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 to verify the data submitted, as other data systems are not providing alerts for substantial increases in disease transmission or severity in the state.

May 26, 2022: COVID-19 Community Level (CCL) data released for McCracken County, KY for the week of May 5, 2022 have been updated to correct a data processing error. McCracken County, KY should have appeared in the low community level category during the week of May 5, 2022. This correction is reflected in this update.

May 26, 2022: COVID-19 Community Level (CCL) data released for several Florida counties for the week of May 19th, 2022, have been corrected for a data processing error. Of note, Broward, Miami-Dade, Palm Beach Counties should have appeared in the high CCL category, and Osceola County should have appeared in the medium CCL category. These corrections are reflected in this update.

May 26, 2022: COVID-19 Community Level (CCL) data released for Orange County, New York for the week of May 26, 2022 displayed an erroneous case rate of zero and a CCL category of low due to a data source error. This county should have appeared in the medium CCL category.

June 2, 2022: COVID-19 Community Level (CCL) data released for Tolland County, CT for the week of May 26, 2022 have been updated to correct a data processing error. Tolland County, CT should have appeared in the medium community level category during the week of May 26, 2022. This correction is reflected in this update.

June 9, 2022: COVID-19 Community Level (CCL) data released for Tolland County, CT for the week of May 26, 2022 have been updated to correct a misspelling. The medium community level category for Tolland County, CT on the week of May 26, 2022 was misspelled as “meduim” in the data set. This correction is reflected in this update.

June 9, 2022: COVID-19 Community Level (CCL) data released for Mississippi counties for the week of June 9, 2022 should be interpreted with caution due to a reporting cadence change over the Memorial Day holiday that resulted in artificially inflated case rates in the state.

July 7, 2022: COVID-19 Community Level (CCL) data released for Rock County, Minnesota for the week of July 7, 2022 displayed an artificially low case rate and CCL category due to a data source error. This county should have appeared in the high CCL category.

July 14, 2022: COVID-19 Community Level (CCL) data released for Massachusetts counties for the week of July 14, 2022 should be interpreted with caution due to a reporting cadence change that resulted in lower than expected case rates and CCL categories in the state.

July 28, 2022: COVID-19 Community Level (CCL) data released for all Montana counties for the week of July 21, 2022 had case rates of 0 due to a reporting issue. The case rates have been corrected in this update.

July 28, 2022: COVID-19 Community Level (CCL) data released for Alaska for all weeks prior to July 21, 2022 included non-resident cases. The case rates for the time series have been corrected in this update.

July 28, 2022: A laboratory in Nevada reported a backlog of historic COVID-19 cases. As a result, the 7-day case count and rate will be inflated in Clark County, NV for the week of July 28, 2022.

August 4, 2022: COVID-19 Community Level (CCL) data was updated on August 2, 2022 in error during performance testing. Data for the week of July 28, 2022 was changed during this update due to additional case and hospital data as a result of late reporting between July 28, 2022 and August 2, 2022. Since the purpose of this data set is to provide point-in-time views of COVID-19 Community Levels on Thursdays, any changes made to the data set during the August 2, 2022 update have been reverted in this update.

August 4, 2022: COVID-19 Community Level (CCL) data for the week of July 28, 2022 for 8 counties in Utah (Beaver County, Daggett County, Duchesne County, Garfield County, Iron County, Kane County, Uintah County, and Washington County) case data was missing due to data collection issues. CDC and its partners have resolved the issue and the correction is reflected in this update.

August 4, 2022: Due to a reporting cadence change, case rates for all Alabama counties will be lower than expected. As a result, the CCL levels published on August 4, 2022 should be interpreted with caution.

August 11, 2022: COVID-19 Community Level (CCL) data for the week of August 4, 2022 for South Carolina have been updated to correct a data collection error that resulted in incorrect case data. CDC and its partners have resolved the issue and the correction is reflected in this update.

August 18, 2022: COVID-19 Community Level (CCL) data for the week of August 11, 2022 for Connecticut have been updated to correct a data ingestion error that inflated the CT case rates. CDC, in collaboration with CT, has resolved the issue and the correction is reflected in this update.

August 25, 2022: A laboratory in Tennessee reported a backlog of historic COVID-19 cases. As a result, the 7-day case count and rate may be inflated in many counties and the CCLs published on August 25, 2022 should be interpreted with caution.

