Explanation of the data presented

The dashboard presents a variety of data that give us an idea of how we are managing the COVID-19 epidemic. In this section we explain how we arrive at this data.

Reported number of doses administered

The number of reported vaccine doses administered is manually updated daily, based on figures from municipal health services (GGDs) and the LNAZ. They are not yet available as open data.

Calculated number of doses administered

The numbers of reported vaccine doses administered are incomplete, because the registration systems of healthcare institutions and general practices have not yet been linked to the central registration system of RIVM (CIMS). Manual updates are not feasible because of the large amount of institutions and general practices. It is therefore unknown exactly how many vaccine doses have been administered in institutions and general practices. To give a better idea of how many doses have been administered, from 31 January 2021 the dashboard will provisionally provide a calculation by RIVM. An explanation of the calculation can be found on the RIVM website.

As soon as the reporting of the number of doses administered has been computerised, the dashboard will switch to data from the CIMS. This change to our working methods may lead to a sudden change in the figures, as was the case on 31 January 2021.

Vaccinated people

The dashboard currently shows the number of doses administered. Until 27 January 2021, this was the same as the number of people who have received one, initial dose of vaccine. People need two separately administered doses for the vaccine to be maximally effective. This means that people who have already received one dose will start receiving their second dose from 27 January 2021 onwards. From that point on, the number of doses administered will be higher than the number of people who have received one dose. As soon as automated figures on the number of fully vaccinated people are available, these will be shown on the dashboard.

Deliveries

Data on deliveries are provided by RIVM. The dashboard shows how many vaccines in total have been delivered and checked. By default, a number of checks are carried out on new deliveries. After that, the doses are available for administration. The dashboard only shows figures on available doses and not on doses that have been delivered but have not yet been checked.

RIVM and other agencies also share information about deliveries before they are checked. These numbers of doses delivered are higher than the numbers on the dashboard.

The figures on willingness to be vaccinated are derived from RIVM’s survey of the Dutch public. As part of the survey, people were asked whether they wanted to be vaccinated against COVID-19. RIVM supplies the figures as open data. The RIVM Data Catalogue contains the description of this dataset.

Other recurring surveys, such as those of I&O Research and Ipsos, also measure willingness to be vaccinated. RIVM also conducts another survey (WP3) that measures willingness to be vaccinated, but it is carried out less frequently.

The results of these surveys may vary. That is because the RIVM survey counts only those people who give a clear ‘yes’ when asked whether they want to be vaccinated, whereas others also include people who answer ‘probably’ to this question. The date the survey was conducted may also be of influence, because people’s willingness to be vaccinated changes over time.

The number of positive tests (confirmed cases) is supplied daily via open data by the National Institute for Public Health and the Environment (RIVM). The description of this dataset can be found here. The figures show the daily positive tests reported to RIVM in the preceding 24 hours, up to 10:00 on the day of publication. The date attributed to positive tests is the date on which RIVM was notified. This is not the same as the day on which people were tested.

Doctors and laboratories in the Netherlands are required by law to report cases of certain infectious diseases to the municipal health service (GGD). However, coronavirus tests are also being carried out by commercial organisations and individuals, and the results are not always reported to the GGD. Moreover, tests that do not meet the standards of the National Diagnostic Chain Coordination Team (LCDK) or RIVM are not counted (https://www.rijksoverheid.nl/onderwerpen/coronavirus-covid-19/testen/soorten-testen/ontwikkeling-van-sneltesten-op-corona). In other words, the figure for daily positive tests published on the dashboard may not be 100% accurate. This must be kept in mind when interpreting the figures.

