This dataset is no longer being updated as of 6/30/2022. It is being retained on the Open Data Portal for its potential historical interest.

In November 2020, the City of Cambridge began collecting and analyzing COVID-19 data from municipal wastewater, which can serve as an early indicator of increased COVID-19 infections in the city. The Cambridge Public Health Department and Cambridge Department of Public Works are using technology developed by Biobot, a Cambridge based company, and partnering with the Massachusetts Water Resources Authority (MWRA). This Cambridge wastewater surveillance initiative is funded through a $175,000 appropriation from the Cambridge City Council.

This dataset indicates the presence of the COVID-19 virus (measured as viral RNA particles from the novel coronavirus per ml) in municipal wastewater. The Cambridge site data here were collected as a 24-hour composite sample, which is taken weekly. The MWRA site data ere were collected as a 24-hour composite sample, which is taken daily. MWRA and Cambridge data are listed here in a single table.

An interactive graph of this data is available here: https://cityofcambridge.shinyapps.io/COVID19/?tab=wastewater

All areas within the City of Cambridge are captured across four separate catchment areas (or sewersheds) as indicated on the map viewable here: https://cityofcambridge.shinyapps.io/COVID19/_w_484790f7/BioBot_Sites.png. The North and West Cambridge sample also includes nearly all of Belmont and very small areas of Arlington and Somerville (light yellow). The remaining collection sites are entirely -- or almost entirely -- drawn from Cambridge households and workplaces.

Data are corrected for wastewater flow rate, which adjusts for population in general. Data listed are expected to reflect the burden of COVID-19 infections within each of the four sewersheds. A lag of approximately 4-7 days will occur before new transmissions captured in wastewater data would result in a positive PCR test for COVID-19, the most common testing method used. While this wastewater surveillance tool can provide an early indication of major changes in transmission within the community, it remains an emerging technology. In assessing community transmission, wastewater surveillance data should only be considered in conjunction with other clinical measures, such as current infection rates and test positivity.

Each location is selected because it reflects input from a distinct catchment area (or sewershed) as identified on the color-coded map. Viral data collected from small catchment areas like these four Cambridge sites are more variable than data collected from central collection points (e.g., the MWRA facility on Deer Island) where wastewater from dozens of communities are joined and mixed. Data from each catchment area will be impacted by daily activity among individuals living in that area (e.g., working from home vs. traveling to work) and by daytime activities that are not from residences (businesses, schools, etc.) As such, the Regional MWRA data provides a more stable measure of regional viral counts. COVID wastewater data for Boston North and Boston South regions is available at https://www.mwra.com/biobot/biobotdata.htm

As described in https://data.cityofchicago.org/stories/s/sihu-d8va, the function of this dataset was replaced by https://data.cityofchicago.org/d/4tzt-ir6h. This dataset is historical-only. Concentrations of the SARS-CoV-2 virus (the COVID-19 virus) gene in the Chicago sewer system, as measured at eight sewershed sites. These sites represent catchment areas that describe wastewater from 319,700 Chicagoans as of January 2023. Because SARS-CoV-2 is shed in human feces, this method can be used to estimate changes in COVID-19 at different times and locations across Chicago. While the data in this dataset do not indicate how many people were infected with SARS-CoV-2, differences in the the normalized_n1 indicate whether the proportion of individuals at the site who are shedding that virus has changed.

Overview of policy measures taken in jurisdictions and by type of measure in support of the financial sector to address the impact of the COVID-19 pandemic. This dataset is updated regularly and remains work in progress. As such, it may contain errors and omissions.

Compiled by the Finance, Competitiveness & Innovation Global Practice. For inquiries, please reach out to Erik Feyen (efeijen@worldbank.org) and Davide Mare (dmare@worldbank.org).

Sources: National authorities; Yale, IIF, IMF, OECD, IADB.