August 25, 2022: Due to a data source error, the 7-day case rate for St. Louis County, Missouri, is reported as zero in the COVID-19 Community Level data released on August 25, 2022. Therefore, the COVID-19 Community Level for this county should be interpreted with caution.

September 1, 2022: Due to a reporting issue, case rates for all Nebraska counties will include 6 days of data instead of 7 days in the COVID-19 Community Level (CCL) data released on September 1, 2022. Therefore, the CCLs for all Nebraska counties should be interpreted with caution.

September 8, 2022: Due to a data processing error, the case rate for Philadelphia County, Pennsylvania,

This public use dataset has 11 data elements reflecting United States COVID-19 community levels for all available counties. This dataset contains the same values used to display information available on the COVID Data Tracker at: https://covid.cdc.gov/covid-data-tracker/#county-view?list_select_state=all_states&list_select_county=all_counties&data-type=CommunityLevels The data are updated weekly.

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.

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.

For more details on the Minnesota Department of Health COVID-19 thresholds, see COVID-19 Public Health Risk Measures: Data Notes (Updated 4/13/22). https://mn.gov/covid19/assets/phri_tcm1148-434773.pdf

Note: 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.

Earlier this year, Dr. Hoffman and Dr. Fafard published a book chapter on the efficacy and legality of border closures enacted by governments in response to changing COVID-19 conditions. The authors concluded border closures are at best, regarded as powerful symbolic acts taken by governments to show they are acting forcefully, even if the actions lack an epidemiological impact and breach international law. This COVID-19 travel restriction project was developed out of a necessity and desire to further examine the empirical implications of border closures. The current dataset contains bilateral travel restriction information on the status of 179 countries between 1 January 2020 and 8 June 2020. The data was extracted from the ‘international controls’ column from the Oxford COVID-19 Government Response Tracker (OxCGRT). The data in the ‘international controls’ column outlined a country’s change in border control status, as a response to COVID-19 conditions. Accompanying source links were further verified through random selection and comparison with external news sources. Greater weight is given to official national government sources, then to provincial and municipal news-affiliated agencies. The database is presented in matrix form for each country-pair and date. Subsequently, each cell is represented by datum Xdmn and indicates the border closure status on date d by country m on country n. The coding is as follows: no border closure (code = 0), targeted border closure (= 1), and a total border closure (= 99). The dataset provides further details in the ‘notes’ column if the type of closure is a modified form of a targeted closure, either as a land or port closure, flight or visa suspension, or a re-opening of borders to select countries. Visa suspensions and closure of land borders were coded separately as de facto border closures and analyzed as targeted border closures in quantitative analyses. The file titled ‘BTR Supplementary Information’ covers a multitude of supplemental details to the database. The various tabs cover the following: 1) Codebook: variable name, format, source links, and description; 2) Sources, Access dates: dates of access for the individual source links with additional notes; 3) Country groups: breakdown of EEA, EU, SADC, Schengen groups with source links; 4) Newly added sources: for missing countries with a population greater than 1 million (meeting the inclusion criteria), relevant news sources were added for analysis; 5) Corrections: external news sources correcting for errors in the coding of international controls retrieved from the OxCGRT dataset. At the time of our study inception, there was no existing dataset which recorded the bilateral decisions of travel restrictions between countries. We hope this dataset will be useful in the study of the impact of border closures in the COVID-19 pandemic and widen the capabilities of studying border closures on a global scale, due to its interconnected nature and impact, rather than being limited in analysis to a single country or region only. Statement of contributions: Data entry and verification was performed mainly by GL, with assistance from MJP and RN. MP and IW provided further data verification on the nine countries purposively selected for the exploratory analysis of political decision-making.