Test results are transferred from laboratories to the GDD and RIVM using different IT systems and is partly done manually, which delays the process somewhat. The GGD needs to question infected persons or their relatives to collect important data, so there is a time lag between a test result becoming known and RIVM being notified. A rise in the number of infected persons or a technical malfunction can lead to notification backlogs. So the data provided for the previous 24-hour period does not include all newly confirmed cases at the moment that RIVM accesses the data file. However, the data is always corrected with any late notifications that come in. Delays in reporting can skew the daily figures for positive tests. The moving average filters out daily fluctuations and thus provides a better picture of the trend in the number of infections.

Sometimes RIVM has to correct data that has already been published. These are not content-related corrections, but concern minor procedural errors, such as a typo in a GGD questionnaire, or the wrong selection of an answer category. These corrections are also processed in the open data file and incorporated in the dashboard.

Based on the absolute numbers, we calculate the relative number of positive people in proportion to the number of inhabitants. We do this by dividing the number of positive people by the number of inhabitants of the Netherlands or the relevant safety region or municipality. We present this as the number of positively tested persons per 100,000 inhabitants. The number of inhabitants nationally, per safety region and per municipality is based on this CBS classification.

A system of alert values has been set up. They signal when a situation has become alarming and needs urgent attention. The alert value for the number of confirmed cases a day is 7 per 100,000 inhabitants. This is the stage at which the number of infections is increasing too fast to keep the virus under control. The same alert value is also used in Germany.

Explanation of the age groups

The distribution of positive cases over age groups is based on another set of open data from the RIVM. This is because ages are not recorded in the municipal data set. The following dates can be found in the data set used: (1) first day of illness, (2) if that date is unknown, the date of the first positive lab test result, (3) if that date is unknown, the date on which the municipal health service (GGD) was notified of the positive result.

The municipal data set, by contrast, only records the date on which each positive case is reported by RIVM, which is usually more recent than the three aforementioned dates. That's why there are differences between the graph for the total number of positive cases and the positive cases by age group. What’s more, the latter graph depicts seven-day rolling averages. And sometimes, the age of someone who tests positive for coronavirus is unknown. To be able to compare the different age groups, we calculate the number of positive tests per 100,000 people in each age group, using the Statistics Netherlands (CBS) distribution of the total population by age group.

The graph of positive cases by age group shows that the data for the most recent days in particular is still incomplete. RIVM supplements and corrects the data retrospectively.

This graph shows the trend in positive cases for each age group, per 100,000 people in that age group. The figures in the graph are seven-day rolling averages. They do not agree exactly with the number of positive cases presented elsewhere on the page, which is based on a different data set. The data set used for this graph contains other dates, such as the first day of illness. You can read more about it in the explanation of the presented data.

Growth rate

The Growth rate (or G rate) shows how the number of daily positive tests over the last 7 days has developed relative to the previous 7 days. To calculate this Growth rate, data must be available for 14 consecutive days (the last 7 days and the 7 days before that).

The historical Growth rates are recalculated daily by the Ministry of Health, Welfare and Sport based on the data of RIVM, in order to incorporate any corrections made by RIVM to the number of daily positive tests into the historical trend of the Growth rate. The Growth rate is calculated in line with the method used by research institute IPSE Studies. The Growth rate has been introduced by de Volkskrant.

Percentage of positive test results at GGD test locations

The number and percentage of positive tests by the GGD is supplied daily via open data by the National Institute for Public Health and the Environment (RIVM). The description of this dataset can be found here. The percentage of positive tests is based solely on tests carried out by the GGD, and for which the test result is known. This is because the GGD is the only organisation that reports negative test results as well as positive ones. Negative results of any tests not carried out by the GGD are not reported to the GGD or to RIVM.

The number of infectious people is published via open data by the RIVM on a weekly basis (on Tuesdays). The description of this dataset can be found here. A person who carries coronavirus can infect other people within a certain time frame. How long someone is infectious for differs from person to person. Using a range of data sources regarding the number of people infected with the virus in a specific time frame, it is possible to estimate the number of infectious people in the Netherlands. See RIVM for more information regarding the method. There is no alert value for the number of infectious people as this number is an estimate based on a calculation.

