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Data source: Big Local News · About: big-local-datasette

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UHJvamVjdDo1YzU3ZTY1NS03YzJmLTRlZTUtOGI0My05YzVmYzY5YmFkOTU= biglocalnews@stanford.edu 2020-04-10T22:15:39.540617+00:00 Big Local News snapshots this data once a day from The Johns Hopkins University Center for Systems Science and Engineering. These are time series reports which provide cases, deaths and recovered for global locations as well as United States counties. Each file covers from Jan 22 to the present. For the United States there is one file for cases and one for deaths. For the global locations, there is one file for cases, one for deaths and one for recovered cases. The entire set of CSV files are refreshed daily. The zipped archives contain previous time series reports and are date stamped when they were uplaoded by Big Local News. Please cite Johns Hopkins University as described in the README. 1 COVID_Johns_Hopkins_time_series 2020-05-13T05:19:52.825000+00:00

Time series summary (csse_covid_19_time_series)

This folder contains daily time series summary tables, including confirmed, deaths and recovered. All data is read in from the daily case report. The time series tables are subject to be updated if inaccuracies are identified in our historical data. The daily reports will not be adjusted in these instances to maintain a record of raw data.

Two time series tables are for the US confirmed cases and deaths, reported at the county level. They are named time_series_covid19_confirmed_US.csv, time_series_covid19_deaths_US.csv, respectively.

Three time series tables are for the global confirmed cases, recovered cases and deaths. Australia, Canada and China are reported at the province/state level. Dependencies of the Netherlands, the UK, France and Denmark are listed under the province/state level. The US and other countries are at the country level. The tables are renamed time_series_covid19_confirmed_global.csv and time_series_covid19_deaths_global.csv, and time_series_covid19_recovered_global.csv, respectively.

Update frequency

  • Once a day around 23:59 (UTC).

Deprecated warning

The files below were archived here, and will no longer be updated. With the release of the new data structure, we are updating our time series tables to reflect these changes. Please reference time_series_covid19_confirmed_global.csv and time_series_covid19_deaths_global.csv for the latest time series data.

  • time_series_19-covid-Confirmed.csv
  • time_series_19-covid-Deaths.csv
  • time_series_19-covid-Recovered.csv
UHJvamVjdDo2YjJiYjVhOS00OTAzLTRkMDItYTU4MS00NjI0NzIyNTFkNmQ= biglocalnews@stanford.edu 2020-05-06T18:47:54.458224+00:00 Data provided by Big Local News includes hospital staffing aggregated by U.S. metropolitan area and states. Compiled from the American Hospital Association (AHA) 2018 Annual Survey, the data provides counts for medical residents/interns, registered nurses, licensed practical nurses, respiratory therapists and some physicians. Please cite the data source as: American Hospital Association 2018 Annual Survey provided via Big Local News. See the README file for additional details and data definitions. 1 COVID_AHA_Staffing 2020-05-11T22:46:26.360000+00:00  
UHJvamVjdDo3N2Q3ZjM0MS1mNWUxLTQ2MzgtOWI4Ny1lMTEyNmJkYjIyY2Y= biglocalnews@stanford.edu 2020-06-03T20:10:28.230584+00:00 Google’s Community Mobility Reports are aggregated location data showing the change in visits to places like grocery stores, parks, workplaces, etc. Big Local News takes a snap shot of this data whenever it is updated, which is typically once a week. The CSV files with "BLN" were processed by Big Local News with additional columns of data added such as rolling averages. All files are provided as one zipped file for easier downloading. The date in the file name represents the most recent data available from Google. Please read the README file for complete documentation. 1 Mobility_Google 2020-06-12T19:04:54.283000+00:00  
UHJvamVjdDo3NDQ0YTU5NC01MzlkLTQyZjktYTI3My0yMWE2MWRmMjE5MDE= biglocalnews@stanford.edu 2020-03-18T23:39:51.622526+00:00 Data provided via a collaboration between The Accountability Project and Big Local News. This project provides and joins datasets pertinent to the COVID-19 pandemic: hospital location and number of beds by type, county-level population estimates by age (which can be linked to hospital data) and nursing home location and capacity. See README for additional information. 1 COVID_HospitalBeds_CountyDemographics_NursingHomes 2020-03-23T21:49:07.674000+00:00  
UHJvamVjdDo4NTBjOWJmYy03YzAyLTRkNDgtYjYzMS04OThhODFmZjQxNDQ= biglocalnews@stanford.edu 2020-03-27T19:23:57.548111+00:00 The full text of tweets and selected Twitter metadata from the Twitter accounts of state and local public officials, as well as education and health agencies, since the beginning of the COVID-19 pandemic. We begin with state governors with more data to come. Where possible, please cite Big Local News as collecting and disseminating this information. We ask because it helps us show the impact we can have and that makes it easier to create ongoing support for Big Local News. 1 COVID_twitter_data 2020-06-13T16:36:52.113000+00:00  
UHJvamVjdDowM2ZiMjA5NS03MzcxLTRjODEtOTMzNi05YTFiZGY2YWE2NDU= biglocalnews@stanford.edu 2020-03-31T22:53:04.993458+00:00 Big Local News snapshots this data once a day from The Johns Hopkins University Center for Systems Science and Engineering. These are daily reports which provide cases, deaths and recovered for all global locations as wells as United States counties. There is one CSV file for each day's report. The entire set of CSV files are refreshed daily. The zipped archives contain previous daily reports and are date stamped when they were uplaoded by Big Local News. Please cite Johns Hopkins University as described in the README. 1 COVID_Johns_Hopkins_daily_reports 2020-05-13T05:19:30.295000+00:00

COVID-19 Data Repository by the Center for Systems Science and Engineering (CSSE) at Johns Hopkins University

This is the data repository for the 2019 Novel Coronavirus Visual Dashboard operated by the Johns Hopkins University Center for Systems Science and Engineering (JHU CSSE). Also, Supported by ESRI Living Atlas Team and the Johns Hopkins University Applied Physics Lab (JHU APL).