Background: Antibodies play a key role in the immune defence against infectious pathogens. Understanding the underlying process of B cell recognition is not only of fundamental interest; it supports important applications within diagnostics and therapeutics. Whereas the nature of conformational B cell epitope recognition is inherently complicated, linear B cell epitopes offer a straightforward approach that potentially can be reduced to one of peptide recognition. Methods: Using an overlapping peptide approach representing the entire proteomes of the seven main coronaviruses known to infect humans, we analysed sera pooled from eight PCR-confirmed COVID-19 convalescents and eight pre-pandemic controls. Using a high-density peptide microarray platform, 13-mer peptides overlapping by 11 amino acids were in situ synthesised and incubated with the pooled primary serum samples, followed by development with secondary fluorochrome-labelled anti-IgG and -IgA antibodies. Interactions were detected by fluorescence detection. Strong Ig interactions encompassing consecutive peptides were considered to represent "high-fidelity regions" (HFRs). These were mapped to the coronavirus proteomes using a 60% homology threshold for clustering. Results: We identified 333 human coronavirus derived HFRs. Among these, 98 (29%) mapped to SARS-CoV-2, 144 (44%) mapped to one or more of the four circulating common cold coronaviruses (CCC), and 54 (16%) cross-mapped to both SARS-CoV-2 and CCCs. The remaining 37 (11%) mapped to either SARS-CoV or MERS-CoV. Notably, the COVID-19 serum was skewed towards recognising SARS-CoV-2-mapped HFRs, whereas the pre-pandemic was skewed towards recognising CCC-mapped HFRs. In terms of absolute numbers of linear B cell epitopes, the primary targets are the ORF1ab protein (60%), the spike protein (21%), and the nucleoprotein (15%) in that order; however, in terms of epitope density, the order would be reversed. Conclusion: We identified linear B cell epitopes across coronaviruses, highlighting pan-, alpha-, beta-, or SARS-CoV-2-corona-specific B cell recognition patterns. These findings could be pivotal in deciphering past and current exposures to epidemic and endemic coronavirus. Moreover, our results suggest that pre-pandemic anti-CCC antibodies may cross-react against SARS-CoV-2, which could explain the highly variable outcome of COVID-19. Finally, the methodology used here offers a rapid and comprehensive approach to high-resolution linear B-cell epitope mapping, which could be vital for future studies of emerging infectious diseases. Methods Peptide microarray design Peptide microarrays were designed using the proteomes of the seven human coronaviruses (HcoVs): · HCoV-229E: 7 proteins · HCoV-HKU1(N1): 8 proteins · HCoV-NL63: 6 proteins · HCoV-OC43: 9 proteins · MERS-CoV: 9 proteins · SARS-CoV: 14 proteins · SARS-CoV-2: 13 proteins The open reading frame (ORF) 1a was excluded from all the HCoV proteomes since the ORF1ab covered these sequences. As a positive control pathogen, the entire proteome of the human cytomegalovirus (HCMV, strain AD169), consisting of 190 proteins, was included together with the entire proteome of the Zaire Ebola virus (strain Mayinga-76, EBOZM), consisting of 9 proteins. These 265 protein sequences were represented as 13 amino acid long peptides, overlapping by 11 amino acids and tiling by 2 amino acids. Leading to total of 66581 non-redundant virus-derived peptide sequences. As a source of background-binding control, 3900 non-overlapping 13 amino acid peptide sequences were generated in silico using the amino acid frequencies from the 265 virus-derived proteins. Peptide microarray synthesis and probing The 66581 virus-derived peptide sequences in triplicate and the 3900 random background-binding peptides in duplicate were distributed randomly across 12 virtual sectors using proprietary software (PepArray, Schafer-N). Peptides were synthesised by Schafer-N (Copenhagen) on amino-functionalized glass microscope slides using a maskless photolithographic light-directed solid-phase peptide synthesis. Peptide microarrays were incubated with convalescent COVID-19 or pre-pandemic sera diluted 1:100 in PBS (0.1% BSA, 0.1% Triton X-100) for 2 hours at room temperature, followed by washing and development using 1 µg/mL Cy3 or Cy5-labelled secondary antibodies against human IgG or IgA, respectively. After washing, microarrays were dried and scanned on a microarray laser scanner (INNOSCAN 900, Innopsys, France) and then quantified at an 8-bit resolution and purged of artefacts using proprietary PepArray software. Usage notes Aggregated microarray data for all samples: microarray_data_aggregated.txt Fluorescence intensity values for each peptide on the microarray are found in an aggregated tab-separated file format. The first column contains the synthesised peptide sequences, and the second column contains the peptide group: · Test: Peptides derived from any one of the 9 virus strains · Random: The random peptides The remaining four columns contain the fluorescence intensity values extracted for the COVID-19 convalescent (pandemic) serum pool IgA and IgG and the pre-pandemic serum pool IgA and IgG. Peptide to protein mapper file: peptide_map.txt The peptide parent protein names and their locations are in a tab-separated file format. The first column contains the synthesised peptide sequences, the second column contains the organism name, the third column is the UniProt ID of their parent proteins, the fourth column is the abbreviated protein name, the fifth column is the length of the parent proteins, the sixth and seventh columns are the start and end coordinates of the peptides in their parent protein. Combining files: microarray_data_aggregated.txt and peptide_map.txt can be combined using the "coresequence" column, representing the peptide sequences.

