pages) and we filtered them to keep only page containing DOI citations. For our second strategy, we made a search with EuroPMC on Covid-19, SARS-CoV2, SARS-nCoV19 (30’000 sci papers, reviews and preprints) and a selection on scientific papers form 2019 onwards that we compared to the Wikipedia extracted citations from the english Wikipedia dump of May 2020 (2’000’000 DOIs). This search led to 231 Wikipedia articles containing at least one citation of the EuroPMC search or part of the wikipedia COVID-19 project pages containing DOIs. Next, from our 231 Wikipedia articles corpus we extracted DOIs, PMIDs, ISBNs, websites and URLs using a set of regular expressions. Subsequently, we computed several statistics for each wikipedia article and we retrive Atmetics, CrossRef and EuroPMC infromations for each DOI. Finally, our method allowed to produce tables of citations annotated and extracted infromations in each wikipadia articles such as books, websites, newspapers.Files used as input and extracted information on Wikipedia's COVID-19 sources are presented in this archive.See the WikiCitationHistoRy Github repository for the R codes, and other bash/python scripts utilities related to this project.

Attendees wearing masks and the availability of hand sanitizer were rated as two of the most important factors for attendees when determining if they would attend an event at the time of the COVID-19 pandemic. According to the survey, conducted with event professionals in July 2020, 90 percent thought that from their attendees perspective it would be important to have hand sanitizer available everywhere at events.

Analysis of ‘COVID-19 State Data’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/nightranger77/covid19-state-data on 28 January 2022.

--- Dataset description provided by original source is as follows ---

This dataset is a per-state amalgamation of demographic, public health and other relevant predictors for COVID-19.

Deaths, Infections and Tests by State

The COVID Tracking Project: https://covidtracking.com/data/api

Used positive, death and totalTestResults from the API for, respectively, Infected, Deaths and Tested in this dataset. Please read the documentation of the API for more context on those columns

Predictor Data and Sources

Population (2020)

Density is people per meter squared https://worldpopulationreview.com/states/

ICU Beds and Age 60+

https://khn.org/news/as-coronavirus-spreads-widely-millions-of-older-americans-live-in-counties-with-no-icu-beds/

GDP

https://worldpopulationreview.com/states/gdp-by-state/

Income per capita (2018)

https://worldpopulationreview.com/states/per-capita-income-by-state/

Gini

https://en.wikipedia.org/wiki/List_of_U.S._states_by_Gini_coefficient

Unemployment (2020)

Rates from Feb 2020 and are percentage of labor force
https://www.bls.gov/web/laus/laumstrk.htm

Sex (2017)

Ratio is Male / Female
https://www.kff.org/other/state-indicator/distribution-by-gender/

Smoking Percentage (2020)

https://worldpopulationreview.com/states/smoking-rates-by-state/

Influenza and Pneumonia Death Rate (2018)

Death rate per 100,000 people
https://www.cdc.gov/nchs/pressroom/sosmap/flu_pneumonia_mortality/flu_pneumonia.htm

Chronic Lower Respiratory Disease Death Rate (2018)

Death rate per 100,000 people
https://www.cdc.gov/nchs/pressroom/sosmap/lung_disease_mortality/lung_disease.htm

Active Physicians (2019)

https://www.kff.org/other/state-indicator/total-active-physicians/

Hospitals (2018)

https://www.kff.org/other/state-indicator/total-hospitals

Health spending per capita

Includes spending for all health care services and products by state of residence. Hospital spending is included and reflects the total net revenue. Costs such as insurance, administration, research, and construction expenses are not included.
https://www.kff.org/other/state-indicator/avg-annual-growth-per-capita/

Pollution (2019)

Pollution: Average exposure of the general public to particulate matter of 2.5 microns or less (PM2.5) measured in micrograms per cubic meter (3-year estimate)
https://www.americashealthrankings.org/explore/annual/measure/air/state/ALL

Medium and Large Airports

For each state, number of medium and large airports https://en.wikipedia.org/wiki/List_of_the_busiest_airports_in_the_United_States

Temperature (2019)

Note that FL was incorrect in the table, but is corrected in the Hottest States paragraph
https://worldpopulationreview.com/states/average-temperatures-by-state/
District of Columbia temperature computed as the average of Maryland and Virginia

Urbanization (2010)

Urbanization as a percentage of the population https://www.icip.iastate.edu/tables/population/urban-pct-states

Age Groups (2018)

https://www.kff.org/other/state-indicator/distribution-by-age/

School Closure Dates

Schools that haven't closed are marked NaN https://www.edweek.org/ew/section/multimedia/map-coronavirus-and-school-closures.html

Note that some datasets above did not contain data for District of Columbia, this missing data was found via Google searches manually entered.

--- Original source retains full ownership of the source dataset ---

Context

This Data is related to the World Fight against the Infectious Disease COVID-19 (CoronaVirus).

Content

This DataSet contains the World Data of Total Cases, Total Death, Total Tests and more by each Country and Continents.

Acknowledgements

This data is collected by Web Scraping. In this, I Scrap the data from the website Worldometers by writing the code in Python. For more, please Check the Code. Special Thanks to the Website Worldometers for providing such data. https://www.kaggle.com/samrat77/coronavirus-data-web-scraping

Inspiration

Inspired by all the others kagglers who are posting datasets and kernels on a daily bases.