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

The Response2covid19 dataset tracks governments’ responses to COVID-19 all around the world. The dataset is at the country-level and covers the January-October 2020 period; it is updated on a monthly basis. It tracks 20 measures – 13 public health measures and 7 economic measures – taken by 228 governments. The tracking of the measures allows creating an index of the rigidity of public health measures and an index of economic response to the pandemic. The objective of the dataset is both to inform citizens and to help researchers and governments in fighting the pandemic.The dataset can be downloaded and used freely. Please properly cite the name of the dataset (“Governments’ Responses to COVID-19 (Response2covid19)”) and the reference: Porcher, Simon "A novel dataset of governments' responses to COVID-19 all around the world", Chaire EPPP 2020-03 discussion paper, 2020.

Abstract copyright UK Data Service and data collection copyright owner.

As the UK went into the first lockdown of the COVID-19 pandemic, the team behind the biggest social survey in the UK, Understanding Society (UKHLS), developed a way to capture these experiences. From April 2020, participants from this Study were asked to take part in the Understanding Society COVID-19 survey, henceforth referred to as the COVID-19 survey or the COVID-19 study.

The COVID-19 survey regularly asked people about their situation and experiences. The resulting data gives a unique insight into the impact of the pandemic on individuals, families, and communities. The COVID-19 Teaching Dataset contains data from the main COVID-19 survey in a simplified form. It covers topics such as

• Socio-demographics
• Whether working at home and home-schooling
• COVID symptoms
• Health and well-being
• Social contact and neighbourhood cohesion
• Volunteering

The resource contains two data files:

• Cross-sectional: contains data collected in Wave 4 in July 2020 (with some additional variables from other waves);
• Longitudinal: Contains mainly data from Waves 1, 4 and 9 with key variables measured at three time points.

Key features of the dataset

• Missing values: in the web survey, participants clicking "Next" but not answering a question were given further options such as "Don't know" and "Prefer not to say". Missing observations like these are recorded using negative values such as -1 for "Don't know". In many instances, users of the data will need to set these values as missing. The User Guide includes Stata and SPSS code for setting negative missing values to system missing.

• The Longitudinal file is a balanced panel and is in wide format. A balanced panel means it only includes participants that took part in every wave. In wide format, each participant has one row of information, and each measurement of the same variable is a different variable.

• Weights: both the cross-sectional and longitudinal files include survey weights that adjust the sample to represent the UK adult population. The cross-sectional weight (betaindin_xw) adjusts for unequal selection probabilities in the sample design and for non-response. The longitudinal weight (ci_betaindin_lw) adjusts for the sample design and also for the fact that not all those invited to participate in the survey, do participate in all waves.

• Both the cross-sectional and longitudinal datasets include the survey design variables (psu and strata).

A full list of variables in both files can be found in the User Guide appendix.

Who is in the sample?

All adults (16 years old and over as of April 2020), in households who had participated in at least one of the last two waves of the main study Understanding Society, were invited to participate in this survey. From the September 2020 (Wave 5) survey onwards, only sample members who had completed at least one partial interview in any of the first four web surveys were invited to participate. From the November 2020 (Wave 6) survey onwards, those who had only completed the initial survey in April 2020 and none since, were no longer invited to participate

The User guide accompanying the data adds to the information here and includes a full variable list with details of measurement levels and links to the relevant questionnaire.

• Socio-demographics;
• Whether working at home and home-schooling;
• COVID symptoms;
• Health and well-being;
• Social contact and neighbourhood cohesion;
• Volunteering.

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 week of testing. These data can also be accessed here: https://github.com/nychealth/coronavirus-data/blob/master/trends/antibody-by-week.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 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