The Swedish approach to managing the COVID-19 pandemic has received significant attention in international scholarly work and the press. For this dataset, we have reviewed governmental and media archives to build a detailed timeline that chronicles significant policies, interventions, and events in the Swedish management of COVID-19. The dataset contains summary descriptions of what took place, when it happened, and who the principal actors involved were. Links to primary sources are provided for each entry. Because of the level of detail and saturation, the dataset offers a detailed account of Swedish pandemic governance and will benefit anyone working on Swedish pandemic management or doing comparative work between Sweden and other jurisdictions.

The dataset contains details on the date an event took place (column 1), tags to facilitate navigation (column 2), details on the principal actors involved in the event (column 3), a summary description of what took place and who was involved (column 4), and links to primary materials (e.g., archival entries) (columns 4-12). Through a structured and detailed outline, the dataset provides a saturated account of policy interventions and events in Sweden during the COVID-19 pandemic for the period 2020-2023 until it was no longer considered a public health emergency of international concern by the WHO and complements existing and less detailed timelines published at earlier points in the period.

The dataset contains temperature measurements on the surface of the face taken on 109 people. Each patient (identified by Patient ID in the dataset) acclimatized in a room with a temperature of 22-24 degrees Celsius. Then the person completed a survey, during which they provided their: • age (column Survey - age [years]), • gender (column Survey - Gender), • temperature measurement using a pyrometer thermometer (column Survey - temperature [°C]), • and pulse measurement using a pulse oximeter (column Survey - measured pulse [BPM]). After that, the examined person stood in front of the contactless temperature measurement system (using a thermal camera), which was continuously calibrated to the black body at a distance of 1.5-3 meters (column Distance between camera and patient [m]). Then, several hundred temperature measurements were taken on each person in the following ways: • Median temperature on face [°C] • Median temperature on face, 1% of pixels with max temperature [°C] • Median temperature on face, 5% of pixels with max temperature [°C] • Median temperature on face, 10% of pixels with max temperature [°C] • Median temperature in the center of the eyes (3x3 pixels) [°C] • Median temperature measured at the corners of the eyes (3x3 pixels) [°C] Additionally, the system automatically estimated: • the age of the examined person (column Estimated Age [years]), • the pulse of the examined person (column Estimated Pulse [BPM]), • and gender (Estimated Gender). According to [1], the measured temperature on the surface of the face is influenced by the age of the measured person. As part of the project, a Binary Regression Tree was developed, which considers (estimated) age when calculating the temperature on the surface of the face (column Temperature calculated by Binary Tree Regression algorithm [°C]). [1] Cheung, Ming & Chan, Lung & Lauder, I & Kumana, Cyrus. (2012). Detection of body temperature with infrared thermography: accuracy in detection of fever. Hong Kong medical journal = Xianggang yi xue za zhi / Hong Kong Academy of Medicine. 18 Suppl 3. 31-4.

Abstract (our paper)

The spread of COVID-19, the so-called new coronavirus, is currently having an enormous social and economic impact on the entire world. Under such a circumstance, the spread of information about the new coronavirus on SNS is having a significant impact on economic losses and social decision-making. In this study, we investigated how the new type of coronavirus has become a social topic in Japan, and how it has been discussed. In order to determine what kind of impact it had on people, we collected and analyzed Japanese tweets containing words related to the new corona on Twitter. First, we analyzed the bias of users who tweeted. As a result, it is clear that the bias of users who tweeted about the new coronavirus almost disappeared after February 28, 2020, when the new coronavirus landed in Japan and a state of emergency was declared in Hokkaido, and the new corona became a popular topic. Second, we analyzed the emotional words included in tweets to analyze how people feel about the new coronavirus. The results show that the occurrence of a particular social event can change the emotions expressed on social media.