Updated weekly on Thursdays Older adults and people with disabilities who live in long term care facilities are at high risk for COVID-19 illness and death. The data below describes the impacts of COVID-19 on the residents and staff of Long Term Care Facilities licensed by the State Department of Social and Health Services (DSHS), including Skilled Nursing Facilities (nursing homes); Adult Family Homes and Assisted Living Facilities. Cases and deaths are also occurring in other forms of senior housing not licensed by DSHS, including subsidized housing for people age 50+, Permanent Supportive Housing, and naturally occurring retirement communities (NORCs) and among people with disabilities living in Supportive Living Facilities (also licensed by DSHS).

NOTE: This dataset is no longer being updated as of 4/27/2023. It is retired and no longer included in public COVID-19 data dissemination. See this link for more information https://imap.maryland.gov/pages/covid-data Summary Congregate living facilities in Maryland that have active COVID-19 outbreaks. Description The MD COVID-19 Congregate Outbreak data layer represents congregate living facilities in Maryland (i.e., nursing homes, assisted living facilities, state and local facilities, and group homes with 10 or more occupants) that have active COVID-19 outbreaks, with at least one confirmed case of COVID-19 within the past 14 days. Data are based on facility reports to MDH. Facilities are removed from the list when health Terms of Use The Spatial Data, and the information therein, (collectively the "Data") is provided "as is" without warranty of any kind, either expressed, implied, or statutory. The user assumes the entire risk as to quality and performance of the Data. No guarantee of accuracy is granted, nor is any responsibility for reliance thereon assumed. In no event shall the State of Maryland be liable for direct, indirect, incidental, consequential or special damages of any kind. The State of Maryland does not accept liability for any damages or misrepresentation caused by inaccuracies in the Data or as a result to changes to the Data, nor is there responsibility assumed to maintain the Data in any manner or form. The Data can be freely distributed as long as the metadata entry is not modified or deleted. Any data derived from the Data must acknowledge the State of Maryland in the metadata.

Florida COVID-19 Cases by Zip Code exported from the Florida Department of Health GIS Layer on date seen in file name. Archived by the University of South Florida Libraries, Digital Heritage and Humanities Collections. Contact: LibraryGIS@usf.edu.Please Cite Our GIS HUB. If you are a researcher or other utilizing our Florida COVID-19 HUB as a tool or accessing and utilizing the data provided herein, please provide an acknowledgement of such in any publication or re-publication. The following citation is suggested: University of South Florida Libraries, Digital Heritage and Humanities Collections. 2020. Florida COVID-19 Hub. Available at https://covid19-usflibrary.hub.arcgis.com/.https://doi.org/10.5038/USF-COVID-19-GISLive FDOH Data Source: https://services1.arcgis.com/CY1LXxl9zlJeBuRZ/arcgis/rest/services/Florida_Cases_Zips_COVID19/FeatureServerFor data 5/10/2020 or after: Archived data was exported directly from the live FDOH layer into the archive. For data prior to 5/10/2020: Data was exported by the University of South Florida - Digital Heritage and Humanities Collection using ArcGIS Pro Software. Data was then converted to shapefile and csv and uploaded into ArcGIS Online archive. For data definitions please visit the following box folder: https://usf.box.com/s/vfjwbczkj73ucj19yvwz53at6v6w614hData definition files names include the relative date they were published. The below information was taken from ancillary documents associated with the original layer from FDOH.Q. How is the zip code assigned to a person or case? Cases are counted in a zip code based on residential or mailing address, or by healthcare provider or lab address if other addresses are missing.Q. Why is the city data and the zip code data different? The zip code data is supplied to a healthcare worker, case manager, or lab technician by each individual during intake when a test is first recorded. When entering a zip code, the system we use automatically produces a list of cities within that zip code for the individual to further specify where they live. Sometimes the individual uses the postal city, which may be Miami, when in reality that person lives outside the City of Miami boundaries in the jurisdiction of Coral Gables. Many zip codes contain multiple city/town jurisdictions, and about 20% of zip codes overlap more than one county. Q: How is the Zip Code data calculated and/or shown? If a COUNTY has five or more cases (total): • In zip codes with fewer than 5 cases, the total number of cases is shown as “<5”. • Zip codes with 0 cases in these counties are “0" or "No cases.” • All values of 5 or greater are shown by the actual number of cases in that zip code. If a COUNTY has fewer than five total cases across all of its zip codes, then ALL of the zip codes within that county show the total number of cases as "Suppressed." Q: My zip code says "SUPPRESSED" under cases. What does that mean? IF Suppressed: This county currently has fewer than five cases across all zip codes in the county. In an effort to protect the privacy of our COVID-19-Positive residents, zip code data is only available in counties where five or more cases have been reported. Q: What about PO Box zip codes, or zip codes with letters, like 334MH? PO Box zip codes are not shown in the map. “Filler” zip codes with letters, like 334MH, are typically areas where no or very few people live – like the Florida Everglades, and are shown on the map like any other zip code. Key Data about Cases by Zip Code: ZIP = The zip code COUNTYNAME = The county for the zip code (multi-part counties have been split) ZIPX = The unique county-zip identifier used to pair the data during updates POName = The postal address name assigned to the zip code place_labels = A list of the municipalities intersecting the zip code boundary c_places = The list of cities cases self-reported as being residents of Cases_1 = The number of cases in each zip code, with conditions*LabelY = A calculated field for map display only. All questions regarding this dataset should be directed to the Florida Department of Health.