As of 10/22/2020, this dataset is no longer being updated and has been replaced with a new dataset, which can be accessed here: https://data.ct.gov/Health-and-Human-Services/COVID-19-case-rate-per-100-000-population-and-perc/hree-nys2 This dataset includes a count and rate per 100,000 population for COVID-19 cases, a count of COVID-19 PCR diagnostic tests, and a percent positivity rate for tests among people living in community settings for the previous two-week period. Dates are based on date of specimen collection (cases and positivity). A person is considered a new case only upon their first COVID-19 testing result because a case is defined as an instance or bout of illness. If they are tested again subsequently and are still positive, it still counts toward the test positivity metric but they are not considered another case. These case and test counts do not include cases or tests among people residing in congregate settings, such as nursing homes, assisted living facilities, or correctional facilities. These data are updated weekly and reflect the previous two full Sunday-Saturday (MMWR) weeks (https://wwwn.cdc.gov/nndss/document/MMWR_week_overview.pdf). DPH note about change from 7-day to 14-day metrics: Prior to 10/15/2020, these metrics were calculated using a 7-day average rather than a 14-day average. The 7-day metrics are no longer being updated as of 10/15/2020 but the archived dataset can be accessed here: https://data.ct.gov/Health-and-Human-Services/COVID-19-case-rate-per-100-000-population-and-perc/s22x-83rd As you know, we are learning more about COVID-19 all the time, including the best ways to measure COVID-19 activity in our communities. CT DPH has decided to shift to 14-day rates because these are more stable, particularly at the town level, as compared to 7-day rates. In addition, since the school indicators were initially published by DPH last summer, CDC has recommended 14-day rates and other states (e.g., Massachusetts) have started to implement 14-day metrics for monitoring COVID transmission as well. With respect to geography, we also have learned that many people are looking at the town-level data to inform decision making, despite emphasis on the county-level metrics in the published addenda. This is understandable as there has been variation within counties in COVID-19 activity (for example, rates that are higher in one town than in most other towns in the county).

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 sex. These data can also be accessed here: https://github.com/nychealth/coronavirus-data/blob/master/totals/antibody-by-sex.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 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

The Assessment Capacities Project (ACAPS) provides independent, high-quality, and timely humanitarian analysis to enable crisis responders to better understand and address the needs of the affected population. ACAPS' COVID19 Government Measures Dataset compiles the measures implemented by governments worldwide in response to the coronavirus pandemic. The measures fall into five categories: social distancing, movement restrictions, public health measures, social and economic measures, and lockdowns. Data are compiled by consultation with government, media, the United Nations, and other organizational sources.

Updates

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

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

• April 9, 2020

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

• April 20, 2020

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

• April 29, 2020

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

• September 1st, 2020

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

• February 12, 2021

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

• February 16, 2021

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

  Overview

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

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

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

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

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

Queries

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

• Filter cases by state here

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

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

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

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

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

Interactive

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

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

Interactive Embed Code

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

Caveats

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

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

Attribution

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

Note: In these datasets, a person is defined as up to date if they have received at least one dose of an updated COVID-19 vaccine. The Centers for Disease Control and Prevention (CDC) recommends that certain groups, including adults ages 65 years and older, receive additional doses.

On 6/16/2023 CDPH replaced the booster measures with a new “Up to Date” measure based on CDC’s new recommendations, replacing the primary series, boosted, and bivalent booster metrics The definition of “primary series complete” has not changed and is based on previous recommendations that CDC has since simplified. A person cannot complete their primary series with a single dose of an updated vaccine. Whereas the booster measures were calculated using the eligible population as the denominator, the new up to date measure uses the total estimated population. Please note that the rates for some groups may change since the up to date measure is calculated differently than the previous booster and bivalent measures.

This data is from the same source as the Vaccine Progress Dashboard at https://covid19.ca.gov/vaccination-progress-data/ which summarizes vaccination data at the county level by county of residence. Where county of residence was not reported in a vaccination record, the county of provider that vaccinated the resident is included. This applies to less than 1% of vaccination records. The sum of county-level vaccinations does not equal statewide total vaccinations due to out-of-state residents vaccinated in California.

These data do not include doses administered by the following federal agencies who received vaccine allocated directly from CDC: Indian Health Service, Veterans Health Administration, Department of Defense, and the Federal Bureau of Prisons.

Totals for the Vaccine Progress Dashboard and this dataset may not match, as the Dashboard totals doses by Report Date and this dataset totals doses by Administration Date. Dose numbers may also change for a particular Administration Date as data is updated.

Previous updates:

• On March 3, 2023, with the release of HPI 3.0 in 2022, the previous equity scores have been updated to reflect more recent community survey information. This change represents an improvement to the way CDPH monitors health equity by using the latest and most accurate community data available. The HPI uses a collection of data sources and indicators to calculate a measure of community conditions ranging from the most to the least healthy based on economic, housing, and environmental measures.

