The dataset summarizes the number and rate of cases, tests and positivity at zip code level over time. Data are summarized as three-week time period.

This dataset is updated every Thursday.

NOTE: This dataset has been retired and marked as historical-only.

Only Chicago residents are included based on the home ZIP Code as provided by the medical provider. If a ZIP was missing or was not valid, it is displayed as "Unknown".

Cases with a positive molecular (PCR) or antigen test are included in this dataset. Cases are counted based on the week the test specimen was collected. For privacy reasons, until a ZIP Code reaches five cumulative cases, both the weekly and cumulative case counts will be blank. Therefore, summing the “Cases - Weekly” column is not a reliable way to determine case totals. Deaths are those that have occurred among cases based on the week of death.

For tests, each test is counted once, based on the week the test specimen was collected. Tests performed prior to 3/1/2020 are not included. Test counts include multiple tests for the same person (a change made on 10/29/2020). PCR and antigen tests reported to Chicago Department of Public Health (CDPH) through electronic lab reporting are included. Electronic lab reporting has taken time to onboard and testing availability has shifted over time, so these counts are likely an underestimate of community infection.

The “Percent Tested Positive” columns are calculated by dividing the number of positive tests by the number of total tests . Because of the data limitations for the Tests columns, such as persons being tested multiple times as a requirement for employment, these percentages may vary in either direction from the actual disease prevalence in the ZIP Code.

All data are provisional and subject to change. Information is updated as additional details are received.

To compare ZIP Codes to Chicago Community Areas, please see http://data.cmap.illinois.gov/opendata/uploads/CKAN/NONCENSUS/ADMINISTRATIVE_POLITICAL_BOUNDARIES/CCAzip.pdf. Both ZIP Codes and Community Areas are also geographic datasets on this data portal.

Data Source: Illinois National Electronic Disease Surveillance System, Cook County Medical Examiner’s Office, Illinois Vital Records, American Community Survey (2018)

This dataset contains information on antibody testing for COVID-19: the number of people who received a test, the number of people with positive results, the percentage of people tested who tested positive, and the rate of testing per 100,000 people, stratified by modified ZIP Code Tabulation Area (ZCTA) of residence. Modified ZCTA reflects the first non-missing address within NYC for each person reported with an antibody test result. This unit of geography is similar to ZIP codes but combines census blocks with smaller populations to allow more stable estimates of population size for rate calculation. It can be challenging to map data that are reported by ZIP Code. A ZIP Code doesn’t refer to an area, but rather a collection of points that make up a mail delivery route. Furthermore, there are some buildings that have their own ZIP Code, and some non-residential areas with ZIP Codes. To deal with the challenges of ZIP Codes, the Health Department uses ZCTAs which solidify ZIP codes into units of area. Often, data reported by ZIP code are actually mapped by ZCTA. The ZCTA geography was developed by the U.S. Census Bureau. These data can also be accessed here: https://github.com/nychealth/coronavirus-data/blob/master/totals/antibody-by-modzcta.csv Exposure to COVID-19 can be detected by measuring antibodies to the disease in a person’s blood, which can indicate that a person may have had an immune response to the virus. Antibodies are proteins produced by the body’s immune system that can be found in the blood. People can test positive for antibodies after they have been exposed, sometimes when they no longer test positive for the virus itself. It is important to note that the science around COVID-19 antibody tests is evolving rapidly and there is still much uncertainty about what individual antibody test results mean for a single person and what population-level antibody test results mean for understanding the epidemiology of COVID-19 at a population level. These data only provide information on people tested. People receiving an antibody test do not reflect all people in New York City; therefore, these data may not reflect antibody prevalence among all New Yorkers. Increasing instances of screening programs further impact the generalizability of these data, as screening programs influence who and how many people are tested over time. Examples of screening programs in NYC include: employers screening their workers (e.g., hospitals), and long-term care facilities screening their residents. In addition, there may be potential biases toward people receiving an antibody test who have a positive result because people who were previously ill are preferentially seeking testing, in addition to the testing of persons with higher exposure (e.g., health care workers, first responders) Rates were calculated using interpolated intercensal population estimates updated in 2019. These rates differ from previously reported rates based on the 2000 Census or previous versions of population estimates. The Health Department produced these population estimates based on estimates from the U.S. Census Bureau and NYC Department of City Planning. Antibody tests are categorized based on the date of specimen collection and are aggregated by full weeks starting each Sunday and ending on Saturday. For example, a person whose blood was collected for antibody testing on Wednesday, May 6 would be categorized as tested during the week ending May 9. A person tested twice in one week would only be counted once in that week. This dataset includes testing data beginning April 5, 2020. Data are updated daily, and the dataset preserves historical records and source data changes, so each extract date reflects the current copy of the data as of that date. For example, an extract date of 11/04/2020 and extract date of 11/03/2020 will both contain all records as they were as of that extract date. Without filtering or grouping by extract date, an analysis wi

This dataset has been retired as of February 17, 2023. This dataset will be kept for historical purposes, but will no longer be updated. Similar data are available on the state’s open data portal: https://data.chhs.ca.gov/dataset/covid-19-time-series-metrics-by-county-and-state.

