As global communities responded to COVID-19, we heard from public health officials that the same type of aggregated, anonymized insights we use in products such as Google Maps would be helpful as they made critical decisions to combat COVID-19. These Community Mobility Reports aimed to provide insights into what changed in response to policies aimed at combating COVID-19. The reports charted movement trends over time by geography, across different categories of places such as retail and recreation, groceries and pharmacies, parks, transit stations, workplaces, and residential.

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 the first reported coronavirus case in Washington State on Jan. 21, 2020, 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.

Daily COVID-19 reports from Johns Hopkins University Center for Systems Science and Engineering. This dataset is generated by calculating differences of each cumulative daily report from the previous day to identify daily changes in the number of confirmed, active, recovered, and fatal cases. This dataset reports from after CSSE changed its daily report schema on March 22.

The presented cross-sectional dataset can be employed to analyze the governmental, trade, and competitiveness relationships of official COVID-19 reports. It contains 18 COVID-19 variables generated based on the official reports of 138 countries, as well as an additional 2163 governance, trade, and competitiveness indicators from the World Bank Group GovData360 and TCdata360 platforms in a preprocessed form. The current version was compiled on July 27, 2020. Note that this version uses 20-40-60-80-day time windows and the first test data are based on the first country reports on tests.

Please cite as: • Kurbucz, M. T. (2020). A Joint Dataset of Official COVID-19 Reports and the Governance, Trade and Competitiveness Indicators of World Bank Group Platforms. Data in Brief, 105881.

Data generation: • Data generation (data_generation. Rmd): Datasets were generated with this R Notebook. It can be used to update datasets and customize the data generation process.

Datasets: • Country data (country_data.txt): Country data. • Metadata (metadata.txt): The metadata of selected GovData360 and TCdata360 indicators. • Joint dataset (joint_dataset.txt): The joint dataset of COVID-19 variables and preprocessed GovData360 and TCdata360 indicators. • Correlation matrix (correlation_matrix.txt): The Kendall rank correlation matrix of the joint dataset.

Raw data of figures and tables: • Raw data of Fig. 2 (raw_data_fig2.txt): The raw data of Fig. 2. • Raw data of Fig. 3 (raw_data_fig3.txt): The raw data of Fig. 3. • Raw data of Table 1 (raw_data_table1.txt): The raw data of Table 1. • Raw data of Table 2 (raw_data_table2.txt): The raw data of Table 2. • Raw data of Table 3 (raw_data_table3.txt): The raw data of Table 3.

For English, see below The number of COVID-19 related hospitalizations has been low for quite some time and COVID-19 is no longer a notifiable disease as of July 1, 2023. Therefore, the data will no longer be updated from July 11, 2023. The reproduction number R gives the average number of people infected by one person with COVID-19. To estimate this reproduction number, we use the number of reported COVID-19 hospital admissions per day in the Netherlands. This number of hospital admissions is tracked by the NICE Foundation (National Intensive Care Evaluation). Because a COVID-19 admission is passed on with some delay in the reporting system, we correct the number of admissions for this delay [1]. The first day of illness is known for a large proportion of the reported cases. This information is used to estimate the first day of illness for hospital admissions. By displaying the number of COVID-19 admissions per date of the first day of illness, it is immediately possible to see whether the number of infections is increasing, peaking or decreasing. For the calculation of the reproduction number, it is also necessary to know the length of time between the first day of illness of a COVID-19 case and the first day of illness of his or her infector. This duration is an average of 4 days for SARS-CoV-2 variants in 2020 and 2021, and an average of 3.5 days for more recent variants, calculated on the basis of COVID-19 reports to the GGD. With this information, the value of the reproduction number is calculated as described in Wallinga & Lipsitch 2007 [2]. Until June 12, 2020, the reproduction number was calculated on the basis of COVID-19 hospital admissions, and until March 15, 2023, the reproduction number was calculated on the basis of COVID-19 reports to the GGDs. [1] van de Kassteele J, Eilers PHC, Wallinga J. Nowcasting the Number of New Symptomatic Cases During Infectious Disease Outbreaks Using Constrained P-spline Smoothing. Epidemiology. 2019;30(5):737-745. doi:10.1097/EDE.0000000000001050. [2] Wallinga J, Lipsitch M. How generation intervals shape the relationship between growth rates and reproductive numbers. Proc Biol Sci. 2007;274(1609):599-604. doi:10.1098/rspb.2006.3754. 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 (https://data.rivm.nl) . Version 2 update (February 8, 2022): - In the calculation of the reproduction number, the date of the positive test result is now used instead of the GGD notification date. Version 3 update (February 17, 2022): - The calculation of the reproduction number now takes into account different generation times for different variants. For the variants up to and including Delta, the average generation time is 4 days, from Omikron it is 3.5 days. The reproduction number published here is a weighted average of the reproduction numbers per variant. Version 4 update (September 1, 2022): - From September 1, 2022, this dataset is split into two parts. The first part contains the dates from the start of the pandemic to October 3, 2021 (week 39) and contains "tm" in the file name. This data will no longer be updated. The second part contains the data from October 4, 2021 (week 40) and is updated every Tuesday and Friday. - Until August 31, the published reproduction number was calculated with the data of the day before publication. From September 1, the published reproduction number is calculated with the data of the day of publication. Version 5 update (March 31, 2023): - From March 15, 2023, the reproduction number is calculated based on COVID-19 hospital admissions according to the NICE hospital registration. From June 13, 2020 to March 14, 2023, the reproduction number was calculated on the basis of COVID-19 reports to the GGD. However, the number of reports is strongly determined by the test policy, and is less suitable as a basis for calculating the reproduction number due to the adjusted test policy as of March 10, 2023 and the closure of the GGD test lanes as of March 17, 2023. Until 12 June 2020, the reproduction number was also calculated on the basis of hospital admissions, but then as reported to the GGD. Date: Date for which the reproduction number was estimated Rt_low: Lower bound 95% confidence interval Rt_avg: Estimated reproduction number Rt_up: Upper bound 95% confidence interval population: patient population with value “hosp” for hospitalized patients or “testpos” for test positive patients For recent R estimates, the reliability is not great, because the reliability depends on the time between infection and becoming ill and the time between becoming ill and reporting. Therefore, the variable Rt_avg is absent in the last two weeks. -------------------------------------------------- --------------------------------------------- Covid-19 reproduction number The number of COVID-19 related hospitalizations has been low for quite some time and COVID-19 is no longer a notifiable disease as of July 1, 2023. Therefore, the data will no longer be updated from July 11, 2023. The reproduction number R gives the average number of people infected by one person with COVID-19. To estimate this reproduction number, we use the number of reported COVID-19 hospital admissions per day in the Netherlands. This number of hospital admissions is tracked by the NICE Foundation (National Intensive Care Evaluation). Because a COVID-19 admission is reported with some delay in the reporting system, we correct the number of admissions for this delay [1]. The first day of illness is known for a large proportion of the reported cases. This information is used to estimate the first day of illness for hospital admissions. By displaying the number of COVID-19 admissions per date of the first day of illness, it is immediately possible to see whether the number of infections is increasing, peaking or decreasing. To calculate the reproduction number, it is also necessary to know the length of time between the first day of illness of a COVID-19 case and the first day of illness of his or her infector. This duration is an average of 4 days for SARS-CoV-2 variants in 2020 and 2021, and an average of 3.5 days for more recent variants, calculated on the basis of COVID-19 reports to the PHS. With this information, the value of the reproduction number is calculated as described in Wallinga & Lipsitch 2007 [2]. Until June 12, 2020, the reproduction number was calculated on the basis of COVID-19 hospital admissions, and until March 15, 2023, the reproduction number was calculated on the basis of COVID-19 reports to the GGDs. [1] van de Kassteele J, Eilers PHC, Wallinga J. Nowcasting the Number of New Symptomatic Cases During Infectious Disease Outbreaks Using Constrained P-spline Smoothing. Epidemiology. 2019;30(5):737-745. doi:10.1097/EDE.0000000000001050. [2] Wallinga J, Lipsitch M. How generation intervals shape the relationship between growth rates and reproductive numbers. Proc Biol Sci. 2007;274(1609):599-604. doi:10.1098/rspb.2006.3754. 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 (https://data.rivm.nl). Version 2 update (February 8, 2022): - In the calculation of the reproduction number, the date of the positive test result is now used instead of the PHS notification date. Version 3 update (February 17, 2022): - The calculation of the reproduction number now takes into account different generation times for different variants. For the variants up to and including Delta, the average generation time is 4 days, from Omikron it is 3.5 days. The reproduction number published here is a weighted average of the reproduction numbers per variant. Version 4 update (September 1, 2022): - As of September 1, 2022, this dataset is split into two parts. The first part contains the dates from the start of the pandemic till October 3, 2021 (week 39) and contains "tm" in the file name. This data will no longer be updated. The second part contains the data from October 4, 2021 (week 40) and is updated every Tuesday and Friday. - Until August 31, the published reproduction number was calculated with the data of the day before publication. From September 1, the published reproduction number is calculated with the data of the day of publication. Version 5 update (March 31, 2023): - As of March 15, 2023, the reproduction number is calculated based on COVID-19 hospital admissions according to the NICE hospital registry. From June 13, 2020 to March 14, 2023, the reproduction number was calculated on the basis of COVID-19 reports to the PHS. However, the number of reports is strongly determined by the test policy, and is less suitable as a basis for calculating the reproduction number due to the adjusted test policy as of March 10, 2023 and the closure of the PHS test lanes as of March 17, 2023. Until 12 June 2020, the reproduction number was also calculated on the basis of hospital admissions, but then as reported to the PHS. Date: Date for which the reproduction number was estimated Rt_low: Lower limit 95% confidence interval Rt_avg: Estimated reproduction number Rt_up: Upper bound 95% confidence interval population: patient population with value “hosp” for hospitalized patients or “testpos” for test positive patients For recent R estimates, the reliability is not great, because the reliability depends on the time between infection and becoming ill and the time between becoming ill and reporting. Therefore, the variable Rt_avg is absent in the last two weeks.

