Reporting of Aggregate Case and Death Count data was discontinued May 11, 2023, with the expiration of the COVID-19 public health emergency declaration. Although these data will continue to be publicly available, this dataset will no longer be updated.

This archived public use dataset has 11 data elements reflecting United States COVID-19 community levels for all available counties.

The COVID-19 community levels were developed using a combination of three metrics — new COVID-19 admissions per 100,000 population in the past 7 days, the percent of staffed inpatient beds occupied by COVID-19 patients, and total new COVID-19 cases per 100,000 population in the past 7 days. The COVID-19 community level was determined by the higher of the new admissions and inpatient beds metrics, based on the current level of new cases per 100,000 population in the past 7 days. New COVID-19 admissions and the percent of staffed inpatient beds occupied represent the current potential for strain on the health system. Data on new cases acts as an early warning indicator of potential increases in health system strain in the event of a COVID-19 surge.

Using these data, the COVID-19 community level was classified as low, medium, or high.

COVID-19 Community Levels were used to help communities and individuals make decisions based on their local context and their unique needs. Community vaccination coverage and other local information, like early alerts from surveillance, such as through wastewater or the number of emergency department visits for COVID-19, when available, can also inform decision making for health officials and individuals.

For the most accurate and up-to-date data for any county or state, visit the relevant health department website. COVID Data Tracker may display data that differ from state and local websites. This can be due to differences in how data were collected, how metrics were calculated, or the timing of web updates.

Archived Data Notes:

This dataset was renamed from "United States COVID-19 Community Levels by County as Originally Posted" to "United States COVID-19 Community Levels by County" on March 31, 2022.

March 31, 2022: Column name for county population was changed to “county_population”. No change was made to the data points previous released.

March 31, 2022: New column, “health_service_area_population”, was added to the dataset to denote the total population in the designated Health Service Area based on 2019 Census estimate.

March 31, 2022: FIPS codes for territories American Samoa, Guam, Commonwealth of the Northern Mariana Islands, and United States Virgin Islands were re-formatted to 5-digit numeric for records released on 3/3/2022 to be consistent with other records in the dataset.

March 31, 2022: Changes were made to the text fields in variables “county”, “state”, and “health_service_area” so the formats are consistent across releases.

March 31, 2022: The “%” sign was removed from the text field in column “covid_inpatient_bed_utilization”. No change was made to the data. As indicated in the column description, values in this column represent the percentage of staffed inpatient beds occupied by COVID-19 patients (7-day average).

March 31, 2022: Data values for columns, “county_population”, “health_service_area_number”, and “health_service_area” were backfilled for records released on 2/24/2022. These columns were added since the week of 3/3/2022, thus the values were previously missing for records released the week prior.

April 7, 2022: Updates made to data released on 3/24/2022 for Guam, Commonwealth of the Northern Mariana Islands, and United States Virgin Islands to correct a data mapping error.

April 21, 2022: COVID-19 Community Level (CCL) data released for counties in Nebraska for the week of April 21, 2022 have 3 counties identified in the high category and 37 in the medium category. CDC has been working with state officials t

The COVID-19 dashboard includes data on city/town COVID-19 activity, confirmed and probable cases of COVID-19, confirmed and probable deaths related to COVID-19, and the demographic characteristics of cases and deaths.

COVID-19 Medical Surge Facilities *THIS DATASET IS NO LONGER BEING UPDATED - The last update to the data occurred on August 5th, 2021.

This public use dataset has 11 data elements reflecting COVID-19 community levels for all available counties. This dataset contains the same values used to display information available at https://www.cdc.gov/coronavirus/2019-ncov/science/community-levels-county-map.html. CDC looks at the combination of three metrics — new COVID-19 admissions per 100,000 population in the past 7 days, the percent of staffed inpatient beds occupied by COVID-19 patients, and total new COVID-19 cases per 100,000 population in the past 7 days — to determine the COVID-19 community level. The COVID-19 community level is determined by the higher of the new admissions and inpatient beds metrics, based on the current level of new cases per 100,000 population in the past 7 days. New COVID-19 admissions and the percent of staffed inpatient beds occupied represent the current potential for strain on the health system. Data on new cases acts as an early warning indicator of potential increases in health system strain in the event of a COVID-19 surge. Using these data, the COVID-19 community level is classified as low, medium , or high. COVID-19 Community Levels can help communities and individuals make decisions based on their local context and their unique needs. Community vaccination coverage and other local information, like early alerts from surveillance, such as through wastewater or the number of emergency department visits for COVID-19, when available, can also inform decision making for health officials and individuals. See https://www.cdc.gov/coronavirus/2019-ncov/science/community-levels.html for more information. Visit CDC’s COVID Data Tracker County View* to learn more about the individual metrics used for CDC’s COVID-19 community level in your county. Please note that county-level data are not available for territories. Go to https://covid.cdc.gov/covid-data-tracker/#county-view. For the most accurate and up-to-date data for any county or state, visit the relevant health department website. *COVID Data Tracker may display data that differ from state and local websites. This can be due to differences in how data were collected, how metrics were calculated, or the timing of web updates.