A. DATASET DESCRIPTION This dataset contains COVID-19 positive confirmed cases aggregated by several different geographic areas and by day. COVID-19 cases are mapped to the residence of the individual and shown on the date the positive test was collected. In addition, 2019 American Community Survey (ACS) 5-year population estimates are included to calculate the cumulative rate per 10,000 residents.

Dataset covers cases going back to March 18th, 2020 when the first person in Marin County tested positive for COVID-19. This data may not be immediately available for recently reported cases and data will change to reflect as information becomes available. Data updated daily.

COVID-19 case data undergo quality assurance and other data verification processes and are continually updated to maximize completeness and accuracy of information. This means data may change for previous days as information is updated.

Geographic areas summarized are: 1. City, Town, or Community Area 2. Census Tracts 3. Census ZIP Code Tabulation Areas (ZCTAs)

B. HOW THE DATASET IS CREATED Addresses from the COVID-19 case data are geocoded by Marin County HHS. Those addresses are spatially joined to the geographic areas. Counts are generated based on the number of address points that match each geographic area for a given date.

The 2019 ACS estimates for population provided by the Census are used to create a cumulative rate which is equal to ([cumulative count up to that date] / [acs_population]) * 10000) representing the number of total cases per 10,000 residents (as of the specified date).

C. UPDATE PROCESS Geographic analysis is scripted by Marin HHS staff and synced to this dataset each day.

D. HOW TO USE THIS DATASET This dataset can be used to track the spread of COVID-19 throughout Marin County in a variety of geographic areas. Note that the new cases column in the data represents the number of new cases confirmed in a certain area on the specified day, while the cumulative cases column is the cumulative total of cases in a certain area as of the specified date.

Privacy rules in effect To protect privacy, certain rules are in effect: 1. Any area with a cumulative case count less than 10 are dropped for all days the cumulative count was less than 10. These will be null values. For example if a zip code did not have 10 cumulative cases until June 1, 2020 that location will not be included in the dataset until June 1. 2. Once an area has a cumulative case count of 10 or greater, that area will have a new row of case data every day following. 3. 3. Cases are dropped altogether for areas where acs_population < 1000. Some adjacent geographic areas may be combined until the ACS population exceeds 1,000 to still provide information for these regions.

Note: 14-day case rate or 30-day case rate where the counts are lower than 20 may be unstable. We advise caution in interpreting rates at these small numbers.

A note on Census ZIP Code Tabulation Areas (ZCTAs) ZIP Code Tabulation Areas are special boundaries created by the U.S. Census based on ZIP Codes developed by the USPS. They are not, however, the same thing. ZCTAs are areal representations of routes.

Cumulation of the weekly release of COVID-19 data for Maricopa County by Zip Code. Includes PCR Test Percent Positivity as viewed on the Maricopa County School Reopening Dashboard map by week. For more information about the data, visit: https://www.maricopa.gov/5594/School-Metrics.

A. SUMMARY This dataset contains COVID-19 positive confirmed cases aggregated by several different geographic areas and by day. COVID-19 cases are mapped to the residence of the individual and shown on the date the positive test was collected. In addition, 2016-2020 American Community Survey (ACS) population estimates are included to calculate the cumulative rate per 10,000 residents.

Dataset covers cases going back to 3/2/2020 when testing began. This data may not be immediately available for recently reported cases and data will change to reflect as information becomes available. Data updated daily.

Geographic areas summarized are: 1. Analysis Neighborhoods 2. Census Tracts 3. Census Zip Code Tabulation Areas

B. HOW THE DATASET IS CREATED Addresses from the COVID-19 case data are geocoded by the San Francisco Department of Public Health (SFDPH). Those addresses are spatially joined to the geographic areas. Counts are generated based on the number of address points that match each geographic area for a given date.

The 2016-2020 American Community Survey (ACS) population estimates provided by the Census are used to create a cumulative rate which is equal to ([cumulative count up to that date] / [acs_population]) * 10000) representing the number of total cases per 10,000 residents (as of the specified date).

COVID-19 case data undergo quality assurance and other data verification processes and are continually updated to maximize completeness and accuracy of information. This means data may change for previous days as information is updated.

C. UPDATE PROCESS Geographic analysis is scripted by SFDPH staff and synced to this dataset daily at 05:00 Pacific Time.

D. HOW TO USE THIS DATASET San Francisco population estimates for geographic regions can be found in a view based on the San Francisco Population and Demographic Census dataset. These population estimates are from the 2016-2020 5-year American Community Survey (ACS).

This dataset can be used to track the spread of COVID-19 throughout the city, in a variety of geographic areas. Note that the new cases column in the data represents the number of new cases confirmed in a certain area on the specified day, while the cumulative cases column is the cumulative total of cases in a certain area as of the specified date.