<br>

Visual Dashboard (desktop):<br> https://www.arcgis.com/apps/opsdashboard/index.html#/bda7594740fd40299423467b48e9ecf6 <br><br> Visual Dashboard (mobile):<br> http://www.arcgis.com/apps/opsdashboard/index.html#/85320e2ea5424dfaaa75ae62e5c06e61 <br><br> Lancet Article:<br> An interactive web-based dashboard to track COVID-19 in real time <br><br> Provided by Johns Hopkins University Center for Systems Science and Engineering (JHU CSSE):<br> https://systems.jhu.edu/ <br><br> Data Sources:<br> * World Health Organization (WHO): https://www.who.int/ <br> * DXY.cn. Pneumonia. 2020. http://3g.dxy.cn/newh5/view/pneumonia. <br> * BNO News: https://bnonews.com/index.php/2020/02/the-latest-coronavirus-cases/ <br> * National Health Commission of the People’s Republic of China (NHC): <br> http://www.nhc.gov.cn/xcs/yqtb/list_gzbd.shtml <br> * China CDC (CCDC): http://weekly.chinacdc.cn/news/TrackingtheEpidemic.htm <br> * Hong Kong Department of Health: https://www.chp.gov.hk/en/features/102465.html <br> * Macau Government: https://www.ssm.gov.mo/portal/ <br> * Taiwan CDC: https://sites.google.com/cdc.gov.tw/2019ncov/taiwan?authuser=0 <br> * US CDC: https://www.cdc.gov/coronavirus/2019-ncov/index.html <br> * Government of Canada: https://www.canada.ca/en/public-health/services/diseases/coronavirus.html <br> * Australia Government Department of Health: https://www.health.gov.au/news/coronavirus-update-at-a-glance <br> * European Centre for Disease Prevention and Control (ECDC): https://www.ecdc.europa.eu/en/geographical-distribution-2019-ncov-cases * Ministry of Health Singapore (MOH): https://www.moh.gov.sg/covid-19 * Italy Ministry of Health: http://www.salute.gov.it/nuovocoronavirus * 1Point3Arces: https://coronavirus.1point3acres.com/en * WorldoMeters: https://www.worldometers.info/coronavirus/ * COVID Tracking Project: https://covidtracking.com/data. (US Testing and Hospitalization Data. We use the maximum reported value from "Currently" and "Cumulative" Hospitalized for our hospitalization number reported for each state.) * French Government: https://dashboard.covid19.data.gouv.fr/ * COVID Live (Australia): https://www.covidlive.com.au/ * Washington State Department of Health: https://www.doh.wa.gov/emergencies/coronavirus * Maryland Department of Health: https://coronavirus.maryland.gov/ * New York State Department of Health: https://health.data.ny.gov/Health/New-York-State-Statewide-COVID-19-Testing/xdss-u53e/data * NYC Department of Health and Mental Hygiene: https://www1.nyc.gov/site/doh/covid/covid-19-data.page and https://github.com/nychealth/coronavirus-data * Florida Department of Health Dashboard: https://services1.arcgis.com/CY1LXxl9zlJeBuRZ/arcgis/rest/services/Florida_COVID19_Cases/FeatureServer/0 and https://fdoh.maps.arcgis.com/apps/opsdashboard/index.html#/8d0de33f260d444c852a615dc7837c86 * Palestine (West Bank and Gaza): https://corona.ps/details * Israel: https://govextra.gov.il/ministry-of-health/corona/corona-virus/ * Colorado: https://covid19.colorado.gov/covid-19-data

<br> Additional Information about the Visual Dashboard:<br> https://systems.jhu.edu/research/public-health/ncov/ <br><br>