The calculation method of the number of infectious people has been changed since 13 October. Between 1 June till 13 October this number was calculated based on the results of the first Pienter Corona research, the number of hospital admissions and the number of confirmed cases. From 13 October onwards, this number is calculated based on the results of the second Pienter Corona research and the number of hospital admissions. In this second research, a larger group of people have been examined. Besides, more insight in the infectiousness of people has been gained.

The reproduction number (R) shows the number of people who are infected, on average, by one person with COVID-19. These figures are supplied two times a week (on Tuesdays and Fridays) as open data by the National Institute for Public Health and the Environment (RIVM). The description of this data set can be found here. The reproduction number (R) is not an exact value but a reliable estimate.

RIVM uses the reported number of daily positive test results to calculate R. In most cases, the day on which symptoms first presented is known. In other cases, an estimate can be made of this date. In the case of people who tested positive without symptoms, RIVM uses the date on which they would have developed symptoms if they had not been asymptomatic.

Depicting the number of COVID-19 cases according to the first day of symptoms gives a clear picture of whether the number of transmissions is increasing, at its peak or decreasing. In order to estimate R, it is also necessary to know the length of time between the first day of symptoms of a new COVID-19 case and the first day of symptoms of the person who infected them. This has been calculated to be 4 days on average, based on COVID-19 cases reported to the municipal health service (GGD). This information is then used to calculate the reproduction number.

The most up-to-date value of R provided on the Coronavirus Dashboard at any given time is always from at least two weeks ago. Estimates of R using data from less than 14 days ago could provide an indication of what the definitive value may be, but are not reliable predictors. Estimates using more recent data are less reliable because the data is not yet complete. It is, for instance, impossible to know how many newly confirmed cases will result in hospital admissions in the days ahead.

Until 11 June 2020 R was calculated on the basis of COVID-19 hospital admissions as large-scale testing was not yet underway. Until 2 November 2020, the graph showing the progression of R over time did not give a value of R for the most recent two weeks but did give a projection of this value in the form of a bandwidth. As of 3 November, the dashboard graph no longer displays this bandwidth.

These figures are supplied daily as open data by the National Institute for Public Health and the Environment (RIVM). A description of this dataset can be found here. They show the number of COVID-19 patients reported dead to RIVM in the past 24 hours, up to 10.00 on the day of publication. The date used is the date on which the death was reported by the GGD to RIVM. This may not be the same as the day on which the death occurred. The actual number of deceased COVID-19 patients is higher than what is reported by RIVM because there is no requirement for reporting COVID-19 related deaths.

The graph of COVID-19 deaths nationwide by age group is based on a separate open data file from RIVM. See a description of the dataset here. The dataset groups everyone younger than 50 together; the low numbers of COVID-19 deaths in this age group (elsewhere on the dashboard) could otherwise be traceable back to individual people.

General data on mortality (mortality monitor) on a national level is provided via a public table by the national office Statistics Netherlands (CBS) and updated weekly. The numbers for each safety region are found in a different CBS table. The predicted number of deaths per week have been taken from the mortality projections made once every year by the CBS.

Every day RIVM makes open data available on new hospital admissions. The description of this data set can be found here. These numbers include patients admitted to regular care units and ICU.* RIVM uses data collected by Stichting NICE (the National Intensive Care Evaluation Registry), an organisation, established by the professional association of ICU doctors, which registers available data from ICUs nationwide.

The admissions are not necessarily reported on the day of admission. The dashboard shows the number of admissions reported in one day. This figure is deemed to provide a good reflection of the current developments, as recordings of one day of admissions are illustrated.The dashboard previously used data from the Osiris database, which is provided primarily by the municipal health services (GGDs). However it has come to light that hospital admissions tend to be significantly underreported in the Osiris database because GGDs are not always informed when COVID 19 patients are admitted to hospital. The NICE data is more complete, but its definition of ‘hospital admission’ is also broader. Whereas the Osiris database includes only patients admitted to hospital due to COVID 19, the NICE database also includes hospital patients who have COVID 19 but who were admitted for another reason.