As of May 2, 2023, the outbreak of the coronavirus disease (COVID-19) had been confirmed in almost every country in the world. The virus had infected over 687 million people worldwide, and the number of deaths had reached almost 6.87 million. The most severely affected countries include the U.S., India, and Brazil.

COVID-19: background information COVID-19 is a novel coronavirus that had not previously been identified in humans. The first case was detected in the Hubei province of China at the end of December 2019. The virus is highly transmissible and coughing and sneezing are the most common forms of transmission, which is similar to the outbreak of the SARS coronavirus that began in 2002 and was thought to have spread via cough and sneeze droplets expelled into the air by infected persons.

Naming the coronavirus disease Coronaviruses are a group of viruses that can be transmitted between animals and people, causing illnesses that may range from the common cold to more severe respiratory syndromes. In February 2020, the International Committee on Taxonomy of Viruses and the World Health Organization announced official names for both the virus and the disease it causes: SARS-CoV-2 and COVID-19, respectively. The name of the disease is derived from the words corona, virus, and disease, while the number 19 represents the year that it emerged.

The presented cross-sectional dataset can be employed to analyze the governmental, trade, and competitiveness relationships of official COVID-19 reports. It contains 18 COVID-19 variables generated based on the official reports of 138 countries, as well as an additional 2163 governance, trade, and competitiveness indicators from the World Bank Group GovData360 and TCdata360 platforms in a preprocessed form. The current version was compiled on July 27, 2020. Note that this version uses 20-40-60-80-day time windows and the first test data are based on the first country reports on tests.

Please cite as: • Kurbucz, M. T. (2020). A Joint Dataset of Official COVID-19 Reports and the Governance, Trade and Competitiveness Indicators of World Bank Group Platforms. Data in Brief, 105881. • Kurbucz, M. T., Katona, A. I., Lantos, Z., & Kosztyán, Z. T. (2021). The role of societal aspects in the formation of official COVID-19 reports: A data-driven analysis. International journal of environmental research and public health, 18(4), 1505. • Kurbucz, M. T. (2022). Modeling the social determinants of official COVID-19 reports in the early stages of the pandemic. Journal of Applied Social Science, 16(1), 356-363.

Data generation: • Data generation (data_generation. Rmd): Datasets were generated with this R Notebook. It can be used to update datasets and customize the data generation process.

Datasets: • Country data (country_data.txt): Country data. • Metadata (metadata.txt): The metadata of selected GovData360 and TCdata360 indicators. • Joint dataset (joint_dataset.txt): The joint dataset of COVID-19 variables and preprocessed GovData360 and TCdata360 indicators. • Correlation matrix (correlation_matrix.txt): The Kendall rank correlation matrix of the joint dataset.