Contact Us: <br> * Email: jhusystems@gmail.com <br><br>

Terms of Use:<br>

  1. This website and its contents herein, including all data, mapping, and analysis (“Website”), copyright 2020 Johns Hopkins University, all rights reserved, is provided solely for non-profit public health, educational, and academic research purposes. You should not rely on this Website for medical advice or guidance.
  2. Use of the Website by commercial parties and/or in commerce is strictly prohibited. Redistribution of the Website or the aggregated data set underlying the Website is strictly prohibited.
  3. When linking to the website, attribute the Website as the COVID-19 Dashboard by the Center for Systems Science and Engineering (CSSE) at Johns Hopkins University, or the COVID-19 Data Repository by the Center for Systems Science and Engineering (CSSE) at Johns Hopkins University.
  4. The Website relies upon publicly available data from multiple sources that do not always agree. The Johns Hopkins University hereby disclaims any and all representations and warranties with respect to the Website, including accuracy, fitness for use, reliability, completeness, and non-infringement of third party rights.
  5. Any use of the Johns Hopkins’ names, logos, trademarks, and/or trade dress in a factually inaccurate manner or for marketing, promotional or commercial purposes is strictly prohibited.
  6. These terms and conditions are subject to change. Your use of the Website constitutes your acceptance of these terms and conditions and any future modifications thereof.
UHJvamVjdDoyMjYzOTRiYy1kMmNhLTRjMGItYjcxMC1jMjdhZDJjMTNiOTU= biglocalnews@stanford.edu 2020-03-14T23:50:57.748127+00:00 AHA hospital beds data at state and metro areas provided by USA TODAY for use by journalists in Coronavirus reporting. Please cite the data from the American Hospital Association, U.S. Census, CDC and World Health Organization as the source of your analysis. If possible, in a tagline or credit of any story, share that "The figures come from a USA TODAY analysis of data from the American Hospital Association, U.S. Census, CDC and World Health Organization that was shared publicly via biglocalnews.stanford.edu." We ask this because it helps us show the impact we can have and that makes it easier to create ongoing support for Big Local News. Questions can be directed to Jayme Fraser, USA Today jfraser@gatehousemedia.com or biglocalnews@stanford.edu 1 COVID_AHA_Hospital_beds 2020-04-28T21:50:58.167000+00:00

USA TODAY analysis of Hospital Bed Capacity during Coronavirus Pandemic

Jayme Fraser, USA TODAY jfraser@gatehousemedia.com (941) 361.4923 @JaymeKFraser

What is this?

These two tables compare the number of hospital beds in each state or metro area to the number of possible cases of COVID-19. USA TODAY used 2018 population figures from the U.S. Census and the number of hospital beds from the American Hospital Association’s 2020 statistics. The AHA counts reflect figures provided by community hospitals – all nonfederal, short-term general facilities. It also includes academic medical centers and other teaching hospitals if they are nonfederal but it doesn't include places like prison hospitals or college infirmaries. The AHA data was available at a state level and for Core-Based Statistical Areas or Metropolitan Divisions. Because the infection rate of novel coronavirus in this country remains unclear, the analysis assumed 7.4%, the lowest rate from the last five years of flu data in the U.S. It also assumed that the 13.8% of patients with severe symptoms and 6.1% with critical symptoms all would need hospitalization, rates reported by the World Health Organization. Since the World Health Organization reports that people 60 and over are most at risk, the analysis also focused on that population as reported by the Census. Read the original story for reference here: https://www.usatoday.com/in-depth/news/investigations/2020/03/13/us-hospitals-overwhlemed-coronavirus-cases-result-in-too-few-beds/5002942002/

What data is available?

CoronavirusHospitalCapacity_states includes the data analysis at a state level. (51 rows, 17 columns) CoronavirusHospitalCapacity_metros includes the data analysis for metro areas in the U.S. (449 rows, 19 columns)

Column definitions, sources and calculations

GEO_ID – Census GEO ID for the state. (CoronavirusHospitalCapacity_states)

State – Full state name. The Metro CSV includes three rows with “Combined” in the state field. These are cities that straddled state borders. Not all cities that straddled state borders were combined. (CoronavirusHospitalCapacity_states + CoronavirusHospitalCapacity_metros)

Geography_Type – The AHA used three types of Census boundaries in their tables: state, core-based statistical areas (CBSA) and, in some cases, metropolitan divisions (MD). When we could not confidently determine what type was used, this field reads, “No clean match” and has no Census data linked to the AHA bed counts. There are 24 rows without clean matches. (CoronavirusHospitalCapacity_metros)

Metro – Name of the CBSA or MD, including state name. Some metros sprawl over state lines, so be mindful of that during reporting. (CoronavirusHospitalCapacity_metros)

Total_Beds – The count of hospital beds from the AHA 2020 Statistics. (CoronavirusHospitalCapacity_states + CoronavirusHospitalCapacity_metros) Estimated_Available_Beds – The American Hospital Association says that, on average nationwide, 64% of hospital beds are in use on any given day. We calculated this field by multiplying Total_Beds by .36. (CoronavirusHospitalCapacity_states + CoronavirusHospitalCapacity_metros)

Population_allages – 2018 Census Population Estimate for the geography (CoronavirusHospitalCapacity_states + CoronavirusHospitalCapacity_metros) Infected_allages – We assumed a mild flu infection rate of 7.4% and applied that to the Population_allages to estimate total possible infections. (CoronavirusHospitalCapacity_states + CoronavirusHospitalCapacity_metros)

Severe_allages – We used the WHO’s 13.8% figure and applied that to Infected_allages. (CoronavirusHospitalCapacity_states + CoronavirusHospitalCapacity_metros)

Critical_allages -- We used the WHO’s 6.1% figure and applied that to Infected_allages. (CoronavirusHospitalCapacity_states + CoronavirusHospitalCapacity_metros)

PatientsPerBed_allages – We summed Critical_allages and Severe_allages then divided by Total_Beds. (CoronavirusHospitalCapacity_states + CoronavirusHospitalCapacity_metros)

PatientsPerAvailableBed_allages – We summed Critical_allages and Severe_allages then divided by Estimated_Available_Beds. (CoronavirusHospitalCapacity_states + CoronavirusHospitalCapacity_metros)