Non ICU hospital occupancy rates are made available as open data by LCPS (the national coordination centre for patient distribution). The description of this data set can be found here. The data from before 1 June 2020 may be somewhat less reliable; the LCPS was still in the process of setting up their registration system at that time.

LCPS has also been collecting data on new hospital admissions since October 2020. Despite this, the dashboard uses the data from NICE, and not from LCPS. This is because LCPS provides only aggregated figures. The data is not disaggregated by patients’ age or sex, for example. It is therefore too limited to be of use for the dashboard, and cannot be used by RIVM.

Differences in the two registration systems are a result of what exactly they register, i.e. occupancy (capacity) versus patient counts. An increase in occupancy does not fully reflect changes in patient counts. When a patient is discharged from hospital, their bed does not become available immediately, for example. It’s also possible that a single bed may have several occupants over the course of a day. The data sets contain different information and are used for distinct purposes. These figures are not interchangeable as such.

*Since 17 December 2020 the dashboard has used this dataset. Until 26 January 2021 it said that this date excluded ICU-admissions. This was incorrect: the number includes patients that have been admitted to straight to ICU (not those already admitted to hospital). As of 26 January 2021 the description of this number had been corrected.

Every day RIVM makes open data available on new ICU admissions. The description of this data set can be found here. RIVM uses data collected by Stichting NICE (the National Intensive Care Evaluation Registry), an organisation, established by the professional association of ICU doctors, which registers available data from ICUs nationwide.

ICU occupancy rates are obtained daily via open data provided by the LCPS. The description of this dataset can be found here. The data includes the number of ICU beds occupied by COVID-19 patients and the number of ICU beds occupied by all other patients. The percentage of ICU beds occupied by COVID-19 patients is calculated by dividing the number of ICU beds occupied by COVID-19 patients by the total number of occupied ICU beds. The data from before 1 June 2020 may be somewhat less reliable; the LCPS was still in the process of setting up their registration system at that time.

ICU admissions reported by RIVM, include any ICU-admissions of Dutch patients in German hospitals. ICU occupancy does not include any Dutch patients in German ICU’s. This is because the admissions figures are primarily used to track the spread and the effect of COVID-19, while occupancy numbers are primarily used to show capacity in Dutch ICU’s.

The National Institute for Public Health and the Environment (RIVM) provides the Ministry of Health, Welfare and Sport with information about the COVID-19 situation in nursing homes on a daily basis via open data. The description of this dataset can be found here. This data cannot always be clearly linked to a specific safety region. As a result, the total sum of numbers at the safety-region level may be less than the numbers at the national level. The number of deaths of nursing home residents is based on the date of death. However, when the date of death is unknown that death is not included in this statistic in the dashboard.

The method used to estimate the numbers of nursing homes and nursing home residents infected with coronavirus has been improved. Since 1 July 2020, for every person newly infected with coronavirus, the municipal health services (GGDs) have noted whether that person lives in a nursing home. Until the end of September 2020, no use was made of this data and a different method was used to calculate infection numbers, which led to a significant overestimate of the number of infected nursing homes. As of 29 September 2020, the estimates on the dashboard from 1 July 2020 onwards have been recalculated using the this data and therefore now give a much better picture of the actual number of infected nursing homes and nursing home residents.

Old definition of a nursing home resident: A person was counted as a nursing home resident if, according to the central database OSIRIS: on the basis of their postcode, they can be linked to a registered nursing home or residential care home for the elderly, a setting (place where infection may have occurred) labelled as a ‘nursing home’ or something similar, or if there is any other term that links them in the database to a nursing home or residential care home for the elderly. In addition, the person:

  • must be over 70 years old
  • must not be a health worker, and
  • must not be employed.