Raw data of figures and tables: • Raw data of Fig. 2 (raw_data_fig2.txt): The raw data of Fig. 2. • Raw data of Fig. 3 (raw_data_fig3.txt): The raw data of Fig. 3. • Raw data of Table 1 (raw_data_table1.txt): The raw data of Table 1. • Raw data of Table 2 (raw_data_table2.txt): The raw data of Table 2. • Raw data of Table 3 (raw_data_table3.txt): The raw data of Table 3.

The corpus is a collection of news reports on COVID 19 reported on Bangkok Post Online from November 1, 2019 to April 30, 2020.

This dataset contains all information to reproduce our experiment to produce a high-resolution global map (0.1°) of infection-rate risk for COVID-19, based on temperature, precipitation, and CO2.

The produced risk index map predicts most of the areas with an actual high risk (87% accuracy), which are characterized by a moderate-high level of CO2, moderate-low temperatures, and a moderate level of precipitation. With respect to our previous model (https://zenodo.org/record/3945495#.YG7UEugzaUk) - which had a coarser 0.5° resolution - this new model is much more accurate at predicting real-world scenarios that reported both high and low infection rates in 2020 (80% accuracy).

Explanation of data and images:

comparisonvert.png -> Visualisation of the output produced by our model: (a) distribution of high-infection-rate areas using the MaxEnt balanced threshold (0.008), (b) probability peak areas (0.13 threshold), (c) overlap between low infection rate countries extracted from real data and our risk map, and (d) highlight of low infection rate countries not predicted by our model countries_high_rate.csv-> high-infection-rate countries countries_low_rate.csv-> low-infection-rate countries countries_low_rate_mispredicted.csv-> low-infection-rate countries mispredicted by our model covid_derivatives.csv-> extracted average derivatives of world countries

covid_risk.csv->Risk Map dataset gp.asc-> MaxEnt distribution raster LowDerivativeRegions.png->low-infection-rate countries - image MaxEnt distribution.png->distribution of high-infection-rate areas using the MaxEnt balanced threshold (0.008) - image MaxEnt peaks.png-> MaxEnt probability peak areas (0.13 threshold) Precipitation.png->Average precipitation 2000-2005 RiskMap.png-> New high-infection-rate risk map based on a 0.1° resolution MaxEnt model RiskMap05.png->our previous risk map based on a 0.5° resolution MaxEnt model riskmapcomparison.png-> Visual comparison between (a) our new high-infection-rate risk map based on a 0.1° resolution MaxEnt model and (b) our previous risk map based on a 0.5° resolution MaxEnt model. RiskMapOverlap_mispredicted.png->highlight of low infection rate countries not predicted by our model Temperature.png->Average Surface Air Temperature 2000-2005 time_series_covid19_confirmed_global.csv->World COVID-19 reports up to April 2021

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

Données France entière par d'activité (selon la nomenclature agrégée en 88 postes de la NAF) relatives aux reports de cotisations des employeurs affiliés au régime général dans le cadre la crise sanitaire de la Covid 19 (échéances du 15 mars au 15 décembre 2020). Situations en fin de mois de mars à décembre 2020. Afin de tenir compte de l’impact de l’épidémie de coronavirus sur l’activité économique, le réseau des Urssaf a déclenché des mesures exceptionnelles pour accompagner les entreprises présentant de sérieuses difficultés de trésorerie à compter de l'échéance du 15 mars. En cas de difficultés majeures, les entreprises pouvaient ainsi reporter, d'abord sans demande préalable puis sur demande, tout ou partie du paiement des cotisations salariales et patronales.Ce jeu de données décrit l'ensemble des montants reportés, qu'ils s'inscrivent dans le cadre d'un dispositif autorisant le report ou non. Les montants de reports correspondent donc aux "restes à recouvrer".Les données sont déclinées par échéance de paiement : le 5 ou 15 du mois. Les cotisations doivent en principe être payées au cours du mois suivant la période d’emploi rémunérée :au plus tard le 5 de ce mois pour les employeurs d’au moins 50 salariés et dont la paie est effectuée au cours du même mois que la période de travail ;au plus tard le 15 de ce mois dans les autres cas.Source : Acoss-Urssaf, extraction début mai 2021Indicateurs :Nombre d'établissements à l'échéance ()Montant des cotisations duesNombre d'établissements ayant fait un report ()Montant des reports(*) AVERTISSEMENT : l'information sur le nombre d'établissements doit être interprété avec prudence. En effet, les établissements étant dénombrés à chaque échéance, la sélection d'une période couvrant plus d'un mois conduit à compter plusieurs fois les mêmes établissements (un établissement est susceptible de faire une déclaration chaque mois). Ainsi, pour disposer du nombre total d'établissements sans doubles comptes, il convient de sélectionner deux échéances d'un même mois. Précisions méthodologiques :ces données prennent en compte les dispositifs d'exonération de cotisations sociales et d'aide au paiement introduits par l'article 65 de la loi n° 2020-935 du 30 juillet 2020 pour soutenir les entreprises les plus impactées par la crise. Les cotisations dues sont en effet des montants après application des exonérations. Et les reports sont des montants après imputation de l'aide au paiement, celle-ci venant réduire les montants à payer par les entreprises concernées.un établissement est comptabilisé comme ayant fait l'objet d'un report dès lors que le montant de cotisations non payées à l'échéance dépasse 44€le secteur d'activité "nca non classé ailleurs" regroupe le secteur de l'agriculture (AZ) pour ce qui concerne le régime général (la majeure partie du secteur AZ relève du régime agricole, hors champ ici), les activités extra-territoriales (UZ) et les activités inconnues. DATAVIZ : mise en perspective

Full France data by activity (according to the aggregated classification of 88 NAF posts) relating to the carryovers of contributions of employers affiliated to the general scheme in the context of the COVID-19 health crisis (dues from 15 March to 15 December 2020). Situations at the end of March to December 2020.

In order to take into account the impact of the coronavirus outbreak on economic activity, the Urssaf network triggered exceptional measures to support companies with serious cash flow difficulties as of the deadline of 15 March. In the event of major difficulties, companies could postpone, first without prior request and then on request, all or part of the payment of employee and employer contributions.

This dataset describes all the amounts carried over, regardless of whether they are part of a mechanism allowing the carry-over or not. The amounts of carry-overs therefore correspond to the “rests to be recovered”.