This public use dataset has 11 data elements reflecting COVID-19 community levels for all available counties. This dataset contains the same values used to display information available at https://www.cdc.gov/coronavirus/2019-ncov/science/community-levels-county-map.html. CDC looks at the combination of three metrics — new COVID-19 admissions per 100,000 population in the past 7 days, the percent of staffed inpatient beds occupied by COVID-19 patients, and total new COVID-19 cases per 100,000 population in the past 7 days — to determine the COVID-19 community level. The COVID-19 community level is determined by the higher of the new admissions and inpatient beds metrics, based on the current level of new cases per 100,000 population in the past 7 days. New COVID-19 admissions and the percent of staffed inpatient beds occupied represent the current potential for strain on the health system. Data on new cases acts as an early warning indicator of potential increases in health system strain in the event of a COVID-19 surge. Using these data, the COVID-19 community level is classified as low, medium , or high. COVID-19 Community Levels can help communities and individuals make decisions based on their local context and their unique needs. Community vaccination coverage and other local information, like early alerts from surveillance, such as through wastewater or the number of emergency department visits for COVID-19, when available, can also inform decision making for health officials and individuals. See https://www.cdc.gov/coronavirus/2019-ncov/science/community-levels.html for more information. Visit CDC’s COVID Data Tracker County View* to learn more about the individual metrics used for CDC’s COVID-19 community level in your county. Please note that county-level data are not available for territories. Go to https://covid.cdc.gov/covid-data-tracker/#county-view.

To characterize how the 2020 winter surge in COVID-19 volumes impacted case fatality in an adequately-resourced health system.

The New York Times is releasing a series of data files with cumulative counts of coronavirus cases in the United States, at the state and county level, over time. We are compiling this time series data from state and local governments and health departments in an attempt to provide a complete record of the ongoing outbreak.

Since late January, The Times has tracked cases of coronavirus in real time as they were identified after testing. Because of the widespread shortage of testing, however, the data is necessarily limited in the picture it presents of the outbreak.

We have used this data to power our maps and reporting tracking the outbreak, and it is now being made available to the public in response to requests from researchers, scientists and government officials who would like access to the data to better understand the outbreak.

The data begins with the first reported coronavirus case in Washington State on Jan. 21, 2020. We will publish regular updates to the data in this repository.

The first two cases of the new coronavirus (COVID-19) in Italy were recorded between the end of January and the beginning of February 2020. Since then, the number of cases in Italy increased steadily, reaching over 26.9 million as of January 8, 2025. The region mostly hit by the virus in the country was Lombardy, counting almost 4.4 million cases. On January 11, 2022, 220,532 new cases were registered, which represented the biggest daily increase in cases in Italy since the start of the pandemic. The virus originated in Wuhan, a Chinese city populated by millions and located in the province of Hubei. More statistics and facts about the virus in Italy are available here.For a global overview, visit Statista's webpage exclusively dedicated to coronavirus, its development, and its impact.

After entering Italy, the coronavirus (COVID-19) spread fast. The strict lockdown implemented by the government during the Spring 2020 helped to slow down the outbreak. However, the country had to face four new harsh waves of contagion. As of January 1, 2025, the total number of cases reported by the authorities reached over 26.9 million. The north of the country was mostly hit, and the region with the highest number of cases was Lombardy, which registered almost 4.4 million of them. The north-eastern region of Veneto and the southern region of Campania followed in the list. When adjusting these figures for the population size of each region, however, the picture changed, with the region of Veneto being the area where the virus had the highest relative incidence. Coronavirus in Italy Italy has been among the countries most impacted by the coronavirus outbreak. Moreover, the number of deaths due to coronavirus recorded in Italy is significantly high, making it one of the countries with the highest fatality rates worldwide, especially in the first stages of the pandemic. In particular, a very high mortality rate was recorded among patients aged 80 years or older. Impact on the economy The lockdown imposed during the Spring 2020, and other measures taken in the following months to contain the pandemic, forced many businesses to shut their doors and caused industrial production to slow down significantly. As a result, consumption fell, with the sectors most severely hit being hospitality and tourism, air transport, and automotive. Several predictions about the evolution of the global economy were published at the beginning of the pandemic, based on different scenarios about the development of the pandemic. According to the official results, it appeared that the coronavirus outbreak had caused Italy’s GDP to shrink by approximately nine percent in 2020.