Population_60plus -- 2018 Census Population Estimate for the geography (CoronavirusHospitalCapacity_states + CoronavirusHospitalCapacity_metros)

60plus_PercentOfTotalPopulation -- Population_60plus divided by Population_allages. (CoronavirusHospitalCapacity_states + CoronavirusHospitalCapacity_metros)

Infected_60plus -- We assumed a mild flu infection rate of 7.4% and applied that to the Population_60plus to estimate total possible infections. (CoronavirusHospitalCapacity_states + CoronavirusHospitalCapacity_metros)

Severe_60plus – We used the WHO’s 13.8% figure and applied that to Infected_60plus. (CoronavirusHospitalCapacity_states + CoronavirusHospitalCapacity_metros)

Critical_60plus -- We used the WHO’s 6.1% figure and applied that to Infected_60plus. (CoronavirusHospitalCapacity_states + CoronavirusHospitalCapacity_metros)

PatientsPerBed_60plus -- We summed Critical_60plus and Severe_60plus then divided by Total_Beds. (CoronavirusHospitalCapacity_states + CoronavirusHospitalCapacity_metros)

PatientsPerAvailableBed_60plus We summed Critical_60plus and Severe_60plus then divided by Estimated_Available_Beds. (CoronavirusHospitalCapacity_states + CoronavirusHospitalCapacity_metros)

Why should I care?

Other nations affected by the novel coronavirus pandemic have reported that severe and critical cases have overwhelmed their health care systems, sometimes requiring doctors to make difficult decisions about who to treat and who to simply make comfortable. Most American hospitals run near capacity – that’s cost effective and hospitals are still busy with flu season. We used the beds comparison as a way to gauge relative capacity to handle a local or state outbreak and to identify places where decisions might have to make tougher decisions.

What are some common pitfalls?

Beds are not a clean indicator of actual capacity, just one possible proxy. For instance, many critical access and community hospitals do not normally treat patients requiring critical care, such as ventilators, and instead transfer them to larger hospitals. The actual local preparedness also can be affected by the type of unit each bed is in and the availability of appropriate staff to treat patients. Also, the analysis assumes everyone is infected at the same time and needs hospitalization at the same time. In reality, cases will be spread out over several months, which would change hospitals’ actual ability to manage a surge. The Patients-Per-Bed ratios were intended as a benchmark to help understand the need for mitigation measures and which communities might have more difficulty accommodating a surge than others. The metro-level analysis assumes that hospitals would only serve people who live within their metro-area boundaries. We know metro hospitals also take on patients from a wider area than that. Be careful of any cities that are near borders because this data breaks up cities into chunks for each side of the border. We both know real-life people cross that line all the time. The data does include three metros for which state data was combined. Most of the 24 incomplete rows are for cities that sit on state borders. Our "available bed" calculation is based on the national average vacancy rate of 36%. The hospitals in any particular area might have more or less vacancy on any given day, especially during the tail end of flu season.

What data are missing?

There are 24 rows for which a Census geography could not be matched to the AHA name used. In those cases, there is no population data or analysis, but the bed counts are included. A reporter would need to contact AHA to clarify the boundaries that apply to that entry. There are 45 rows, mostly Metropolitan Divisions, for which the 60+ population was available, so those entries only have an all-ages analysis.

Where did this come from?

The AHA figures were scraped from a pdf table of the 2020 statistics report. “State” or “Metro” field was used to match with Census CBSAs and their populations.

Who is behind this project?

Jayme Fraser and Matt Wynn of USA Today compiled the data and did the original analysis for this story: https://www.usatoday.com/in-depth/news/investigations/2020/03/13/us-hospitals-overwhlemed-coronavirus-cases-result-in-too-few-beds/5002942002/ They also have worked with several local Gannett reporters to do follow-up stories on findings. They now have provided their analysis to Big Local so that more reporters can do the same on a topic of both international and local importance.

How was this data tested?

After Matt ran an initial analysis, Jayme spot checked several lines, and vice versa. When errors were found, cause was identified and corrected. Some metros initially could not be matched because AHA had used MDs instead of CBSAs. Jayme checked each line individually and added new matches when she could do so with confidence. Some metros also could not be matched initially because of spelling quirks, such as extra characters in Coeur d’Alene, Idaho and the difference between “St Louis” and “St. Louis.” Again, Jayme individually matched and validated each line. The assumptions made for the analysis were ran past several international infectious disease experts, who generally said the rates we used for this mental exercise were conservative. For instance, USA TODAY’s analysis estimates 23.8 million Americans could contract COVID-19. The Johns Hopkins Center for Health Security estimates that 38 million Americans will need medical care for COVID-19, including as many as 9.6 million who will need to be hospitalized – about a third of whom might need ICU-level care. In a February presentation to the American Hospital Association, Dr. James Lawler estimated that as many as 96 million Americans could be infected. We generally encourage people using this data to be cautious. On the ground details could impact local meaning. Also, this was intended to be a mental exercise to help understand some of the tough decisions ahead. It is not a hard-and-fast prediction of what is to come.

What other resources have you put up about this?