Data

Tweets_YYYY-MM-DD.tsv.gz:
The first column is the tweet id, the second column is the date and time (JST) when the tweet was posted, the third column is the flag as to whether the tweet was used for emotion analysis or not, and the fourth column is the tweet id of the retweet source.
This data was collected by giving the query "新型肺炎 OR 武漢 OR コロナ OR ウイルス OR ウィルス" to the Twitter Search API. Therefore, most of the tweets are Japanese tweets.
We conducted emotion analysis on tweets, excluding retweets and tweets containing links. The fourth column is empty if the tweet is not a retweet.

KL-Divergence.tsv.gz:
The first column is the date (JST), and the second column is the value of KL-Divergence that calculated the bias of the users who posted tweets related to COVID-19.
The value of KL-Divergence was calculated with all users appearing in Tweets_YYYY-MM-DD.tsv.gz. Based on the sampling stream data, we determined that if the value is below 0.6, there is no bias.

Emotions_by_ML-Ask.tsv.gz:
The first column is the date (JST), the second and subsequent columns are the number of tweets for each emotion, and the last column is the number of tweets analyzed for the day.
For this analysis, we only used tweets with a value of 1 in the third column of Tweets_YYYY-MM-DD.tsv.gz. We used pymlask (Python implementation of ML-Ask) to estimate the emotion of the tweet.

Publication

This data set was created for our study. If you make use of this data set, please cite:
Fujio Toriumi, Takeshi Sakaki, Mitsuo Yoshida. Social Emotions Under the Spread of COVID-19 Using Social Media. Transactions of the Japanese Society for Artificial Intelligence (in Japanese). vol.35, no.4, pp.F-K45_1-7, 2020.
鳥海不二夫, 榊剛史, 吉田光男. ソーシャルメディアを用いた新型コロナ禍における感情変化の分析. 人工知能学会論文誌. vol.35, no.4, pp.F-K45_1-7, 2020.
https://doi.org/10.1527/tjsai.F-K45