• Starting on July 13, 2022, the denominator for calculating vaccine coverage has been changed from age 5+ to all ages to reflect new vaccine eligibility criteria. Previously the denominator was changed from age 16+ to age 12+ on May 18, 2021, then changed from age 12+ to age 5+ on November 10, 2021, to reflect previous changes in vaccine eligibility criteria. The previous datasets based on age 16+ and age 5+ denominators have been uploaded as archived tables.

• Starting on May 29, 2021 the methodology for calculating on-hand inventory in the shipped/delivered/on-hand dataset has changed. Please see the accompanying data dictionary for details. In addition, this dataset is now down to the ZIP code level.

Public health and social measures (PHSMs) are measures or actions by individuals, institutions, communities, local and national governments and international bodies to slow or stop the spread of an infectious disease, such as COVID-19.

Since the start of the COVID-19 pandemic, a number of organizations have begun tracking implementation of PHSMs around the world, using different data collection methods, database designs and classification schemes. A unique collaboration between WHO, the London School of Hygiene and Tropical Medicine, ACAPS, University of Oxford, Global Public Health Intelligence Network, US Centers for Disease Control and Prevention and the Complexity Science Hub Vienna has brought these datasets together, using a common taxonomy and structure, into a single, open-content dataset for public use.

These datasets are for a cohort of n=1540 anonymised hospitalised COVID-19 patients, and the data provide information on outcomes (i.e. patient death or discharge), demographics and biomarker measurements for two New York hospitals: State
University of New York (SUNY) Downstate Health Sciences University and Maimonides
Medical Center.

The file "demographics_both_hospitals.csv" contains the ultimate outcomes of hospitalisation (whether a patient was discharged or died), demographic information and known comorbidities for each of the patients.

The file "dynamics_clean_both_hospitals.csv" contains cleaned dynamic biomarker measurements for the n=1233 patients where this information was available and the data passed our various checks (see https://doi.org/10.1101/2021.11.12.21266248 for information of these checks and the cleaning process). Patients can be matched to demographic data via the "id" column.

Study approval and data collection

Study approval was obtained from the State University of New York (SUNY) Downstate Health Sciences University Institutional Review Board (IRB\#1595271-1) and Maimonides Medical Center Institutional Review Board/Research Committee (IRB\#2020-05-07). A retrospective query was performed among the patients who were admitted to SUNY Downstate Medical Center and Maimonides Medical Center with COVID-19-related symptoms, which was subsequently confirmed by RT PCR, from the beginning of February 2020 until the end of May 2020. Stratified randomization was used to select at least 500 patients who were discharged and 500 patients who died due to the complications of COVID-19. Patient outcome was recorded as a binary choice of “discharged” versus “COVID-19 related mortality”. Patients whose outcome was unknown were excluded. Demographic, clinical history and laboratory data was extracted from the hospital’s electronic health records.

DPH note about change from 7-day to 14-day metrics: As of 10/15/2020, this dataset is no longer being updated. Starting on 10/15/2020, these metrics will be calculated using a 14-day average rather than a 7-day average. The new dataset using 14-day averages can be accessed here: https://data.ct.gov/Health-and-Human-Services/COVID-19-case-rate-per-100-000-population-and-perc/hree-nys2

As you know, we are learning more about COVID-19 all the time, including the best ways to measure COVID-19 activity in our communities. CT DPH has decided to shift to 14-day rates because these are more stable, particularly at the town level, as compared to 7-day rates. In addition, since the school indicators were initially published by DPH last summer, CDC has recommended 14-day rates and other states (e.g., Massachusetts) have started to implement 14-day metrics for monitoring COVID transmission as well.

With respect to geography, we also have learned that many people are looking at the town-level data to inform decision making, despite emphasis on the county-level metrics in the published addenda. This is understandable as there has been variation within counties in COVID-19 activity (for example, rates that are higher in one town than in most other towns in the county).

This dataset includes a weekly count and weekly rate per 100,000 population for COVID-19 cases, a weekly count of COVID-19 PCR diagnostic tests, and a weekly percent positivity rate for tests among people living in community settings. Dates are based on date of specimen collection (cases and positivity).