The data are declined by payment deadline: the 5th or 15th of the month. Contributions must in principle be paid during the month following the period of paid employment:

• no later than the 5th of that month for employers with at least 50 employees whose pay is paid in the same month as the period of work;

• no later than the 15th of this month in other cases.

Source: ACOSS-Urssaf, extraction early May 2021

Indicators:

• Number of institutions at maturity (*)
• Amount of contributions due
• Number of establishments that have carried over (*)
• Amount of carryovers

(*) WARNING: information on the number of establishments should be interpreted with caution. Indeed, as institutions are counted at each maturity, the selection of a period covering more than one month leads to the same institutions being counted several times (an institution is likely to report each month). Thus, in order to have the total number of institutions without double accounts, it is necessary to select two maturities of the same month.

Methodological clarifications:

• these data take into account the social contribution exemption and payment aid schemes introduced by Article 65 of Law No 2020-935 of 30 July 2020 to support the companies most affected by the crisis. The contributions due are indeed amounts after application of the exemptions. And carry-overs are amounts after the payment aid has been charged, which reduces the amounts to be paid by the undertakings concerned.
• an institution is counted as deferred if the amount of contributions not paid at maturity exceeds EUR 44
• the sector of activity “nca not elsewhere” includes the agricultural sector (AZ) for the general scheme (most of the AZ sector falls under the agricultural scheme, out of field here), extraterritorial activities (UZ) and unknown activities.

DATAVIZ: putting in perspective

BackgroundFollowing COVID-19, reports suggest Long COVID and autoimmune diseases (AIDs) in infected individuals. However, bidirectional causal effects between Long COVID and AIDs, which may help to prevent diseases, have not been fully investigated.MethodsSummary-level data from genome-wide association studies (GWAS) of Long COVID (N = 52615) and AIDs including inflammatory bowel disease (IBD) (N = 377277), Crohn’s disease (CD) (N = 361508), ulcerative colitis (UC) (N = 376564), etc. were employed. Bidirectional causal effects were gauged between AIDs and Long COVID by exploiting Mendelian randomization (MR) and Bayesian model averaging (BMA).ResultsThe evidence of causal effects of IBD (OR = 1.06, 95% CI = 1.00–1.11, p = 3.13E-02), CD (OR = 1.10, 95% CI = 1.01–1.19, p = 2.21E-02) and UC (OR = 1.08, 95% CI = 1.03–1.13, p = 2.35E-03) on Long COVID was found. In MR-BMA, UC was estimated as the highest-ranked causal factor (MIP = 0.488, MACE = 0.035), followed by IBD and CD.ConclusionThis MR study found that IBD, CD and UC had causal effects on Long COVID, which suggests a necessity to screen high-risk populations.

Introduction: The analysis of pharmacovigilance databases is crucial for the safety profiling of new and repurposed drugs, especially in the COVID-19 era. Traditional pharmacovigilance analyses–based on disproportionality approaches–cannot usually account for the complexity of spontaneous reports often with multiple concomitant drugs and events. We propose a network-based approach on co-reported events to help assessing disproportionalities and to effectively and timely identify disease-, comorbidity- and drug-related syndromes, especially in a rapidly changing low-resources environment such as that of COVID-19.Materials and Methods: Reports on medications administered for COVID-19 were extracted from the FDA Adverse Event Reporting System quarterly data (January–September 2020) and queried for disproportionalities (Reporting Odds Ratio corrected for multiple comparisons). A network (the Adversome) was estimated considering events as nodes and conditional co-reporting as links. Communities of significantly co-reported events were identified. All data and scripts employed are available in a public repository.Results: Among the 7,082 COVID-19 reports extracted, the seven most frequently suspected drugs (remdesivir, hydroxychloroquine, azithromycin, tocilizumab, lopinavir/ritonavir, sarilumab, and ethanol) have shown disproportionalities with 54 events. Of interest, myasthenia gravis with hydroxychloroquine, and cerebrovascular vein thrombosis with azithromycin. Automatic clustering identified 13 communities, including a methanol-related neurotoxicity associated with alcohol-based hand-sanitizers and a long QT/hepatotoxicity cluster associated with azithromycin, hydroxychloroquine and lopinavir-ritonavir interactions.Conclusion: Findings from the Adversome detect plausible new signals and iatrogenic syndromes. Our network approach complements traditional pharmacovigilance analyses, and may represent a more effective signal detection technique to guide clinical recommendations by regulators and specific follow-up confirmatory studies.