Privacy rules in effect To protect privacy, certain rules are in effect: 1. Any area with a cumulative case count less than 10 are dropped for all days the cumulative count was less than 10. These will be null values. 2. Once an area has a cumulative case count of 10 or greater, that area will have a new row of case data every day following. 3. Cases are dropped altogether for areas where acs_population < 1000 4. Deaths data are not included in this dataset for privacy reasons. The low COVID-19 death rate in San Francisco, along with other publicly available information on deaths, means that deaths data by geography and day is too granular and potentially risky. Read more in our privacy guidelines

Rate suppression in effect where counts lower than 20 Rates are not calculated unless the cumulative case count is greater than or equal to 20. Rates are generally unstable at small numbers, so we avoid calculating them directly. We advise you to apply the same approach as this is best practice in epidemiology.

A note on Census ZIP Code Tabulation Areas (ZCTAs) ZIP Code Tabulation Areas are special boundaries created by the U.S. Census based on ZIP Codes developed by the USPS. They are not, however, the same thing. ZCTAs are areal representations of routes. Read how the Census develops ZCTAs on their website.

Rows included for Citywide case counts Rows are included for the Citywide case counts and incidence rate every day. These Citywide rows can be used for comparisons. Citywide will capture all cases regardless of address quality. While some cases cannot be mapped to sub-areas like Census Tracts, ongoing data quality efforts result in improved mapping on a rolling bases.

Related dataset See the dataset of the most recent cumulative counts for all geographic areas here: https://data.sfgov.org/COVID-19/COVID-19-Cases-and-Deaths-Summarized-by-Geography/tpyr-dvnc

E. CHANGE LOG

• 9/11/2023 - data on COVID-19 cases by geography over time are no longer being updated. This data is currently through 9/6/2023 and will not include any new data after this date.
• 4/6/2023 - the State implemented system updates to improve the integrity of historical data.
• 2/21/2023 - system updates to improve reliability and accuracy of cases data were implemented.
• 1/31/2023 - updated “acs_population” column to reflect the 2020 Census Bureau American Community Survey (ACS) San Francisco Population estimates.
• 1/31/2023 - implemented system updates to streamline and improve our geo-coded data, resulting in small shifts in our case data by geography.
• 1/31/2023 - renamed column “last_updated_at” to “data_as_of”.
• 1/31/2023 - removed the “multipolygon” column. To access the multipolygon geometry column for each geography unit, refer to COVID-19 Cases and Deaths Summarized by Geography.
• 1/22/2022 - system updates to improve timeliness and accuracy of cases and deaths data were implemented.
• 4/16/2021 - dataset updated to refresh with a five-day data lag.

A. SUMMARY Medical provider confirmed COVID-19 cases and confirmed COVID-19 related deaths in San Francisco, CA aggregated by several different geographic areas and normalized by 2016-2020 American Community Survey (ACS) 5-year estimates for population data to calculate rate per 10,000 residents. On September 12, 2021, a new case definition of COVID-19 was introduced that includes criteria for enumerating new infections after previous probable or confirmed infections (also known as reinfections). A reinfection is defined as a confirmed positive PCR lab test more than 90 days after a positive PCR or antigen test. The first reinfection case was identified on December 7, 2021. Cases and deaths are both mapped to the residence of the individual, not to where they were infected or died. For example, if one was infected in San Francisco at work but lives in the East Bay, those are not counted as SF Cases or if one dies in Zuckerberg San Francisco General but is from another county, that is also not counted in this dataset. Dataset is cumulative and covers cases going back to 3/2/2020 when testing began. Geographic areas summarized are: 1. Analysis Neighborhoods 2. Census Tracts 3. Census Zip Code Tabulation Areas B. HOW THE DATASET IS CREATED Addresses from medical data are geocoded by the San Francisco Department of Public Health (SFDPH). Those addresses are spatially joined to the geographic areas. Counts are generated based on the number of address points that match each geographic area. The 2016-2020 American Community Survey (ACS) population estimates provided by the Census are used to create a rate which is equal to ([count] / [acs_population]) * 10000) representing the number of cases per 10,000 residents. C. UPDATE PROCESS Geographic analysis is scripted by SFDPH staff and synced to this dataset daily at 7:30 Pacific Time. D. HOW TO USE THIS DATASET San Francisco population estimates for geographic regions can be found in a view based on the San Francisco Population and Demographic Census dataset. These population estimates are from the 2016-2020 5-year American Community Survey (ACS). Privacy rules in effect To protect privacy, certain rules are in effect: 1. Case counts greater than 0 and less than 10 are dropped - these will be null (blank) values 2. Death counts greater than 0 and less than 10 are dropped - these will be null (blank) values 3. Cases and deaths dropped altogether for areas where acs_population < 1000 Rate suppression in effect where counts lower than 20 Rates are not calculated unless the case count is greater than or equal to 20. Rates are generally unstable at small numbers, so we avoid calculating them directly. We advise you to apply the same approach as this is best practice in epidemiology. A note on Census ZIP Code Tabulation Areas (ZCTAs) ZIP Code Tabulation Areas are special boundaries created by the U.S. Census based on ZIP Codes developed by the USPS. They are not, however, the same thing. ZCTAs are areal representations of routes. Read how the Census develops ZCTAs on their website. Row included for Citywide case counts, incidence rate, and deaths A single row is included that has the Citywide case counts and incidence rate. This can be used for comparisons. Citywide will capture all cases regardless of address quality. While some cases cannot be mapped to sub-areas like Census Tracts, ongo