We sent a list of suggested reporting questions to our local Gannett partners to help them brainstorm. Here they are:

Hospitals -- What is your current bed vacancy? How does your average bed vacancy change during flu season? How many of those beds are in the ICU? How many negative air isolation rooms do you have? How many ventilators do you have? How many ventilators are currently in use or in use on an average flu-season day? Tell me about your surge plan and what strategies you might use to increase capacity if there's a wave of COVID-19 cases. Have you identified alternate care sites? Do you have a list of retired health workers who can boost staffing if needed? What kinds of Personal Protective Equipment do you have in stock? Do you have enough? Have you asked the state or CDC for PPE or other medical materials from their emergency stockpiles? What level of care is this hospital prepared to provide for coronavirus patients? Which hospitals in the region might you transfer patients to and under what circumstances? Have you implemented any kind of screening of all patients coming to the hospital for any kind of care? Have you implemented screening of staff? When might you consider those options?

Local leaders (health department, city/county emergency management, mayors, school districts) -- Is the local health department monitoring the number of beds and ventilators available in area hospitals? What role will the health department play in coordinating transfers or helping hospitals share resources in case of a supply shortage? At what point do you seek assistance or guidance from the state health department? What for? Does the local emergency plan identify possible alternate care sites to manage a surge of cases or to isolate and monitor people who might've been exposed? At what point would you consider closing schools or canceling large public gatherings? For how long? Do you think there is enough testing to have accurate information to make those decisions? What planning has been done to minimize the harm of requiring isolation or quarantine for workers without paid leave or childcare?

State leaders (public health officials, governor's office) -- Is the state health department monitoring the number of beds and ventilators available in area hospitals? What role will the health department play in coordinating transfers or helping hospitals share resources in case of a supply shortage? At what point do you seek assistance or guidance from the CDC? What for? Does the state emergency plan include a section specific to infectious diseases? When was it last updated? How often are state health officials giving briefings and to whom? Have state health officials talked with the governor's office about declaring an emergency? What powers would an emergency declaration give the governor or others?

UHJvamVjdDphNjNlNjkwNy02ZTliLTQ4NDgtYTljYi1hOWI2ZTlmM2ZlYjc= biglocalnews@stanford.edu 2020-03-13T19:55:17.790560+00:00 Data provided by Big Local News and snapshotted daily from the COVID19 Tracker, https://covidtracking.com/. 1 COVID_COVID19Tracker 2020-05-13T05:15:19.669000+00:00

COVID Tracking Data (CSV)

Hourly updated repository with CSV representations of data from the Covid Tracking API - see link for details on each field. Since this repository may be an hour behind our API, please use the API directly if you need the most recent data.

For information about the project and how this data is collected, see the COVID Tracking Project website and Twitter account.

CSV data files

UHJvamVjdDphYzdhMWIxMy04NjJhLTRkMzAtOThkMC0wODY3MTE5ZWIzNTU= biglocalnews@stanford.edu 2020-03-13T22:16:49.234228+00:00 Data provided by Big Local News. Data from the National Health Security Preparedness Index. The overall index is useful for examining a state’s readiness in dealing with any number of issues. There also are specific metrics that directly relate to states’ abilities to respond to the Coronavirus pandemic. With that in mind, we have pulled out and processed key metrics -- from preparedness for surge testing to evaluating how many people in each state have access to paid time off. Our goal is to make it easier for journalists to access and analyze for their reporting. For more information, please start with the NHSPI_READ_ME file. Questions? Contact biglocalnews@stanford.edu. 1 COVID_National Health Security Preparedness Index 2020-03-14T23:46:55.915000+00:00  
UHJvamVjdDphZTM5NjdjMi04YmVmLTQzNDMtYTllMi02OTE1ZGZhNTk4ZDE= biglocalnews@stanford.edu 2020-03-20T22:05:50.208150+00:00 Data provided via a collaboration between USAFacts and Big Local News. This is an ongoing effort to collect and distribute county-level data on confirmed coronavirus cases and deaths. Please cite USAFacts as the data provider. The three CSV files are the current version and will be updated daily around 8 a.m. The zipped archives are prior versions with a date stamp of when they were uploaded by Big Local News. See README for additional details 1 COVID_USAFacts_county_cases 2020-06-13T15:06:12.638000+00:00  
UHJvamVjdDpiMGVmMjIyYS0zNzE4LTRhZTgtYWJjNC1lNzA3M2M0MDFmZGQ= biglocalnews@stanford.edu 2020-03-18T23:38:54.586268+00:00 This is the CDC's Social Vulnerability Index for 2018. It covers the entire nation at both the Census tract and county level. The index indicates the relative vulnerability of each Census tract or county. Social vulnerability refers to the resilience of communities when confronted by external stresses on human health, stresses such as natural or human-caused disasters, or disease outbreaks. 1 COVID_CDC_SVI 2020-03-19T01:40:59.554000+00:00

CDC Social Vulnerability Index

The Center for Disease Control's Social Vulnerability Index was downloaded by Big Local News on March 18, 2020.

According to the CDC, the index is described as:

Social vulnerability refers to the resilience of communities when confronted by external stresses on human health, stresses such as natural or human-caused disasters, or disease outbreaks. Reducing social vulnerability can decrease both human suffering and economic loss. CDC's Social Vulnerability Index uses 15 U.S. Census variables at the Census tract level to help local officials identify communities that may need support in preparing for hazards; or recovering from disaster.

Big Local News is providing national data files at both the Census tract and county level.