For English, see below From July 6, 2022, the GGD will ask a modified set of questions to people with a positive test result for SARS-CoV-2. Among other things, the setting of possible contamination will then no longer be requested. As a result, this data will no longer be updated from that moment on. This file contains the following numbers: - (number of newly reported) positively tested persons - (number of newly reported) positively tested persons for which possible setting(s) of infection have been reported - (number of newly reported) positively tested persons by possible setting of infection by safety region, as of the date on which the reports were published by RIVM. The numbers concern COVID-19 reports from December 14, 2020. From this date, data is available about the capacity of the GGDs to conduct source and contact investigations. Numbers are only shown when the coverage and completeness of source and contact tracing is high, because the numbers are then most complete. The file is structured as follows: - A record per possible setting of contamination for each security region of the Netherlands per date for which the number of reports was above zero. This file is updated every day but only data from fully completed iso weeks is displayed. On Wednesday, a new week is added to the file in its entirety. 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 RIVM data (https://data.rivm.nl ). Version 2 update (10 June 2021): - The cases with setting “hospice” have been retroactively assigned to the setting “nursing home or residential care center for the elderly” from 10 June 2021. - From 10 June 2021, for persons who have tested positive and who may have been infected in a place where they are an employee, both the workplace and the setting “work situation” will be added automatically. By way of illustration: For primary school teachers with a possible source of contamination in the work situation, the setting “school and childcare” could previously be specified and / or the setting “work situation”. Now both settings are automatically recorded. If someone's workplace does not appear in the list of settings, only the overarching setting category “work situation” is included. The frequency of the overarching setting “work situation” will therefore probably be higher from June 10, 2021 than before. Version 3 update (November 17, 2021): - Until November 17, the variables [Total_reported], [Reports_with_settings], [Setting_reported] and [Number_settings_reported] were only shown when [Source_and_contact_tracing_phase] was “high”. From [Date_of_report] November 17, 2021, for [Date_of_publication] from November 8, 2021, these variables are additionally shown when [Source_and_contact_tracing_phase] is “medium”. Version 4 update (February 8, 2022) - From February 8, 2022, the positive SARS-CoV-2 test results will be reported directly from CoronIT to RIVM. The test results of other test providers (such as Testing for Access) and healthcare institutions (such as hospitals, nursing homes and general practitioners) that enter their positive SARS-CoV-2 test results via the Reporting Portal of GGD GHOR are also reported directly to RIVM. Reports that are part of the source and contact investigation sample and positive SARS-CoV-2 test results from healthcare institutions that are reported to the GGD via healthcare email are reported to RIVM via HPZone. From 8 February, the date of the positive test result is used and no longer the date of notification to the GGD. Version 5 update (March 24, 2022): - In version 5 of this dataset, records are compiled according to the municipality reclassification of March 24, 2022. See description of the variable Security_region_code for more information. Date_of_report: Date and time on which the data file was created by RIVM. Date_of_publication: Date on which the reports were published by RIVM. Security_region_code: Security region based on the place of residence of the positively tested person. If the place of residence is not known, the Security Region is based on the GGD that submitted the report, except for the Central and West Brabant Security Region and Brabant-Noord, since the GGD and Security Region are not comparable for these regions. See also: https://www.cbs.nl/nl-nl/figures/detail/84721ENG?q=Veiliteiten From March 24, 2022, this file has been compiled according to the municipality classification of March 24, 2022. The municipality of Weesp has been merged into the municipality of Amsterdam . With this division, the Gooi- en Vechtstreek safety region has become smaller and the Amsterdam-Amstelland safety region larger; GGD Amsterdam has become larger and GGD Gooi- en Vechtstreek has become smaller (https://www.cbs.nl/nl-nl/our-services/methods/classifications/other/municipal-classifications-per-year/classification-per-year/municipal-classification-on-1-januari-2022 ). Source_and_contact_tracing_phase: The degree of coverage and completeness of source and contact tracing (hereinafter referred to as BCO phase) per safety region per isoweek (Monday to Sunday): high (high), limited (medium) or low (low). The BCO phase is reported as the lowest BCO phase of the week per safety region. RIVM receives the BCO phase per GGD per week. This information is then linked to COVID-19 reports based on the GGD where the positively tested person lives. Most safety regions correspond to the work area of ​​a GGD, except for the Central and West Brabant Safety Region (working area in GGD West Brabant and GGD Hart voor Brabant). If the BCO phase differs in a week between these two GGDs, then the lowest BCO phase of the two GGDs in this file for this security region is reported. If the BCO phase for certain GGDs has not been delivered to RIVM, “missing” is entered. The BCO phases are described as follows: High Regular source and contact investigations (BCO) are carried out in this region, with or without monitoring. The registration and analysis of infection settings is complete. Medium Risk-based BCO is performed in this region due to the high numbers of people who tested positive. The registration and analysis of infection settings is not complete. Low Due to the particularly high numbers of people who tested positive, the pressure on BCO in this region is very high. The positive result is always passed on to the person who is infected. BCO takes place to a limited extent. The registration and analysis of infection settings is not complete. As a result, this information cannot be displayed at an aggregated level. Total_reported: The number of new people reported to the GGDs with a positive test result for SARS-CoV-2 that was published by RIVM on [Date_of_publication]. Numbers are shown when [Source_and_contact_tracing_phase] is “high” and from [Date_of_publication] November 8, 2021 also when [Source_and_contact_tracing_phase] is “medium”. Suppressed numbers are replaced by the number “-9999”. The actual number of infected persons is higher than the number of reports in surveillance because not everyone with possible infection is tested. Please note: the numbers in this column cannot simply be added up, because they are repeated on a single day for all types of settings reported within a security region. If no settings are specified within a security region on a day, no record will be included in this file for this security region for this day. For the totals per safety region, use the file “COVID-19_aantallen_gemeente_per_dag”. Reports_with_settings: The number of new persons reported to the GGDs with a positive test result for SARS-CoV-2 published on [Date_of_publication] by RIVM for which possible setting(s) of infection and related cases have been reported. Numbers are shown when [Source_and_contact_tracing_phase] is “high” and from [Date_of_publication] November 8, 2021 also when [Source_and_contact_tracing_phase] is “medium”. Suppressed numbers are replaced by the number “-9999”. The actual number of people who tested positive with related cases is higher than the number of reports in surveillance. In addition, the GGD is not aware of the relationship with another infection or the setting of infection for every positively tested person. Please note: the numbers in this column cannot simply be added up, because they are repeated on a single day for all types of settings reported within a security region. Setting_reported: Reported possible setting of infection of persons reported to the GGDs with a positive test result for SARS-CoV-2 with related cases. The reported settings in this dataset have been translated and coded according to the following scheme: - Home situation (housemates including non-cohabiting partner) - home - Visit in the home situation (of or with family, friends, etc.) - visit - Work situation - work - School and childcare - school_daycare - 1st line health care / general practitioner - general_practitioner - 2nd line health care / hospital - hospital - Other health care - health_care_other - Nursing home or residential care center for the elderly - nursing_home - Residential facility for people with a disability - residential_care_disabled - Other residential facility - residential_care_other - Day care for elderly and people with a disability - daycare_elderly_disabled - Party (party, birthday, drinks, wedding, etc.) - gathering - Fellow traveler / trip / vacation - travel - Flight - flight - Catering - hospitality - Student association/activities - student_activity - Leisure activities, such as sports club - leisure - Religious meetings - religious_activity - Choir - choir - Funeral - funeral - Other - other Settings are shown when [Source_and_contact_tracing_phase] is “high” and