Cumulation of the weekly release of COVID-19 data for Maricopa County by City. Includes PCR Test Percent Positivity as viewed on the Maricopa County School Reopening Dashboard map by week. For more information about the data, visit: https://www.maricopa.gov/5594/School-Metrics.

This dataset contains information on antibody testing for COVID-19: the number of people who received a test, the number of people with positive results, the percentage of people tested who tested positive, and the rate of testing per 100,000 people, stratified by ZIP Code Tabulation Area (ZCTA) neighborhood poverty group. These data can also be accessed here: https://github.com/nychealth/coronavirus-data/blob/master/totals/antibody-by-poverty.csv

Exposure to COVID-19 can be detected by measuring antibodies to the disease in a person’s blood, which can indicate that a person may have had an immune response to the virus. Antibodies are proteins produced by the body’s immune system that can be found in the blood. People can test positive for antibodies after they have been exposed, sometimes when they no longer test positive for the virus itself. It is important to note that the science around COVID-19 antibody tests is evolving rapidly and there is still much uncertainty about what individual antibody test results mean for a single person and what population-level antibody test results mean for understanding the epidemiology of COVID-19 at a population level.

These data only provide information on people tested. People receiving an antibody test do not reflect all people in New York City; therefore, these data may not reflect antibody prevalence among all New Yorkers. Increasing instances of screening programs further impact the generalizability of these data, as screening programs influence who and how many people are tested over time. Examples of screening programs in NYC include: employers screening their workers (e.g., hospitals), and long-term care facilities screening their residents.

In addition, there may be potential biases toward people receiving an antibody test who have a positive result because people who were previously ill are preferentially seeking testing, in addition to the testing of persons with higher exposure (e.g., health care workers, first responders.)

Neighborhood-level poverty groups were classified in a manner consistent with Health Department practices to describe and monitor disparities in health in NYC. Neighborhood poverty measures are defined as the percentage of people earning below the Federal Poverty Threshold (FPT) within a ZCTA. The standard cut-points for defining categories of neighborhood-level poverty in NYC are: • Low: <10% of residents in ZCTA living below the FPT • Medium: 10% to <20% • High: 20% to <30% • Very high: ≥30% residents living below the FPT The ZCTAs used for classification reflect the first non-missing address within NYC for each person reported with an antibody test result.

Rates were calculated using interpolated intercensal population estimates updated in 2019. These rates differ from previously reported rates based on the 2000 Census or previous versions of population estimates. The Health Department produced these population estimates based on estimates from the U.S. Census Bureau and NYC Department of City Planning. Rates for poverty were calculated using direct standardization for age at diagnosis and weighting by the US 2000 standard population. Antibody tests are categorized based on the date of specimen collection and are aggregated by full weeks starting each Sunday and ending on Saturday. For example, a person whose blood was collected for antibody testing on Wednesday, May 6 would be categorized as tested during the week ending May 9. A person tested twice in one week would only be counted once in that week. This dataset includes testing data beginning April 5, 2020.

Data are updated daily, and the dataset preserves historical records and source data changes, so each extract date reflects the current copy of the data as of that date. For example, an extract date of 11/04/2020 and extract date of 11/03/2020 will both contain all records as they were as of that extract date. Without filtering or grouping by extract date, an analysis will almost certainly be miscalculating or counting the same values multiple times. To analyze the most current data, only use the latest extract date. Antibody tests that are missing dates are not included in the dataset; as dates are identified, these events are added. Lags between occurrence and report of cases and tests can be assessed by comparing counts and rates across multiple data extract dates.

For further details, visit: • https://www1.nyc.gov/site/doh/covid/covid-19-data.page • https://github.com/nychealth/coronavirus-data • https://data.cityofnewyork.us/Health/Modified-Zip-Code-Tabulation-Areas-MODZCTA-/pri4-ifjk

Associations with SARS-CoV-2 positivity.

Association of ZIP code characteristics with the percent of residents aged 5 years and older that are vaccinated for COVID-19 and/or have had a COVID-19 booster shot.

This is the place to look for important information about how to use this dataset, so please expand this box and read on!

This is the source data for some of the metrics available at https://www.chicago.gov/city/en/sites/covid-19/home/latest-data.html.