New definition of a nursing home resident: As of 1 July 2020, every time a person tests positive for COVID-19, the GGD asks whether that person is a resident of a nursing home or residential care home for the elderly. This information is used in the new definition. A person is considered to be a nursing home resident if, according to the data in OSIRIS:

  • This person is a resident of a nursing home or a residential care home for the elderly.
  • If it is not known whether the person lives in a nursing home or residential care home for the elderly, the old definition is used.

The total number of nursing homes per safety region is provided by RIVM. Any nursing homes with the same postcode are counted as one nursing home. This is because the number of infected nursing homes is also determined by postcode. It is necessary to know the total number of nursing homes per safety region in order to calculate what percentage of nursing homes in the safety region is infected. The total number of nursing homes nationwide is the sum of the total number of nursing homes in all safety regions.

Every day the National Institute for Public Health and the Environment (RIVM) provides open data about the COVID-19 situation in disability care homes. A description of the data set can be found here.

This data cannot always be clearly linked to a specific safety region. As a result, the total sum of numbers at safety-region level may be less than the numbers at national level. The number of deaths is based on the date of death. However, when the date of death is unknown that death is not included in this statistic in the dashboard.

The word ‘locations’ refers to the 2,586 care homes in the Netherlands that are registered by Zorgkaart Nederland as providing residential care to people with disabilities. The number of locations in each safety region is provided by RIVM. Care homes with the same postcode are counted as one location, which is identical to the method used to determine the number of infected locations. The number of locations in each safety region is needed in order to calculate the percentage of infected locations in that region. The total number of locations in the Netherlands is calculated by adding up the numbers for all of the safety regions.

As with nursing homes, information about disability care homes is based on records kept by the municipal health services (GGDs). As of 1 July 2020, every time a person tests positive for COVID-19, the GGD asks whether that person is a resident of a disability care home. A person is considered to be a disability care home resident if this is evident from information in OSIRIS (the database used by RIVM for this purpose). If it is unknown whether someone is a disability care home resident, their postcode can be used to link them to a registered disability care home or a setting (place where the infection may have occurred) labelled as a ‘care home’ or something similar.

Every day the National Institute for Public Health and the Environment (RIVM) provides open data about the COVID-19 situation among over-70s living at home. A description of the data set can be found here. This data cannot always be clearly linked to a specific safety region. As a result, the total sum of numbers at safety-region level may be less than the numbers at national level. The number of deaths is based on the date of death. However, when the date of death is unknown that death is not included in this statistic in the dashboard.

For its calculations RIVM uses test results and mortality data which it receives from the municipal health service (GGD). When people get tested for coronavirus, the GGD also records certain personal details, such as their age and whether they live in a nursing home or disability care home. RIVM calculates the number of new cases among over-70s living at home by, first, determining the total number of new cases in this age group and then subtracting the number of infections:

  • Amongst nursing home residents;
  • Amongst care workers;
  • Amongst people that can be linked to a care home via their address;
  • Amongst people that can be linked to a care home because of the setting of their infection.

The population of over-70s living at home is highly diverse in terms of their health and living situation. Many over-70s are active and in good health. But others live with at least one chronic disease and/or have physical or cognitive limitations, and are less active. It is not possible at this time to distinguish between COVID-19 infections among active and inactive over-70s living at home because information about activity levels is not recorded when people get tested for coronavirus.

The dashboard also shows the number of daily confirmed cases per 100,000 for over-70s living at home. The total number of over-70s living at home is based on data collected by Statistics Netherlands on the number of over-70s living in a private household (Households: size, composition, position, 1 January; cbs.nl). This group consists of roughly 2.3 million people.

What figures are we showing?

The coronavirus dashboard presents figures for the number of coronavirus particles found in wastewater. We provide figures for the whole country, each of the safety regions and each municipality. The measurements are converted into the number of virus particles per 100,000 inhabitants. This makes it easier to compare the situation in different regions.