Données par département et grand secteur d'activité relatives aux reports de cotisations des employeurs affiliés au régime général dans le cadre des mesures Urssaf exceptionnelles liées à la crise du Covid-19 (échéances du 15 mars au 15 décembre 2020). Situations en fin de mois de mars à décembre 2020. Afin de tenir compte de l’impact de l’épidémie de coronavirus sur l’activité économique, le réseau des Urssaf a déclenché des mesures exceptionnelles pour accompagner les entreprises présentant de sérieuses difficultés de trésorerie à compter de l'échéance du 15 mars. En cas de difficultés majeures, les entreprises pouvaient ainsi reporter, d'abord sans demande préalable puis sur demande, tout ou partie du paiement des cotisations salariales et patronales.Ce jeu de données décrit l'ensemble des montants reportés, qu'ils s'inscrivent dans le cadre d'un dispositif autorisant le report ou non. Les montants de reports correspondent donc aux "restes à recouvrer".Les données sont déclinées par échéance de paiement : le 5 ou 15 du mois. Les cotisations doivent en principe être payées au cours du mois suivant la période d’emploi rémunérée :au plus tard le 5 de ce mois pour les employeurs d’au moins 50 salariés et dont la paie est effectuée au cours du même mois que la période de travail ;au plus tard le 15 de ce mois dans les autres cas.Source : Acoss-Urssaf, extraction début mai 2021Indicateurs :Nombre d'établissements à l'échéance ()Montant des cotisations duesNombre d'établissements ayant fait un report ()Montant des reports(*) AVERTISSEMENT : l'information sur le nombre d'établissements doit être interprété avec prudence. En effet, les établissements étant dénombrés à chaque échéance, la sélection d'une période couvrant plus d'un mois conduit à compter plusieurs fois les mêmes établissements (un établissement est susceptible de faire une déclaration chaque mois). Ainsi, pour disposer du nombre total d'établissements sans doubles comptes, il convient de sélectionner les échéances d'un même mois. Précisions méthodologiques :ces données prennent en compte les dispositifs d'exonération de cotisations sociales et d'aide au paiement introduits par l'article 65 de la loi n° 2020-935 du 30 juillet 2020 pour soutenir les entreprises les plus impactées par la crise. Les cotisations dues sont en effet des montants après application des exonérations. Et les reports sont des montants après imputation de l'aide au paiement, celle-ci venant réduire les montants à payer par les entreprises concernées.un établissement est comptabilisé comme ayant fait l'objet d'un report dès lors que le montant de cotisations non payées à l'échéance dépasse 44€.le secteur d'activité "nca non classé ailleurs" regroupe le secteur de l'agriculture (AZ) pour ce qui concerne le régime général (la majeure partie du secteur AZ relève du régime agricole, hors champ ici), les activités extra-territoriales (UZ), les activités inconnues, ainsi que les données ne respectant pas le secret statistique au niveau département x grand secteur.Le département et les régions "_non classé ailleurs_" concernent les établissements situés dans les collectivités d'Outre-mer ou à l'étranger, ou ceux (très rares) pour lesquels l'information sur la localisation est manquante. une catégorie "calage" (avec des codes régions et départements à "_" et des libellés "_calage_") permet de recaler sur les niveaux nationaux les données sectorielles altérées par le traitement du secret statistique. DATAVIZ : mise en perspective

Données par département et grand secteur d'activité relatives aux reports de cotisations des employeurs affiliés au régime général dans le cadre des mesures Urssaf exceptionnelles liées à la crise du Covid-19 (échéances du 15 mars au 15 décembre 2020). Situations en fin de mois de mars à décembre 2020. Afin de tenir compte de l’impact de l’épidémie de coronavirus sur l’activité économique, le réseau des Urssaf a déclenché des mesures exceptionnelles pour accompagner les entreprises présentant de sérieuses difficultés de trésorerie à compter de l'échéance du 15 mars. En cas de difficultés majeures, les entreprises pouvaient ainsi reporter, d'abord sans demande préalable puis sur demande, tout ou partie du paiement des cotisations salariales et patronales.Ce jeu de données décrit l'ensemble des montants reportés, qu'ils s'inscrivent dans le cadre d'un dispositif autorisant le report ou non. Les montants de reports correspondent donc aux "restes à recouvrer".Les données sont déclinées par échéance de paiement : le 5 ou 15 du mois. Les cotisations doivent en principe être payées au cours du mois suivant la période d’emploi rémunérée :au plus tard le 5 de ce mois pour les employeurs d’au moins 50 salariés et dont la paie est effectuée au cours du même mois que la période de travail ;au plus tard le 15 de ce mois dans les autres cas.Source : Acoss-Urssaf, extraction début mai 2021Indicateurs :Nombre d'établissements à l'échéance ()Montant des cotisations duesNombre d'établissements ayant fait un report ()Montant des reports(*) AVERTISSEMENT : l'information sur le nombre d'établissements doit être interprété avec prudence. En effet, les établissements étant dénombrés à chaque échéance, la sélection d'une période couvrant plus d'un mois conduit à compter plusieurs fois les mêmes établissements (un établissement est susceptible de faire une déclaration chaque mois). Ainsi, pour disposer du nombre total d'établissements sans doubles comptes, il convient de sélectionner les échéances d'un même mois. Précisions méthodologiques :ces données prennent en compte les dispositifs d'exonération de cotisations sociales et d'aide au paiement introduits par l'article 65 de la loi n° 2020-935 du 30 juillet 2020 pour soutenir les entreprises les plus impactées par la crise. Les cotisations dues sont en effet des montants après application des exonérations. Et les reports sont des montants après imputation de l'aide au paiement, celle-ci venant réduire les montants à payer par les entreprises concernées.un établissement est comptabilisé comme ayant fait l'objet d'un report dès lors que le montant de cotisations non payées à l'échéance dépasse 44€.le secteur d'activité "nca non classé ailleurs" regroupe le secteur de l'agriculture (AZ) pour ce qui concerne le régime général (la majeure partie du secteur AZ relève du régime agricole, hors champ ici), les activités extra-territoriales (UZ), les activités inconnues, ainsi que les données ne respectant pas le secret statistique au niveau département x grand secteur.Le département et les régions "_non classé ailleurs_" concernent les établissements situés dans les collectivités d'Outre-mer ou à l'étranger, ou ceux (très rares) pour lesquels l'information sur la localisation est manquante. une catégorie "calage" (avec des codes régions et départements à "_" et des libellés "_calage_") permet de recaler sur les niveaux nationaux les données sectorielles altérées par le traitement du secret statistique. DATAVIZ : mise en perspective