For all datasets related to COVID-19, see https://data.cityofchicago.org/browse?limitTo=datasets&sortBy=alpha&tags=covid-19.

Only Chicago residents are included based on the home ZIP Code as provided by the medical provider. If a ZIP was missing or was not valid, it is displayed as "Unknown".

Confirmed cases are counted based on the week the test specimen was collected. For privacy reasons, until a ZIP Code reaches five cumulative cases, both the weekly and cumulative case counts will be blank. Therefore, summing the “Cases - Weekly” column is not a reliable way to determine case totals. Deaths are those that have occurred among confirmed cases based on the week of death.

For tests, each individual is counted once, based on the week the test specimen was collected. Tests performed prior to 3/1/2020 are not included. Test counts do not include multiple tests for the same person or some negative tests not reported to CDPH.

The “Percent Tested Positive” columns are calculated by dividing the corresponding Cases and Tests columns. Because of the data limitations for the Tests columns, as well as strict criteria for performing COVID-19 tests, these percentages may vary in either direction from the actual disease prevalence in the ZIP Code. Of particular note, these rates do not represent population-level disease surveillance.

Population counts are from the 2010 Decennial Census.

All data are provisional and subject to change. Information is updated as additional details are received.

To compare ZIP Codes to Chicago Community Areas, please see http://data.cmap.illinois.gov/opendata/uploads/CKAN/NONCENSUS/ADMINISTRATIVE_POLITICAL_BOUNDARIES/CCAzip.pdf. Both ZIP Codes and Community Areas are also geographic datasets on this data portal.

Data Source: Illinois National Electronic Disease Surveillance System, Cook County Medical Examiner’s Office, Illinois Vital Records

This item has been archived. It is no longer being updated.For current COVID-19 cases data updates, please see the COVID-19 Cases Per 100,000 by Zip Code dashboard, which shows the COVID-19 case rate per 100,000 population by week for each zip code and is supported by the weekly release of data from the Maricopa County Department of Public Health (MCDPH) https://data.tempe.gov/datasets/covid-19-case-indicators/explore.--------As of 3/2/2022 the Arizona Department of Health Services has shifted to a weekly update schedule. We've adjusted our process to update every Wednesday afternoon.This table provides a weekly log of confirmed COVID-19 cases by Zip Code. Data are provided by the Arizona Department of Health Services (ADHS). Data Source: Arizona Department of Health Services (AZDHS) daily COVID-19 report by zip code (https://adhsgis.maps.arcgis.com/apps/opsdashboard/index.html#/84b7f701060641ca8bd9ea0717790906). Daily Change is calculated by taking the current day’s case value for a given Postal Code and subtracting the prior day’s value. This resulting value is the Daily Change. Based on reporting from ADHS Daily Change may be a positive or negative number or 0 if no change has been reported. Moving Average is calculated by summing the current day’s case count with the prior 6 days’ cases for a given Postal Code and dividing by 7.Arizona Department of Health Services (AZDHS) data are scheduled for daily updates at 9:00 AM (COVID-19 cases) and 12:00 PM (COVID-19 vaccinations), but the times when the AZDHS releases that days COVID-19 cases and vaccinations may vary. City of Tempe data are updated each afternoon at 3:00 PM to allow for possible AZDHS delays. When there are AZDHS delays in updating the daily data, dashboard data updates may be delayed by 24 hours. The charts and daily values list can be used to confirm the date of the most recent counts on the COVID-19 cases and vaccinations dashboards. If data are not released by the time of the scheduled daily dashboard refresh, that day's values may appear on the dashboard as an addition to the next day's value.Additional InformationSource: Arizona Department of Health Services (AZDHS) daily COVID-19 report by zip code (https://adhsgis.maps.arcgis.com/apps/opsdashboard/index.html#/84b7f701060641ca8bd9ea0717790906)Contact (author): n/aContact E-Mail (author): n/aContact (maintainer): City of Tempe Open Data TeamContact E-Mail (maintainer): data@tempe.govData Source Type: TablePreparation Method: Data are exposed via ArcGIS Server and its REST API.Publish Frequency: DailyPublish Method: Data are downloaded each afternoon once ADHS updates its public API. Data are transformed and appended to a table in Tempe’s Enterprise GIS.Data Dictionary

Association of ZIP code characteristics with percent vaccinated and boosted, by age.

This dataset compiles daily snapshots of publicly reported data on 2019 Novel Coronavirus (COVID-19) testing in Ontario.

Learn how the Government of Ontario is helping to keep Ontarians safe during the 2019 Novel Coronavirus outbreak.

Data includes:

• approximation of onset date
• age group
• patient gender
• case acquisition information
• patient outcome
• reporting Public Health Unit (PHU)
• postal code, website, longitude, and latitude of PHU

This dataset is subject to change. Please review the daily epidemiologic summaries for information on variables, methodology, and technical considerations.

Additional information

This data is no longer available on this page. Information about COVID-19, and other respiratory viruses, is available through Public Health Ontario’s “Ontario Respiratory Virus Tool".