Note: As of July 21, 2021, this dataset no longer updates.

A. SUMMARY Data on daily hospital bed use and available capacity at San Francisco acute care hospitals from April 2020 onward. Long Term Care facilities (like Laguna Honda and Kentfield) are not included in this data as acute care patients cannot be admitted to these facilities. B. HOW THE DATASET IS CREATED This hospital capacity information is based on data that all SF acute care hospitals report to the San Francisco Department of Public Health. C. UPDATE PROCESS Updates automatically at 05:00 Pacific Time each day. Redundant runs are scheduled at 07:00 and 09:00 in case of pipeline failure. This data is on a 4-day lag to account for the time needed to complete and validate data from all SF acute care hospitals. D. HOW TO USE THIS DATASET This data provides visibility into current occupancy levels and use of San Francisco acute care hospitals and potential ability to accommodate anticipated surges of COVID patients. Data includes current census of COVID-19 patients (including both confirmed cases and suspected COVID patients) and other patients in acute care hospitals, shown in the “Status” column. The “Status” column also includes all available beds. This daily census information is stratified by type of bed (acute care, intensive care, and surge) in the “Bed Type” column. Acute care beds treat patients with illnesses and injuries including recovery from surgeries. Intensive care (ICU) beds are for sicker patients in need of critical and life support services that can include the use of a ventilator. Surge beds are the additional beds that can be made available to handle an influx of COVID-19 patients; surge beds are differentiated between acute care surge beds and ICU surge beds. Note: The current census of COVID patients shown here may not always match the hospitalizations data (https://data.sfgov.org/COVID-19/COVID-19-Hospitalizations/nxjg-bhem), as that data includes all hospitals and long term care facilities. As described above, those long term care facilities are not included here as they don’t have the capacity to take in additional acute care patients and therefore aren’t included in capacity measures.

COVID-19: Army Corps Uses Maps and Models to Create Surge Hospital CapacityAfter recognizing the possibility that the COVID-19 pandemic could cause hospital bed capacity to be exceeded, the US Army Corps of Engineers (USACE) was tasked with working with the states to build and inspect alternate care facilities.A team from USACE developed engineering plans for converting existing facilities with rooms (such as hotels or college dormitories) and those with large open areas (like field houses or convention centers). From there, the team developed standardized designs, then used mobile applications to quickly assess candidate sites and inspect the retrofitted facilities for readiness._Communities around the world are taking strides in mitigating the threat that COVID-19 (coronavirus) poses. Geography and location analysis have a crucial role in better understanding this evolving pandemic.When you need help quickly, Esri can provide data, software, configurable applications, and technical support for your emergency GIS operations. Use GIS to rapidly access and visualize mission-critical information. Get the information you need quickly, in a way that’s easy to understand, to make better decisions during a crisis.Esri’s Disaster Response Program (DRP) assists with disasters worldwide as part of our corporate citizenship. We support response and relief efforts with GIS technology and expertise.More information...

As of January 1, 2025, the number of active coronavirus (COVID-19) infections in Italy was approximately 218,000. Among these, 42 infected individuals were being treated in intensive care units. Another 1,332 individuals infected with the coronavirus were hospitalized with symptoms, while approximately 217,000 thousand were in isolation at home. The total number of coronavirus cases in Italy reached over 26.9 million (including active cases, individuals who recovered, and individuals who died) as of the same date. The region mostly hit by the spread of the virus was Lombardy, which counted almost 4.4 million cases.For a global overview, visit Statista's webpage exclusively dedicated to coronavirus, its development, and its impact.