The index indicates the relative vulnerability of each Census tract or county. In national files, this means all tracts/counties are compared against each other. Individual state files where counties/tracts are compared against others within the same state are available at https://svi.cdc.gov/data-and-tools-download.html.

More information on the index can be found at https://svi.cdc.gov/.

The data we are providing is in two different formats - a shapefile for GIS analysis and a CSV for work within a spreadsheet program or other data analysis program.

The files included in this project are as follows:

  • vulnerability_index_paper.pdf: Original study published in the Journal of Homeland Security and Emergency Management, 2011.
  • SVI2018Documentation.pdf: Documentation and data dictionary for the 2018 index, the most recently available.
  • SVI2018_US.csv: 2018 SVI data at the Census tract level in CSV format.
  • SVI2018_US.zip: 2018 SVI data at the Census tract level in shapefile format.
  • SVI2018_US_COUNTY.csv: 2018 SVI data at the county level in CSV format.
  • SVI2018_US_COUNTY.zip: 2018 SVI data at the county level in shapefile format.
UHJvamVjdDpiZGM5NmU1MS1kMzBhLTRlYTctODY4Yi04ZGI4N2RjMzQ1ODI= biglocalnews@stanford.edu 2020-03-29T22:52:52.513905+00:00 Big Local News is collecting and updating WARN (layoff/furlough) notices from state government sites and is processing the data to make it more useable. So far, we are collecting and updating daily or weekly data from 36 states. A federal law -- the WARNA Act (Worker Adjustment and Retraining Notification Act) requires companies with more than 100 employees to give 60 days notice of any layoff, closure or furlough and 17 states have their own similar statutes. This data provides much more granularity into the types of jobs being lost and the companies that are furloughing workers, or that plan to close completely. A key note is that "unforeseen business circumstances" can mean that a company doesn't have to provide 60 days notice. For example, California has lifted that level of notice for its state version, or mini-WARN Act. Nonetheless, even though the advance time limit is flexible, companies still have to submit. Please cite the WARN data as collected by Big Local News. California data can be cited as collected and processed by Stanford journalism student Vanessa Ochavillo. This project includes a general README, a more specific README for California and a more specific file layout README that details the fields by state. Some states are missing but we are working to obtain the data or build scrapers. If you are interested in contributing, please let us know at biglocalnews@stanford.edu. 1 COVID_WARN_Notices 2020-06-13T17:30:37.087000+00:00

WARN data collected by Big Local News

From dol.gov: "The Worker Adjustment and Retraining Notification Act (WARN) protects workers, their families, and communities by requiring employers with 100 or more employees (generally not counting those who have worked less than six months in the last 12 months and those who work an average of less than 20 hours a week) to provide at least 60 calendar days advance written notice of a plant closing and mass layoff affecting 50 or more employees at a single site of employment."

Since COVID-19, some states (including California) have lifted the 60-day advance notice required. Nonetheless, employers still are required to submit the layoff notices.

This WARN (layoff) data was downloaded from different states' websites. Not all states are represented in this data. For states where data was easily scraped, data will be updated daily. Data that must be processed more manually, data will be updated weekly. So far, we have collected data from xx states and expect to process data from at least three other states.

Each file is a state's WARN data as either a raw or processed CSV, or in a few cases as a PDF (pending processing). Processed files indicate that BLN has updated or cleaned fields to be more readable and easier to analyze. Processed files will continue to be added as they become available.

An additional README is available for California where more granular data was available and was processed differently. Files corresponding to this data has been appended with 'expanded' in the file name.

Record layouts for each file are in a WARN_layout.md file.

File manifest

For more information, email biglocalnews@stanford.edu

UHJvamVjdDpkOWVkYTcyNi1iMDc4LTRmZjQtODU2My01ODRhNzIxMDdlOTc= biglocalnews@stanford.edu 2020-03-31T22:29:52.354165+00:00 Big Local News snapshots this data daily from https://github.com/nytimes/covid-19-data. This is time series data for U.S. confirmed cases and deaths down to the county level. There are three CSV files released once a day: national-level data, state-level data and county-level data. Each file contains date, county/state name, FIPS code, number of confirmed cases, number of deaths going back to January 21. All data is gathered by New York Times reporters from state and local health departments or official government announcements. The data is updated once a day in the morning, usually around 8 a.m. EDT, with a full day’s worth of data from the day before. Please cite the New York Times as described in the README. 1 COVID_NYT_COVID_data 2020-05-13T05:15:34.540000+00:00

Coronavirus (Covid-19) Data in the United States

NEW: The data in the counties.csv, states.csv and us.csv now include both confirmed and probable Covid-19 cases and deaths. Because of changes in how states and local health departments are reporting their data, it is no longer possible to report a comprehensive “confirmed-only” dataset. Please see our note for a full explanation of the differences and how probable cases are defined.

<hr>

[ U.S. Data (Raw CSV) | U.S. State-Level Data (Raw CSV) | U.S. County-Level Data (Raw CSV) ]

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.

United States Data

Data on cumulative coronavirus cases and deaths can be found in three files, one for each of these geographic levels: U.S., states and counties.

Each row of data reports cumulative counts based on our best reporting up to the moment we publish an update. We do our best to revise earlier entries in the data when we receive new information. If a county is not listed for a date, then there were zero reported cases and deaths.