On November 30, 2023 the count of COVID-19 deaths was updated to include missing historical deaths from January 15, 2020 to March 31, 2023. This impacts data captured in the column ‘Outcome1’.

Due to changes in data availability, the following variables will be removed from this file, effective Thursday April 13, 2023: ‘Case_AcquisitionInfo’, ‘Outbreak_Related’. Also due to changes in data availability, the variable ‘Outcome1’ will be equal to ‘Fatal’ (deaths due to COVID-19) or blank (all other cases)

The methodology used to count COVID-19 deaths has changed to exclude deaths not caused by COVID. This impacts data captured in the column ‘‘Outcome1’ starting with data posted to the catalogue on March 11, 2022.

CCM is a dynamic disease reporting system which allows ongoing update to data previously entered. As a result, data extracted from CCM represents a snapshot at the time of extraction and may differ from previous or subsequent results. Public Health Units continually clean up COVID-19 data, correcting for missing or overcounted cases and deaths. These corrections can result in data spikes and current totals being different from previously reported cases and deaths. Observed trends over time should be interpreted with caution for the most recent period due to reporting and/or data entry lags.

Related dataset(s)

• Status of COVID-19 cases in Ontario

NYC Coronavirus (COVID-19) data

This repository contains data on coronavirus (COVID-19) in New York City (NYC), updated daily. Data are assembled by the NYC Department of Health and Mental Hygiene (DOHMH) Incident Command System for COVID-19 Response (Surveillance and Epidemiology Branch in collaboration with Public Information Office Branch). You can view these data on the Department of Health's website. Note that data are being collected in real-time and are preliminary and subject to change as COVID-19 response continues.

Files summary.csv This file contains summary information, including when the dataset was "cut" - the cut-off date and time for data included in this update.

Estimated hospitalization counts reflect the total number of people ever admitted to a hospital, not currently admitted.

case-hosp-death.csv This file includes daily counts of new confirmed cases, hospitalizations, and deaths.

Cases are by date of diagnosis Hospitalizations are by date of admission Deaths are by date of death Because of delays in reporting, the most recent data may be incomplete. Data shown currently will be updated in the future as new cases, hospitalizations, and deaths are reported.

boro.csv This contains rates of confirmed cases, by NYC borough of residence. Rates are:

Cumulative since the start of the outbreak Age adjusted according to the US 2000 standard population Per 100,000 people in the borough by-age.csv This contains age-specific rates of confirmed cases, hospitalizations, and deaths.

by-sex.csv This contains rates of confirmed cases, hospitalizations, and deaths.

testing.csv This file includes counts of New York City residents with specimens collected for SARS-CoV-2 testing by day, the subsets who tested positive as confirmed COVID-19 cases, were ever hospitalized, and who died, as of the date of extraction from the NYC Health Department's disease surveillance database. For each date of extraction, results for all specimen collection dates are appended to the bottom of the dataset. Lags between specimen collection date and report dates of cases, hospitalizations, and deaths can be assessed by comparing counts for the same specimen collection date across multiple data extract dates.

tests-by-zcta.csv This file includes the cumulative count of New York City residents by ZIP code of residence who:

Were ever tested for COVID-19 (SARS-CoV-2) Tested positive The cumulative counts are as of the date of extraction from the NYC Health Department's disease surveillance database. Technical Notes This section may change as data and documentation are updated.

Estimated number of COVID-19 patients ever hospitalized At this time, NYC DOHMH does not have the ability to robustly quantify the number of people currently admitted to a hospital given intense resource and time constraints on hospital reporting systems. Therefore, we have estimated the number of individuals diagnosed with COVID-19 who have ever been hospitalized by matching the list of key fields from known cases that are reported by laboratories to the NYC DOHMH Bureau of Communicable Disease surveillance database to other sources of hospital admission information. These other sources include:

The NYC DOHMH syndromic surveillance database that tracks daily hospital admissions from all 53 emergency departments across NYC The New York State Department of Health Hospital Emergency Response Data System (HERDS). Rates per 100,000 people Annual citywide, borough-specific, and demographic specific intercensal population estimates from 2018 were developed by NYC DOHMH on the basis of the US Census Bureau’s Population Estimates Program, as of November 2019.