A person is considered a new case only upon their first COVID-19 testing result because a case is defined as an instance or bout of illness. If they are tested again subsequently and are still positive, it still counts toward the test positivity metric but they are not considered another case.

These case and test counts do not include cases or tests among people residing in congregate settings, such as nursing homes, assisted living facilities, or correctional facilities.

These data are updated weekly; the previous week period for each dataset is the previous Sunday-Saturday, known as an MMWR week (https://wwwn.cdc.gov/nndss/document/MMWR_week_overview.pdf). The date listed is the date the dataset was last updated and corresponds to a reporting period of the previous MMWR week. For instance, the data for 8/20/2020 corresponds to a reporting period of 8/9/2020-8/15/2020.

Notes: 9/25/2020: Data for Mansfield and Middletown for the week of Sept 13-19 were unavailable at the time of reporting due to delays in lab reporting.

Wastewater collection areas are comprised of merged sewage drainage basins that flow to a shared testing location for the COVID-19 wastewater study. The collection area polygons are published with related wastewater testing data, which are provided by scientists from Arizona State University's Biodesign Institute.Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) causes COVID-19. People infected with SARS-CoV-2 excrete the virus in their feces in a process known as “shedding”. The municipal wastewater treatment system (sewage system) collects and aggregates these bathroom contributions across communities. Tempe wastewater samples are collected downstream of a community and the samples are brought to the ASU lab to analyze for the virus. Analysis is based on the genetic material inside the virus. This dashboard focuses on the genome copies per liter. The absence of a value in a chart indicates that either no samples were collected or that samples are still being analyzed. A value of 5,000 represents samples that are below detection or reporting limits for the test being used. Note of Caution:The influence of this data on community health decisions in the future is unknown. Data collection is being used to depict overall weekly trends and should not be interpreted without a holistic assessment of public health data. The purpose of this weekly data is to support research as well as to identify overall trends of the genome copies in each liter of wastewater per collection area. In the future these trend data could be used alongside other authoritative data, including the number of daily new confirmed cases in Tempe published by the Arizona Department of Health and data documenting the state and local interventions (i.e. social distancing, closures and safe openings). The numeric values of the results should not be viewed as actionable right now; they represent one potentially helpful piece of information among various data sources.We share this information with the public with the disclaimer that only the future can tell how much “diagnostic value” we can and should attribute to the numeric measurements we obtain from the sewer. However, what we measure, the COVID-19-related RNA in wastewater, we know is real and we share that info with our community.In the Tempe COVID -19 Wastewater Results Dashboard, please note:These data illustrate a trend of the signal of the weekly average of COVID-19 genome copies per liter of wastewater in Tempe's sewage. The dashboard and collection area map do not depict the number of individuals infected. Each collection area includes at least one sampling location, which collects wastewater from across the collection area. It does not reflect the specific location where the deposit occurs.While testing can successfully quantify the results, research has not yet determined the relationship between these genome values and the number of people who are positive for COVID-19 in the community.The quantity of RNA detected in sewage is real; the interpretation of that signal and its implication for public health is ongoing research. Currently, there is not a baseline for determining a strong or weak signal.The shedding rate and shedding duration for individuals, both symptomatic and asymptomatic, is still unknown.Data are shared as the testing results become available. As results may not be released at the same time, testing results for each area may not yet be seen for a given day or week. The dashboard presents the weekly averages. Data are collected from 2-7 days per week. The quantifiable level of 5,000 copies per liter is the lowest amount measurable with current testing. Results that are below the quantifiable level of 5,000 copies per liter do not suggest the absence of the virus in the collection area. It is possible to have results below the quantifiable level of 5,000 on one day/week and then have a greater signal on a subsequent day/week.For Collection Area 1, Tempe's wastewater co-mingles with wastewater from a regional sewage line. Tempe's sewage makes up the majority of Collection Area 1 samples. After the collection period of April 7-24, 2020, Collection Area 1 samples include only Tempe wastewater.For Collection Area 3, Tempe's wastewater co-mingles with wastewater from a regional sewage line. For analysis and reporting, Tempe’s wastewater is separated from regional sewage. This operations dashboard is used in an associated story map Fighting Coronavirus/COVID-19 with Public Health Data https://storymaps.arcgis.com/stories/e6a45aad50c24e22b7285412d2d6ff2a about the COVID-19 wastewater testing project. This operations dashboard also support's the main Tempe Wastewater BioIntel Program hub site https://wastewater.tempe.gov/.