State and county files contain FIPS codes, a standard geographic identifier, to make it easier for an analyst to combine this data with other data sets like a map file or population data.

Download all the data or clone this repository by clicking the green "Clone or download" button above.

U.S. National-Level Data

The daily number of cases and deaths nationwide, including states, U.S. territories and the District of Columbia, can be found in the us.csv file. (Raw CSV file here.)

date,cases,deaths 2020-01-21,1,0 ...

State-Level Data

State-level data can be found in the states.csv file. (Raw CSV file here.)

date,state,fips,cases,deaths 2020-01-21,Washington,53,1,0 ...

County-Level Data

County-level data can be found in the counties.csv file. (Raw CSV file here.)

date,county,state,fips,cases,deaths 2020-01-21,Snohomish,Washington,53061,1,0 ...

In some cases, the geographies where cases are reported do not map to standard county boundaries. See the list of geographic exceptions for more detail on these.

Methodology and Definitions

The data is the product of dozens of journalists working across several time zones to monitor news conferences, analyze data releases and seek clarification from public officials on how they categorize cases.

It is also a response to a fragmented American public health system in which overwhelmed public servants at the state, county and territorial level have sometimes struggled to report information accurately, consistently and speedily. On several occasions, officials have corrected information hours or days after first reporting it. At times, cases have disappeared from a local government database, or officials have moved a patient first identified in one state or county to another, often with no explanation. In those instances, which have become more common as the number of cases has grown, our team has made every effort to update the data to reflect the most current, accurate information while ensuring that every known case is counted.

When the information is available, we count patients where they are being treated, not necessarily where they live.

In most instances, the process of recording cases has been straightforward. But because of the patchwork of reporting methods for this data across more than 50 state and territorial governments and hundreds of local health departments, our journalists sometimes had to make difficult interpretations about how to count and record cases.

For those reasons, our data will in some cases not exactly match with the information reported by states and counties. Those differences include these cases: When the federal government arranged flights to the United States for Americans exposed to the coronavirus in China and Japan, our team recorded those cases in the states where the patients subsequently were treated, even though local health departments generally did not. When a resident of Florida died in Los Angeles, we recorded her death as having occurred in California rather than Florida, though officials in Florida counted her case in their own records. And when officials in some states reported new cases without immediately identifying where the patients were being treated, we attempted to add information about their locations later, once it became available.

  • Confirmed Cases

Confirmed cases and deaths are counts of individuals whose coronavirus infections were confirmed by a laboratory test and reported by a federal, state, territorial or local government agency.

The number of cases includes all cases, including those who have since recovered or died.

  • "Probable" Cases and Deaths

Probable cases and deaths count individuals who did not have a confirmed test but were evaluated using criteria developed by states and the federal government.

On April 5, the Council of State and Territorial Epidemiologists Centers advised states to include both confirmed cases, based on laboratory testing, and probable cases, based on specific criteria for symptoms and exposure. The Centers for Disease Control adopted these definitions and national CDC data began including confirmed and probable cases on April 14.

Some governments continue to report only confirmed cases, while others are reporting both confirmed and probable numbers. And there is also another set of governments that are reporting the two types of numbers combined without providing a way to separate the confirmed from the probable.

Please see the Geographic Exceptions section below for more details on specific areas, with the understanding that this changes frequently.

  • Dates

For each date, we show the cumulative number of confirmed cases and deaths as reported that day in that county or state. All cases and deaths are counted on the date they are first announced.

Each date includes all cases and deaths announced that day through midnight Eastern Time. As the West Coast and Hawaii tend to release all of their new data early enough in the day.

  • Declining Counts

In some cases, the number of cases or deaths for a state or county will decline. This can occur when a state or county corrects an error in the number of cases or deaths they've reported in the past, or when a state moves cases from one county to another. When we are able, we will historically revise counts for all impacted dates. In other cases, this will be reflected in a single-day drop in the number of cases or deaths.

  • Counties

In some instances, we report data from multiple counties or other non-county geographies as a single county. For instance, we report a single value for New York City, comprising the cases for New York, Kings, Queens, Bronx and Richmond Counties. In these instances the FIPS code field will be empty. (We may assign FIPS codes to these geographies in the future.) See the list of geographic exceptions.

Cities like St. Louis and Baltimore that are administered separately from an adjacent county of the same name are counted separately.

  • “Unknown” Counties

Many state health departments choose to report cases separately when the patient’s county of residence is unknown or pending determination. In these instances, we record the county name as “Unknown.” As more information about these cases becomes available, the cumulative number of cases in “Unknown” counties may fluctuate.

Sometimes, cases are first reported in one county and then moved to another county. As a result, the cumulative number of cases may change for a given county.

Geographic Exceptions

  • New York

All cases for the five boroughs of New York City (New York, Kings, Queens, Bronx and Richmond counties) are assigned to a single area called New York City. There is a large jump in the number of deaths on April 6th due to switching from data from New York City to data from New York state for deaths. We are not currently including the probable deaths reported by New York City.

For all New York state counties, starting on April 8th we are reporting deaths by place of fatality instead of residence of individual. There were no new deaths reported by the state on April 17th or April 18th.

  • Georgia

Starting April 12th, our case count excludes cases labeled by the state as "Non-Georgia Resident" leading to a one day drop in cases. These cases were previously included as cases with "Unknown" county.