DSH COVID-19 Patient Testing: Last updated -02/10/2025

DSH COVID-19 Patient Data reports on patient positives and testing counts at the facility level for DSH. The table reports on the following data fields:

• Total patients that tested positive for COVID-19 since 5/16/2020

• Patients newly positive for COVID-19 in the last 14 days

• Patient deaths while patient was positive for COVID-19 since 5/30/2020

• Total number of tests administered since 3/23/2020

Table Notes:

COVID-19 test results for patients include DSH patients who are tested while receiving treatment at an outside medical facility. Data has been de-identified in accordance with CalHHS Data De-identification Guidelines. Counts between 1-10 are masked with "<11". Includes Patients Under Investigation (PUIs) testing and proactive testing of asymptomatic patients for surveillance of geriatric, medically fragile, and skilled nursing facility units and for patients upon admission, re-admission, or discharge. Includes all individuals who were positive for COVID-19 at time of death, regardless of underlying health conditions or whether the cause of death has been confirmed to be COVID-19 related illness. Metro-Norwalk is additional COVID-19 surge space and technically a branch location that is part of DSH Metropolitan Hospital.

Dollar sales of packaged foods increased by approximately ** percent (compared to the previous year) in early impact states relative to the rest of the country in the United States in March 2020 due to the impact of the coronavirus pandemic. Other fast moving consumer goods (FMCG) categories that witnessed dollar sales growth are frozen foods and dairy, increasing by around ** and ** percent respectively. For further information about the coronavirus (COVID-19) pandemic, please visit our dedicated Facts and Figures page.

The on-site coronavirus testing service market experienced significant growth during the COVID-19 pandemic and continues to evolve. While precise market size figures for 2025 are unavailable, considering the substantial initial demand and subsequent tapering off, we can estimate a 2025 market size of approximately $2.5 billion USD. This reflects a decline from peak pandemic levels but sustains considerable value due to ongoing demand from various sectors. The market's Compound Annual Growth Rate (CAGR) from 2025 to 2033 is projected to be around 8%, driven primarily by the increasing need for rapid and convenient testing solutions in workplaces, large events, and travel hubs. This relatively moderate CAGR reflects a shift from pandemic-level urgency to a more normalized, albeit still significant, demand for rapid on-site testing. Key growth drivers include the ongoing need for rapid testing in high-traffic areas to prevent outbreaks and the convenience offered by on-site services, minimizing disruptions. The market is segmented by testing type (throat swab, nasal swab, saliva) and application (workplace, large events, tourist attractions, others). While throat and nasal swab testing remain prevalent, saliva testing is gaining traction due to its ease of use and non-invasive nature. The growth is also geographically diverse, with North America and Europe representing significant market shares, followed by Asia Pacific. However, emerging economies are expected to show increased adoption as healthcare infrastructure develops and awareness of preventative testing increases. Restraining factors include fluctuating demand based on infection rates, regulatory changes impacting testing procedures, and cost considerations for businesses and organizations implementing on-site testing programs. The competitive landscape is characterized by a mix of established healthcare companies and specialized on-site testing providers. The continued growth of the on-site coronavirus testing market hinges on several factors. Beyond the obvious need for rapid response to outbreaks, factors like increased integration with workplace safety protocols, the demand for faster turnaround times compared to traditional lab tests, and the increasing convenience for travelers contribute to sustained growth. The development of more advanced and accurate testing technologies, along with the potential integration of on-site testing into broader health screening programs, will shape the market's trajectory over the next decade. While the initial pandemic surge has subsided, the market remains robust, and projections point to a steady and sustained growth, reflecting a shift towards preventative healthcare measures and ongoing monitoring for respiratory illnesses.

Proportion of variance explained from PCA analysis.

Principal component loading vectors from PCA analysis.

Models and Maps Explore COVID-19 Surges and Capacity to Help Officials PrepareMultiple models provide up-to-date estimates of how many people will need to be hospitalized, and maps help explore hospital capacity and impacts to people.CHIME model_Communities around the world are taking strides in mitigating the threat that COVID-19 (coronavirus) poses. Geography and location analysis have a crucial role in better understanding this evolving pandemic.When you need help quickly, Esri can provide data, software, configurable applications, and technical support for your emergency GIS operations. Use GIS to rapidly access and visualize mission-critical information. Get the information you need quickly, in a way that’s easy to understand, to make better decisions during a crisis.Esri’s Disaster Response Program (DRP) assists with disasters worldwide as part of our corporate citizenship. We support response and relief efforts with GIS technology and expertise.More information...

This data package includes the underlying data to replicate the charts, tables, and calculations presented in The role of long histories of “lived experience” in the COVID-era inflationary surge, PIIE Working Paper 25-7.

If you use the data, please cite as:

Gagnon, Joseph E., and Steven Kamin. 2025. The role of long histories of “lived experience” in the COVID-era inflationary surge. PIIE Working Paper 25-7. Washington: Peterson Institute for International Economics.