Rates of cases at the borough-level were calculated using direct standardization for age at diagnosis and weighting by the US 2000 standard population.

https://github.com/nychealth/coronavirus-data/blob/master/README.md

Updated: As of 7/3/2021 the Arizona Department of Health Services is no longer updated its vaccination data. This item has been deprecated as a result.This table provides a daily log of confirmed COVID-19 vaccinations by Zip Code for the state of Arizona. Data are provided by the Arizona Department of Health Services (ADHS). Data Source: Arizona Department of Health Services (AZDHS) daily COVID-19 vaccinations report by zip code (https://experience.arcgis.com/experience/bcf70a0f5cac4262a411166dbcac9053). Daily Change is calculated by taking the current day’s vaccination value for a given Postal Code and subtracting the prior day’s value. This resulting value is the Daily Change. Based on reporting from ADHS Daily Change may be a positive or negative number or 0 if no change has been reported. Arizona Department of Health Services (AZDHS) data are scheduled for daily updates at 9:00 AM (COVID-19 cases) and 12:00 PM (COVID-19 vaccinations), but the times when the AZDHS releases that days COVID-19 cases and vaccinations may vary. City of Tempe data are updated each afternoon at 3:00 PM to allow for possible AZDHS delays. When there are AZDHS delays in updating the daily data, dashboard data updates may be delayed by 24 hours. The charts and daily values list can be used to confirm the date of the most recent counts on the COVID-19 cases and vaccinations dashboards. If data are not released by the time of the scheduled daily dashboard refresh, that day's values may appear on the dashboard as an addition to the next day's value.---------------------------------------------------Please also see the following items for up-to-date COVID-19 vaccination data:COVID-19 Vaccination Rates by Zip Code (Maricopa County)https://data.tempe.gov/datasets/covid-19-vaccination-rates-by-zip-code-maricopa-county/exploreCOVID-19 Vaccination Rates by City (Maricopa County)https://data.tempe.gov/datasets/covid-19-vaccination-rates-by-city-maricopa-county/explore ---------------------------------------------------Additional InformationSource: Arizona Department of Health Services (AZDHS) daily COVID-19 vaccinations report by zip code (https://experience.arcgis.com/experience/bcf70a0f5cac4262a411166dbcac9053)Contact (author): n/aContact E-Mail (author): n/aContact (maintainer): City of Tempe Open Data TeamContact E-Mail (maintainer): data@tempe.govData Source Type: TablePreparation Method: Data are exposed via ArcGIS Server and its REST API.Publish Frequency: DailyPublish Method: Data are downloaded each afternoon once ADHS updates its public API. Data are transformed and appended to a table in Tempe’s Enterprise GIS.Data Dictionary