  • Alabama

Alabama's numbers for April 17th contained an error in reporting of lab test results that the state is working to correct. The number of deaths drops on April 23rd for an unknown reason.

  • Kansas City, Mo.

Four counties (Cass, Clay, Jackson and Platte) overlap the municipality of Kansas City, Mo. The cases and deaths that we show for these four counties are only for the portions exclusive of Kansas City. Cases and deaths for Kansas City are reported as their own line.

  • Alameda County, Calif.

Counts for Alameda County include cases and deaths from Berkeley and the Grand Princess cruise ship.

  • Douglas County, Neb.

Counts for Douglas County include cases brought to the state from the Diamond Princess cruise ship.

  • Chicago

All cases and deaths for Chicago are reported as part of Cook County.

  • Guam

Counts for Guam include cases reported from the USS Theodore Roosevelt.

  • Puerto Rico

On April 21st, the territory's health department revised their number of cases downward, saying they had been double counting some coronavirus patients in official reports, leading to a higher number of cases reported than actually confirmed.

Additionally, from approximately April 12th through April 18th, the count of deaths for Puerto Rico include some probable Covid-19 related deaths that were not lab-confirmed. Starting April 19th these have been removed. We will revise the numbers for the 12th to 18th as possible.

Probable Cases and Deaths

  • Colorado

Numbers reflect the combined number of lab-confirmed and probable cases and deaths as reported by the state. On April 25th, the state revised downward the number of deaths after removing "about 29 duplicates" from the number of "probable deaths" included in the total.

  • Idaho

The total cases number includes only lab-confirmed cases, but the deaths number does include the deaths of probable Covid-19 cases.

  • Louisiana

The total cases number and total deaths number include only lab-confirmed cases and deaths. The state appears to be reporting the deaths of probable Covid-19 cases separately from the total number of deaths statewide and in each parish but we are not yet including those cases in our numbers.

  • Ohio

The state reports lab-confirmed and probable cases and deaths separately at the state level but combine lab-confirmed and probable cases and deaths at the county level. Our statewide and county numbers combine both case types.

  • Pennsylvania

The total cases number includes lab-confirmed and probable cases starting around April 16th, but the deaths number does not include probable deaths, except for on April 21st and April 22nd when it does.

  • Virginia

The state reports lab-confirmed and probable cases and deaths separately at the state level but combine lab-confirmed and probable cases and deaths at the county level. Our statewide and county numbers combine both case types.

  • Puerto Rico

Our number of cases for Puerto Rico includes the results of serological cases in their total number of cases, we believe this more closely matches the definition of probable cases than confirmed cases as reported elsewhere. Our number of deaths is only deaths with a confirmed test.

License and Attribution

In general, we are making this data publicly available for broad, noncommercial public use including by medical and public health researchers, policymakers, analysts and local news media.

If you use this data, you must attribute it to “The New York Times” in any publication. If you would like a more expanded description of the data, you could say “Data from The New York Times, based on reports from state and local health agencies.”

If you use it in an online presentation, we would appreciate it if you would link to our U.S. tracking page at https://www.nytimes.com/interactive/2020/us/coronavirus-us-cases.html.

If you use this data, please let us know at covid-data@nytimes.com.

See our LICENSE for the full terms of use for this data.

This license is co-extensive with the Creative Commons Attribution-NonCommercial 4.0 International license, and licensees should refer to that license (CC BY-NC) if they have questions about the scope of the license.

Contact Us

If you have questions about the data or licensing conditions, please contact us at:

covid-data@nytimes.com

Contributors

Mitch Smith, Karen Yourish, Sarah Almukhtar, Keith Collins, Danielle Ivory and Amy Harmon have been leading our U.S. data collection efforts.

Data has also been compiled by Jordan Allen, Jeff Arnold, Aliza Aufrichtig, Mike Baker, Robin Berjon, Matthew Bloch, Nicholas Bogel-Burroughs, Maddie Burakoff, Christopher Calabrese, Andrew Chavez, Robert Chiarito, Carmen Cincotti, Alastair Coote, Matt Craig, John Eligon, Tiff Fehr, Andrew Fischer, Matt Furber, Rich Harris, Lauryn Higgins, Jake Holland, Will Houp, Jon Huang, Danya Issawi, Jacob LaGesse, Hugh Mandeville, Patricia Mazzei, Allison McCann, Jesse McKinley, Miles McKinley, Sarah Mervosh, Andrea Michelson, Blacki Migliozzi, Steven Moity, Richard A. Oppel Jr., Jugal K. Patel, Nina Pavlich, Azi Paybarah, Sean Plambeck, Carrie Price, Scott Reinhard, Thomas Rivas, Michael Robles, Alison Saldanha, Alex Schwartz, Libby Seline, Shelly Seroussi, Rachel Shorey, Anjali Singhvi, Charlie Smart, Ben Smithgall, Steven Speicher, Michael Strickland, Albert Sun, Thu Trinh, Tracey Tully, Maura Turcotte, Miles Watkins, Jeremy White, Josh Williams and Jin Wu.

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CREATE TABLE [projects] (
   [contact] TEXT,
   [createdAt] TEXT,
   [description] TEXT,
   [id] TEXT PRIMARY KEY,
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   [updatedAt] TEXT
, [readme_markdown] TEXT);
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