For English, see below Per 1 januari 2023 verzamelt het RIVM geen aanvullende informatie meer. Dit heeft als gevolg dat we vanaf 1 januari 2023 geen gegevens over besmettingen bij thuiswonende 70-plussers meer rapporteren . Beschrijving bestand: - Dit bestand bevat de volgende aantallen: (aantal nieuw gemelde) positief geteste thuiswonende individuen van 70 jaar en ouder*, naar veiligheidsregio, per datum van de positieve testuitslag. - (aantal nieuw gemelde) positief geteste overleden thuiswonende individuen van 70 jaar en ouder*, naar veiligheidsregio, per datum waarop patiënt overleden is. De aantallen betreffen COVID-19 meldingen sinds de registratie van (woon)instelling in OSIRIS met ingang van vragenlijst 5 (01-07-2020). * Bij meldingen vanaf 01-07-2020 wordt geregistreerd of de patiënt woonachtig is in een instelling. Meldingen vanaf 01-07-2020 worden aangemerkt als thuiswonende individuen van 70 jaar en ouder indien deze volgens de gegevens bekend bij de GGD: • Niet wonend zijn in een instelling EN • Een leeftijd hebben van 70 jaar of ouder EN • De persoon niet werkzaam is en geen zorgmedewerker is Personen waarvan de woonvoorziening/instelling niet vermeld is kunnen alsnog uitgesloten worden als thuiswonende individuen van 70 jaar en ouder indien zij: • Op basis van hun 6 cijferige postcode gekoppeld kan worden aan een bekende locatie van een gehandicaptenzorginstelling of verpleeghuis OF • ‘Gehandicaptenzorginstelling’ of ‘Verpleeghuis’ als vermelde locatie van de besmetting hebben. OF • Op basis van de inhoud van vrije tekstvelden gelinkt kunnen worden aan een gehandicaptenzorginstelling of verpleeghuis.   Het bestand is als volgt opgebouwd: Een set records per datum van met voor elke datum: • Een record voor elke veiligheidsregio (inclusief ‘Onbekend’) van Nederland, ook als voor de betreffende veiligheidsregio geen meldingen zijn. De aantallen zijn dan 0 (nul). • Veiligheidsregio is onbekend wanneer een record niet toe te wijzen is aan één unieke veiligheidsregio. Er is in dit bestand ook een datum 01-01-1900 opgenomen voor statistieken waarvan de bijbehorende datum onbekend is. Hieronder wordt beschreven hoe de variabelen zijn gedefinieerd. Beschrijving van de variabelen: Version: Versienummer van de dataset. Dit versienummer wordt aangepast (+1) wanneer de inhoud van de dataset structureel wordt gewijzigd (dus niet de dagelijkse update of een correctie op record niveau. Ook de corresponderende metadata in RIVMdata (https://data.rivm.nl) wordt dan gewijzigd. Versie 2 update (25 januari 2022): • Er is een bijgewerkte lijst met bekende verpleeg- of verzorgingshuislocaties en particuliere woonzorgcentra van de koepelorganisatie Patiëntenfederatie Nederland ontvangen op 03-12-2021. Op 25-01-2022 is deze bijgewerkte lijst in gebruik genomen voor de vaststelling of individuen woonachtig zijn in een instelling. Versie 3 update (8 februari 2022) • Vanaf 8 februari 2022 worden de positieve SARS-CoV-2 testuitslagen rechtstreeks vanuit CoronIT aan het RIVM gemeld. Ook worden de testuitslagen van andere testaanbieders (zoals Testen voor Toegang) en zorginstellingen (zoals ziekenhuizen, verpleeghuizen en huisartsen) die hun positieve SARS-CoV-2 testuitslagen via het Meldportaal van GGD GHOR invoeren rechtstreeks aan het RIVM gemeld. Meldingen die onderdeel zijn van de bron- en contactonderzoek steekproef en positieve SARS-CoV-2 testuitslagen van zorginstellingen die via zorgmail aan de GGD worden gemeld worden wel via HPZone aan het RIVM gemeld. Vanaf 8 februari wordt de datum van de positieve testuitslag gebruikt en niet meer de datum van melding aan de GGD. Versie 4 update (24 maart 2022): • In versie 4 van deze dataset zijn records samengesteld volgens de gemeente herindeling van 24 maart 2022. Zie beschrijving van de variabele security_region_code voor meer informatie. Versie 5 update (2 augustus 2022): • De indeling van personen als zelfstandig wonende personen van 70 jaar en ouders is niet toe gepast op meldingen die sinds 8 februari 2022 alleen via een alternatieve meldroute bij het RIVM binnenkwamen. Van 8 februari t/m 1 augustus 2022 is hierdoor het aantal meldingen van zelfstandig wonende personen van 70 jaar en ouders met ongeveer 14% onderschat. Vanaf 2 augustus 2022 wordt deze indeling met terugwerkende kracht bijgewerkt. Versie 6 update (1 september 2022): - Vanaf 1 september 2022 wordt de data niet meer iedere werkdag geüpdatet, maar op dinsdagen en vrijdagen. De data wordt op deze dagen met terugwerkende kracht bijgewerkt voor de andere dagen. - Vanaf 1 september 2022 is deze dataset opgesplitst in twee delen. Het eerste deel bevat de data vanaf het begin van de pandemie tot en met 3 oktober 2021 (week 39) en bevat ‘tm’ in de bestandsnaam. Deze data wordt niet meer geüpdatet. Het tweede deel bevat de data vanaf 4 oktober 2021 (week 40) en wordt iedere dinsdag en vrijdag geüpdatet. Date_of_report: Datum en tijd waarop het databestand is aangemaakt door het RIVM. Date_of_statistic_reported: De datum die gebruikt wordt voor het rapporteren van de thuiswonende 70plus statistiek. Deze kan voor iedere gerapporteerde statistiek anders zijn, namelijk: • Voor [Total_cases_reported] is dat de datum van de positieve testuitslag. • Voor [Total_deceased_reported] is dat de datum waarop de patiënten zijn overleden. Security_region_code: Veiligheidsregiocode. De code van de veiligheidsregio gebaseerd op de woonplaats van de patiënt. Indien de woonplaats niet bekend is, wordt de veiligheidsregio gebaseerd op de GGD die de melding heeft gedaan, behalve voor veiligheidsregio Midden- en West-Brabant en Brabant-Noord aangezien voor deze regio’s GGD en veiligheidsregio niet vergelijkbaar zijn. Zie ook: https://www.cbs.nl/nl-nl/cijfers/detail/84721NED?q=Veiligheid Vanaf 24 maart 2022 is dit bestand samengesteld volgens de gemeente indeling van 24 maart 2022. Gemeente Weesp is opgegaan in gemeente Amsterdam. Met deze indeling is de veiligheidsregio Gooi- en Vechtstreek kleiner geworden en de veiligheidsregio Amsterdam-Amstelland groter; GGD Amsterdam is groter geworden en GGD Gooi- en Vechtstreek is kleiner geworden (Gemeentelijke indeling op 1 januari 2022 (cbs.nl). Security_region_name: Veiligheidsregionaam. Veiligheidsregionaam is gebaseerd op de Veiligheidsregiocode. Zie ook: https://www.rijksoverheid.nl/onderwerpen/veiligheidsregios-en-crisisbeheersing/veiligheidsregios Total_cases_reported: Het aantal nieuwe bij de GGD gemelde COVID-19 besmette thuiswonende 70-plussers op [Date_of_statistic_reported]. Het werkelijke aantal COVID-19 besmette thuiswonende 70-plussers is hoger dan het aantal meldingen in de surveillance, omdat niet iedereen met een mogelijke besmetting getest wordt. Bovendien is niet van iedere melding bekend of dit een thuiswonende 70-plusser betreft. Total_deceased_reported: Het aantal bij de GGD gemelde thuiswonende 70-plussers dat is overleden aan COVID-19 op [Date_of_statistic_reported]. Het werkelijke aantal overleden thuiswonende 70-plusser dat is overleden aan COVID-19 is hoger dan het aantal meldingen in de surveillance, omdat niet alle overleden patiënten getest worden en overlijdens niet wettelijk meldingsplichtig zijn. Bovendien is niet van iedere melding bekend of dit een thuiswonende 70-plusser betreft. Correcties die in meldingen in het bronsysteem OSIRIS worden gedaan kunnen ook leiden tot correcties in dit databestand. Aantallen die in het verleden door het RIVM zijn gepubliceerd kunnen in dat geval afwijken van de aantallen in dit databestand. Dit bestand bevat dus altijd de aantallen op basis van de meest actuele gegevens in het bronsysteem OSIRIS. In het CSV-bestand wordt als scheidingsteken een ‘;’ puntkomma gebruikt. Er staan geen lege regels in het bestand. Hieronder de kolomnamen en de typen waarden in het CSV-bestand: • Version: Bestaande uit een enkel geheel getal (integer). Is voor elke rij altijd gevuld. Voorbeeld: 2. • Date_of_report: Geschreven in formaat JJJJ-MM-DD HH:MM. Is voor elke rij altijd gevuld. Voorbeeld: 2020-10-16 10:00. • Date_of_statistic_reported: Geschreven in formaat JJJJ-MM-DD. Is voor elke rij altijd gevuld. Voorbeeld: 2020-10-09. • Security_region_code: Bestaande uit ‘VR’ gevolgd door twee cijfers. Kan ook leeg zijn indien de regio onbekend is. Voorbeeld: VR01. • Security_region_name: Bestaande uit een character string. Is voor elke rij altijd gevuld. Voorbeeld: Midden- en West-Brabant. • Total_cases_reported: Bestaande uit enkel hele getallen (integer). Is voor elke rij altijd gevuld. Voorbeeld: 12. • Total_deceased_reported: Bestaande uit enkel hele getallen (integer). Is voor elke rij altijd gevuld. Voorbeeld: 8. -------------------------------------------------------------------------------- Covid-19 statistics for persons aged 70 and older living outside an institution, by security region and date As of 1 January 2023, the RIVM will no longer collect additional information. As a result, from January 1, 2023, we will no longer report data on infections among people over 70 living at home. File description: This file contains the following numbers: - Number of newly reported persons aged 70 and older living at home who tested positive*, by security region, by date of the positive test result. - Number of newly reported deceased persons aged 70 and older living at home who tested positive*, by security region, by date on which the patient died. The numbers concern COVID-19 reports since the registration of the (residential) institution in OSIRIS with effect from questionnaire 5 (01-07-2020). * For reports from 01-07-2020, it is recorded whether the patient lives in an institution. For reports from 01-07-2020 persons aged 70 and older are considered to be living at home if, according to the information known to the PHS, they: • were not living in an institution AND • Are aged 70 years or older AND • The person is not employed and is not a healthcare worker