Note: Data elements were retired from HERDS on 10/6/23 and this dataset was archived.

This dataset includes the cumulative number and percent of healthcare facility-reported fatalities for patients with lab-confirmed COVID-19 disease by reporting date and patient sex. This dataset does not include fatalities related to COVID-19 disease that did not occur at a hospital, nursing home, or adult care facility. The primary goal of publishing this dataset is to provide users with information about healthcare facility fatalities among patients with lab-confirmed COVID-19 disease.

The information in this dataset is also updated daily on the NYS COVID-19 Tracker at https://www.ny.gov/covid-19tracker.

The data source for this dataset is the daily COVID-19 survey through the New York State Department of Health (NYSDOH) Health Electronic Response Data System (HERDS). Hospitals, nursing homes, and adult care facilities are required to complete this survey daily. The information from the survey is used for statewide surveillance, planning, resource allocation, and emergency response activities. Hospitals began reporting for the HERDS COVID-19 survey in March 2020, while Nursing Homes and Adult Care Facilities began reporting in April 2020. It is important to note that fatalities related to COVID-19 disease that occurred prior to the first publication dates are also included.

The fatality numbers in this dataset are calculated by summing the patient fatalities by patient sex, as of each reporting date. The statewide total fatality numbers are calculated by summing the number of fatalities across all patient sexes, by reporting date. The fatality percentages are calculated by dividing the number of fatalities in each patient sex by the statewide total number of fatalities, by reporting date. The fatality numbers represent the cumulative number of fatalities that have been reported as of each reporting date.

In collaboration with the Public Health Agency of Canada (PHAC), this data file provides Canadians and researchers with preliminary data to monitor only the confirmed cases of coronavirus (COVID-19) in Canada. Given the rapidly-evolving nature of this situation, these data are considered preliminary.

RT-PCR measurements of SARS-CoV-2 levels for COVID-19 patients in Seattle, Washington. Complimentary dataset to Systems Serology measurments of the same patient cohort.

This datase contains the metadata associated with this publication:

A. Estévez-Danta, L. Bijlsma, R. Capela, R. Cela, A. Celma, F. Hernández, U. Lertxundi, J. Matias, R. Montes, G. Orive, A. Prieto, M.M. Santos, R. Rodil, J.B. Quintana

Use of illicit drugs, alcohol and tobacco in Spain and Portugal during the COVID-19 crisis in 2020 as measured by wastewater-based epidemiology

Science of the Total Environment, 2022, 836, 155697

https://doi.org/10.1016/j.scitotenv.2022.155697

The data is deposited in ZENODO:

https://zenodo.org/doi/10.5281/zenodo.10829752

If you reuse the data, please cite the publication and ZENODO deposit mentioned above

Explanation of the different sheets of the Excel file (All_Data_STOTEN_2022_155697) or different individual CSV files (named as below):

WWTP_details: explanation of wastewater treatment plats (WWTPs) sampled, flow rates, etc.

Concentrations: concentrations measured in the samples

PNDL: population normalized daily loads calculated per each sample

Consumption: estimated drug use (see the publication for correction factors)

EF: enantiomeric fraction, expressed as fraction of the R-enantiomer for the samples analyzed

Abreviations

AMP Amphetamine

MAMP Methamphetamine

MDMA 3,4-Methylenedioxymethamphetamine

BE Benzoylecgonine

COC Cocaine

THC-COOH 11-Nor-9-carboxy-Δ9-tetrahydrocannabinol

THC Δ9-Tetrahydrocannabinol

COT Cotinine

OH-COT Trans-3'-Hydroxycotinine

NIC Nicotine

EtS Ethyl sulfate