For English, see below This file contains, in addition to a column with the version number and a column with the date of creation of the file, the following characteristics per sampled sewage treatment plant (WWTP) in the Netherlands: Date of sample, WWTP code, WWTP name, Virus load per 100,000 inhabitants The file is structured as follows: For each treatment plant, a sample is taken from the sewage water for 24 hours. These samples are analyzed by RIVM researchers for the number of virus particles present. A record contains the average number of virus particles in the sewage water for each waste/sewage treatment plant (WWTP) sampled, corrected for the daily amount of sewage water (flow rate) and shown per 100,000 inhabitants. The file is refreshed from Monday to Friday (before 2:00 PM). The information on population numbers per WWTP can be found in a turnover table, which is supplied by Statistics Netherlands (CBS). (The version for 2021:) (https://www.cbs.nl/nl-nl/maatwerk/2021/06/inwoners-per-sewage treatment plant-1-1-2021) (The version for 2022:) (https: //www.cbs.nl/nl-nl/maatwerk/2022/42/inwoners-per-sewage treatment plant-1-1-2022) As of March 4, 2021, a number of changes have been implemented for the WWTPs below. - As of October 8, 2020, WWTP Aalst has been closed. The associated catchment area has been added to that of WWTP Zaltbommel. The values ​​for the RNA_flow_per_100000 for Zaltbommel have been changed in the database from March 4, 2021 with retroactive effect to the aforementioned date of suspension. For the values ​​reported before the closure date, the individual population numbers for WWTP Aalst and WWTP Zaltbommel that applied before the closure of WWTP Aalst were used. - As of December 9, 2020, WWTP Lienden has been closed. The associated catchment area has been added to that of WWTP Tiel. The values ​​for RNA_flow_per_100000 for WWTP Tiel have been changed in the database from March 4, 2021 with retroactive effect to the above-mentioned lifting date. For the values ​​reported before the closure date, the individual population numbers for WWTP Lienden and WWTP Tiel that applied before the closure of WWTP Lienden were used. Changes from 1 January 2021 have been incorporated in the CBS turnover table. From September 30, 2021, changes in the CBS turnover table will be processed as soon as they become known. As of September 30, the column RNA_per_ml has been removed from the open data file. Values ​​reported in this column have been converted to RNA_flow_per_100000 and reported in that column where possible. In addition, all values ​​for 2021 and 2020 were retroactively recalculated on September 30, 2021 with the population numbers in the CBS table published on September 30, 2021. All values ​​for 2022 have been retroactively recalculated on December 30, 2022 using the CBS table published on October 19, 2022. Description of the variables: Version: Version number of the dataset. When the content of the dataset is structurally changed (so not the daily update or a correction at record level), the version number will be adjusted (+1) and also the corresponding metadata in RIVMdata (data.rivm.nl). Date_of_report: Date on which the file was created. (format: yyyy-mm-dd) Date_measurement: Date on which the sampling of the 24-hour influent (raw waste/sewage) sample started (format: yyyy-mm-dd). WWTP_WWTP_code: Code of sewage treatment plant (WWTP) or waste water treatment plant (WWTP). WWTP_WWZI_name: Name of sewage treatment plant (WWTP) or waste water treatment plant (WWTP). RNA_flow_per_100000: The average concentration of SARS-CoV-2 RNA, converted to daily amount of sewage (flow rate) and displayed per 100,000 inhabitants. -------------------------------------------------- --------------------------------------------- Covid-19 National SARS-CoV-2 sewage surveillance This file contains, in addition to a column with the version number and a column with the date of creation of the file, the following characteristics per sampled sewage treatment plant (STP) in the Netherlands: Sample date, STP code, STP name, Virus load per 100,000 inhabitants The file is structured as follows: A sample of the sewage water is taken for 24 hours per treatment plant. These samples are analyzed by RIVM researchers for the number of virus particles present. A record contains the average number of virus particles in the sewage water for each waste/sewage treatment plant (STP) sampled, corrected for the daily amount of sewage water (flow rate) and shown per 100,000 inhabitants. The file is refreshed from Monday to Friday (before 2:00 PM). The information on population numbers per STP can be found in a conversion table, which is supplied by Statistics Netherlands (CBS). (The version for 2021:) (https://www.cbs.nl/nl-nl/maatwerk/2021/06/inwoners-per-rioolwaterzuiveringsinstallatie-1-1-2021) (The version for 2022:) (https://www.cbs.nl/nl-nl/maatwerk/2022/42/inwoners-per-rioolwaterzuiveringsinstallatie-1-1-2022) As of March 4, 2021, a number of changes have been implemented for the STPs below. - As of October 8, 2020, STP Aalst has been closed. The associated catchment area has been added to the STP of Zaltbommel. The values for the RNA_flow_per_100000 for Zaltbommel have been changed in the database from March 4, 2021 retroactively to the aforementioned date of suspension. For the values reported before the closure date, the individual population numbers for STP Aalst and STP Zaltbommel that applied before the closure of STP Aalst were used. - As of December 9, 2020, STP Lienden has been closed. The associated catchment area has been added to the STP of Tiel. The values for RNA_flow_per_100000 for STP Tiel have been changed in the database from March 4, 2021 retroactively to the aforementioned date of suspension. For the values reported before the closure date, the individual population numbers for STP Lienden and STP Tiel that applied before the closure of STP Lienden were used. Changes from 1 January 2021 have been incorporated in the CBS conversion table. From September 30, 2021, changes in the CBS conversion table will be processed as soon as they become known. As of September 30, the column RNA_per_ml has been removed from the open data file. Values reported in this column have been converted to RNA_flow_per_100000 and reported in that column where possible. In addition, all values for 2021 and 2020 were retroactively recalculated on September 30, 2021 with the population numbers in the CBS table published on September 30, 2021. All values for 2022 have been retroactively recalculated on December 30, 2022 using the CBS table published on October 19, 2022. Description of the variables: Version: Version number of the dataset. When the content of the dataset is structurally changed (so not the daily update or a correction at record level), the version number will be adjusted (+1) and also the corresponding metadata in RIVMdata (data.rivm.nl). Date_of_report: Date on which the file was created. (format: yyyy-mm-dd) Date_measurement: Date on which the sampling of the 24-hour influent (raw waste/sewage) sample started (format: yyyy-mm-dd). RWZI_AWZI_code: Code of sewage treatment plant (RWZI in Dutch abbreviation) or waste water treatment plant (AWZI in Dutch abbreviation). RWZI_AWZI_name: Name of sewage treatment plant (RWZI in Dutch abbreviation) or waste water treatment plant (AWZI in Dutch abbreviation). RNA_flow_per_100000: The average concentration of SARS-CoV-2 RNA, converted to daily amount of sewage (flow rate) and displayed per 100,000 inhabitants.