Analysis of ‘Mesures exceptionnelles Covid-19 : reports de cotisations Urssaf (TI), par département x grand secteur’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from http://data.europa.eu/88u/dataset/5efd242c3fc863ce24adc547 on 17 January 2022.

--- Dataset description provided by original source is as follows ---

Données par département et grand secteur d'activité relatives aux reports de cotisations des travailleurs indépendants dans le cadre des mesures Urssaf exceptionnelles liées à la crise du Covid-19 (échéances du 20 mars et des 5 et 20 des mois d'avril, mai, juin, juillet et août).

Afin de tenir compte de l’impact de l’épidémie de coronavirus sur l’activité économique, le réseau des Urssaf a déclenché des mesures exceptionnelles pour accompagner les entreprises présentant de sérieuses difficultés de trésorerie. Pour les travailleurs indépendants, les échéances comprises entre le 20 mars et le 20 août n'ont pas été prélevées. Les montants de cotisations afférents ont été reportés pour un lissage sur de les échéances ultérieures.

Les reports des travailleurs indépendants sont calculés sur la base des revenus prévisionnels eux-mêmes basés sur les revenus 2018, puis 2019 dès que ceux-ci sont connus. Les échéanciers établis pour la reprise du recouvrement des cotisations en septembre 2020 intègrent quant à eux un abattement de 50% afin de tenir de la baisse d'activité liée à la crise du Covid-19. Cet abattement n'est ici pas pris en compte.

Les données sont déclinées par catégorie de travailleur indépendant, par département et par grands secteurs d'activité.

Source : Acoss-Urssaf, situation au 31/08/2020

Indicateurs :

• Nombre de travailleurs concernés par les reports (*)

• Montant des reports

(*) AVERTISSEMENT : l'information sur le nombre de TI concernés doit être interprété avec prudence. En effet, les TI étant dénombrés à chaque mois d'échéance, la sélection d'une période couvrant plus d'un mois conduit à compter plusieurs fois les mêmes TI

Précisions méthodologiques :

• le secteur d'activité "nca non classé ailleurs" regroupe le secteur de l'agriculture (AZ) pour ce qui concerne le régime général (la majeure partie du secteur AZ relève du régime agricole, hors champ ici), les activités extra-territoriales (UZ), les activités inconnues, ainsi que les données ne respectant pas le secret statistique au niveau département x grand secteur.

• Le département et les régions "_non classé ailleurs_" concernent les TI situés dans les collectivités d'Outre-mer ou à l'étranger, ou ceux (très rares) pour lesquels l'information sur la localisation est manquante.

• une catégorie "calage" (avec des codes régions et départements à "_" et des libellés "_calage_") permet de recaler sur les niveaux nationaux les données sectorielles altérées par le traitement du secret statistique.

DATAVIZ : mise en perspective

--- Original source retains full ownership of the source dataset ---