For English, see below As of 1 January 2023, RIVM will no longer collect additional information. As a result, from January 1, 2023, we will no longer report data on infections among people over 70 living at home . File description: - This file contains the following numbers: (number of newly reported) positively tested individuals aged 70 and older living at home*, by safety region, per date of the positive test result. - (number of newly reported) deceased individuals aged 70 and older living at home who tested positive*, by safety region, by date on which the patient died. The numbers concern COVID-19 reports since the registration of the (residential) institution in OSIRIS with effect from questionnaire 5 (01-07-2020). * For reports from 01-07-2020, it is recorded whether the patient lives in an institution. Reports from 01-07-2020 are regarded as individuals aged 70 and older living at home if, according to the information known to the GGD, they: • Do not live in an institution AND • Are aged 70 or older AND • The person is not employed and is not a healthcare worker Persons whose residential facility/institution is not listed can still be excluded as individuals aged 70 and older living at home if they: • Can be linked to a known location of a disability care institution or nursing home on the basis of their 6-digit zip code OR • Have 'Disabled care institution' or 'Nursing home' as the location of the contamination mentioned. OR • Based on the content of free text fields, can be linked to a disability care institution or nursing home. The file is structured as follows: A set of records per date of with for each date: • A record for each security region (including 'Unknown') in the Netherlands, even if there are no reports for the relevant security region. The numbers are then 0 (zero). • Security region is unknown when a record cannot be assigned to one unique security region. A date 01-01-1900 is also included in this file for statistics whose associated date is unknown. The following describes how the variables are defined. Description of the variables: Version: Version number of the dataset. This version number is adjusted (+1) when the content of the dataset is structurally changed (so not the daily update or a correction at record level. The corresponding metadata in RIVMdata (https://data.rivm.nl) is also changed. Version 2 update (January 25, 2022): • An updated list of known nursing or care home locations and private residential care centers was received from the umbrella organization Patient Federation of the Netherlands on 03-12-2021. taken to determine whether individuals live in an institution Version 3 update (February 8, 2022) • From February 8, 2022, positive SARS-CoV-2 test results will be reported directly from CoronIT to RIVM. such as Testing for Access) and healthcare institutions (such as hospitals, nursing homes and general practitioners) that enter their positive SARS-CoV-2 test results via the Reporting Portal of GGD GHOR directly to RIVM. Reports that are part of the source and contact investigation sample and positive SARS-CoV-2 test results from healthcare institutions that are reported to the GGD via healthcare email are reported to RIVM via HPZone. From 8 February, the date of the positive test result is used and no longer the date of notification to the GGD. Version 4 update (March 24, 2022): • In version 4 of this dataset, records have been compiled according to the municipality reclassification of March 24, 2022. See description of the variable security_region_code for more information. Version 5 update (August 2, 2022): • The classification of persons aged 70 years and parents living independently has not been applied to reports that have only been received by RIVM since February 8, 2022 via an alternative reporting route. From 8 February to 1 August 2022, the number of reports from independently living persons aged 70 and parents was therefore underestimated by approximately 14%. As of August 2, 2022, this format will be retroactively updated. Version 6 update (September 1, 2022): - From September 1, 2022, the data will no longer be updated every working day, but on Tuesdays and Fridays. The data is retroactively updated on these days for the other days. - As of September 1, 2022, this dataset is split into two parts. The first part contains the dates from the start of the pandemic to October 3, 2021 (week 39) and contains "tm" in the file name. This data will no longer be updated. The second part contains the data from October 4, 2021 (week 40) and is updated every Tuesday and Friday. Date_of_report: Date and time on which the data file was created by RIVM. Date_of_statistic_reported: The date used for reporting the 70plus statistic living at home. This can be different for each reported statistic, namely: • For [Total_cases_reported] this is the date of the positive test result. • For [Total_deceased_reported] this is the date on which the patients died. Security_region_code: Security region code. The code of the security region based on the patient's place of residence. If the place of residence is not known, the safety region is based on the GGD that submitted the report, except for the Central and West Brabant and Brabant-Noord safety regions, since the GGD and safety region are not comparable for these regions. See also: https://www.cbs.nl/nl-nl/figures/detail/84721ENG?q=Veiliteiten From March 24, 2022, this file has been compiled according to the municipality classification of March 24, 2022. The municipality of Weesp has been merged into the municipality of Amsterdam . With this division, the Gooi- en Vechtstreek safety region has become smaller and the Amsterdam-Amstelland safety region larger; GGD Amsterdam has become larger and GGD Gooi- en Vechtstreek has become smaller (Municipal division on 1 January 2022 (cbs.nl). Security_region_name: Security region name. Security region name is based on the Security Region Code. See also: https://www.rijksoverheid.nl /topics/safety-regions-and-crisis-management/safety-regions Total_cases_reported: The number of new COVID-19 infected over-70s living at home reported to the GGD on [Date_of_statistic_reported].The actual number of COVID-19 infected over-70s living at home is higher than the number of reports in surveillance, because not everyone with a possible infection is tested. In addition, it is not known for every report whether this concerns a person over 70 living at home. Date_of_statistic_reported] The actual number of deceased people over 70 living at home who died of COVID-19 is higher than the number of reports in the surveillance, because not all deceased patients are tested and deaths are not legally reportable. Moreover, it is not known for every report whether this concerns a person over 70 living at home. Corrections made to reports in the OSIRIS source system can also lead to corrections in this database. In that case, numbers published by RIVM in the past may deviate from the numbers in this database. This file therefore always contains the numbers based on the most up-to-date data in the OSIRIS source system. The CSV file uses a semicolon as a separator. There are no empty lines in the file. Below are the column names and the types of values ​​in the CSV file: • Version: Consisting of a single whole number (integer). Is always filled for each row. Example: 2. • Date_of_report: Written in format YYYY-MM-DD HH:MM. Is always filled for each row. Example: 2020-10-16 10:00 AM. • Date_of_statistic_reported: Written in format YYYY-MM-DD. Is always filled for each row. Example: 2020-10-09. • Security_region_code: Consisting of 'VR' followed by two digits. Can also be empty if the region is unknown. Example: VR01. • Security_region_name: Consisting of a character string. Is always filled for each row. Example: Central and West Brabant. • Total_cases_reported: Consisting of only whole numbers (integer). Is always filled for each row. Example: 12. • Total_deceased_reported: Consisting of only whole numbers (integer). Is always filled for each row. Example: 8. ---------------------------------------------- ---------------------------------- Covid-19 statistics for persons aged 70 and older living outside an institution, by security region and date As of 1 January 2023, the RIVM will no longer collect additional information. As a result, from January 1, 2023, we will no longer report data on infections among people over 70 living at home. File description: This file contains the following numbers: - Number of newly reported persons aged 70 and older living at home who tested positive*, by security region, by date of the positive test result. - Number of newly reported deceased persons aged 70 and older living at home who tested positive*, by security region, by date on which the patient died. The numbers concern COVID-19 reports since the registration of the (residential) institution in OSIRIS with effect from questionnaire 5 (01-07-2020). * For reports from 01-07-2020, it is recorded whether the patient lives in an institution. For reports from 01-07-2020 persons aged 70 and older are considered to be living at home if, according to the information known to the PHS, they: • were not living in an institution AND • Are aged 70 years or older AND • The person is not employed and is not a healthcare worker Persons whose residential facility/institution is not listed can still be excluded as being an persons aged 70 and older living at home if they: • Based on their 6-digit zip code, can be linked to a known location of a care institution for the disabled or a nursing home OR • Have 'Disability care institution' or 'Nursing home' as the stated location of transmission. OR • Based on the content of free text fields, links can be made to a care institution for the disabled or a nursing home. The file is structured as follows: A set of records by date, with for

Key modeling variables and their availability in the study population.