This data focused on the Indonesian government's global pandemic policies mapping from 2000-2023. Indonesia has been affected by some diseases such as H1N1, H5N1, SARS-Cov-1, and the current SARS-CoV-2 (Covid-19). This data will measure Indonesian health policy mapping based on its capability to adapt to the local context, construct a care delivery value chain, leverage shared delivery infrastructure, and improve health delivery and economic development. We perform feasibility analysis with a method scoring system to the fourth Pillar above.

According to Cognitive Market Research, the global Universal Testing Machine market size will be USD 451.5 million in 2024. It will expand at a compound annual growth rate (CAGR) of 5.00% from 2024 to 2031.

North America held the major market share for more than 40% of the global revenue with a market size of USD 180.60 million in 2024 and will grow at a compound annual growth rate (CAGR) of 3.2% from 2024 to 2031.
Europe accounted for a market share of over 30% of the global revenue with a market size of USD 135.45 million in 2024 and will grow at a compound annual growth rate (CAGR) of 3.5% from 2024 to 2031.
Asia Pacific held a market share of around 23% of the global revenue with a market size of USD 103.85 million in 2024 and will grow at a compound annual growth rate (CAGR) of 7.0% from 2024 to 2031.
Latin America had a market share of more than 5% of the global revenue with a market size of USD 22.58 million in 2024 and will grow at a compound annual growth rate (CAGR) of 4.4% from 2024 to 2031.
Middle East and Africa had a market share of around 2% of the global revenue and was estimated at a market size of USD 9.03 million in 2024 and will grow at a compound annual growth rate (CAGR) of 4.7% from 2024 to 2031.
The Four Column Testing Machines held the highest Universal Testing Machine market revenue share in 2024.

Market Dynamics of Universal Testing Machine Market

Key Drivers for Universal Testing Machine Market

Advancements in material testing to propel market growth

The market for universal testing machines (UTMs) is expanding due in large part to developments in material testing. Sophisticated testing techniques are required to guarantee the performance, durability, and safety of novel materials, like composites, polymers, and advanced alloys, which are constantly being developed. Modern UTMs with digital controls and automation, among other features, are becoming more and more necessary for precise and effective material characterization. New developments in UTM capabilities, like increased accuracy, wider testing ranges, and data analysis software integration, improve their usefulness in a variety of industries. UTM use has increased as a result of these developments, which allow producers to comply with strict quality standards and legal obligations. The need for sophisticated material testing solutions will drive the UTM market forward as industries change.

Increasing demand for the automotive industry to propel market growth

The market for universal testing machines (UTMs) is mostly driven by the expansion of the automobile sector. The need for lightweight and high-strength materials has increased as manufacturers work to increase performance, safety, and fuel economy. In order to make sure these materials fulfill strict standards and performance requirements, UTMs are essential for testing. This need is accelerated by the development of electric vehicles (EVs) since the longevity and dependability of EV components must be thoroughly tested. Further driving technology improvements, such as autonomous driving systems, necessitate accurate material testing for sensors and other vital parts. Thus, the UTM market is growing as a result of the automobile industry's constant evolution and its emphasis on innovation and safety regulations.

Restraint Factor for the Universal Testing Machine Market

High initial investment costs to hinder market growth

The market for universal testing machines (UTMs) is severely constrained by high initial costs. Advanced UTMs might come with hefty acquisition prices because they come equipped with complex technology, including automation, digital controls, and increased precision. Small and medium-sized businesses (SMEs), which can find it difficult to set aside the funds required for such equipment, are especially affected by this financial obstacle. Further discouraging prospective purchasers is the cost of necessary updates, calibration, and routine maintenance, which raises the entire cost burden. The high initial expenses of owning a UTM, even with its long-term advantages, can hinder market penetration and adoption rates, particularly in developing and cost-sensitive businesses. To stimulate broader usage of UTMs, creative finance methods, and government backing are required to overcome this financial obstacle.

Impact of Covid-19 on the Universal Testing Machine Market

The market for universal testing mach...