What do the figures mean?

Human excrement flushed down the toilet can contain pathogens (viruses and bacteria that can make people sick), which can be measured in wastewater. These tests measure the concentration of coronavirus particles in wastewater that may (but do not always) come from the excrement of infected individuals. In the future, wastewater monitoring could be a useful tool for early detection of coronavirus in a community, independently of regular testing. The results can also help us understand the effects of vaccination and immunity. The experience gained in this research programme can prove useful in the future.

How precise are the figures?

The current study makes trends visible, such as an increase or decrease in the number of virus particles. But more research is needed before any conclusions can be drawn about these trends.

RIVM also corrects for the volume of water flowing into the wastewater treatment plant at the time of sampling, which is called the flow rate. Rainwater, for example, has a diluting effect.

If the number of virus particles in a sample is too low to be measured, the value is recorded as 0.

Where is the data collected?

Wastewater samples are collected at 315 wastewater treatment plants across the country. So the data we have covers the entire country. The samples of wastewater are kept chilled during transport to RIVM. RIVM researchers analyse the samples and determine how many coronavirus particles each sample contains. At least once a week for each location, researchers test one sample of wastewater collected over a 24-hour period. The number of locations where samples were collected successfully differs from one week to the next. The open data file shows on which date and at which locations measurements were taken successfully.

Data analytics

Assisted by the water authorities, Statistics Netherlands (CBS) has calculated the number of inhabitants served by each wastewater treatment plant, so that RIVM can determine the number of virus particles per 100,000 inhabitants.

The weekly average is calculated by first determining the average of all measurements taken at each sampling location in a given week, and then determining the average of all sampling locations for that week per municipality or safety region, or for the country as a whole. A week runs from Monday to Sunday. The weekly average is calculated on the following Tuesday, so that the week is complete and RIVM has time to receive and analyse the samples, and report on them. Please note that additional data relating to the preceding week or even the weeks before that may be received after Tuesday. This is why the open data file is read every day and the weekly averages recalculated. Any corrections relating to previous weeks are also entered in the dashboard. The graph shows the value of the sample for each location and the date on which it was collected.

The weekly averages at national, safety region and municipal level take account of the number of inhabitants served by each location, as well as the extent to which a wastewater treatment plant’s catchment area falls within different municipalities or safety regions. If no sample has been collected (yet) at a certain location in a given week, that location is not included in the analytics. The figures for locations that serve more inhabitants weigh proportionally more. The number of inhabitants served by a location can be found in a table provided by Statistics Netherlands (CBS). For each location, the table also shows which percentages of the incoming wastewater can be attributed to specific safety regions and municipalities.The exact calculations are as follows:

Weekly average for the whole country:

  • For each location, divide the weekly average by 100,000.
  • For each location, multiply this by the number of inhabitants served by the location.
  • Add up the answers for all the locations.
  • Divide this answer by the sum of all inhabitants served by these locations.
  • Multiply this number by 100,000.

Weekly average for a safety region:

  • For each location, the number of inhabitants served is multiplied by the proportion of that location’s wastewater attributed to the safety region in question. This value is referred to as (X).
  • For each location in the safety region, divide the weekly average by 100,000.
  • Multiply this number by (X).
  • Add up the answers of all the locations in the safety region.
  • Divide this answer by the sum of all (X) values of all the locations in the safety region.
  • Multiply this number by 100,000.

Weekly average for a municipality:

  • For each location, the number of inhabitants served is multiplied by the proportion of that location’s wastewater attributed to the municipality in question. This value is referred to as (X).
  • For each location in the municipality, divide the weekly average by 100,000.
  • Multiply this number by (X).
  • Add up the answers of all the locations in the municipality.
  • Divide this answer by the sum of all (X) values of all the locations in the municipality.
  • Multiply this number by 100,000.

Change in data sources

The National Institute for Public Health and the Environment (RIVM) providesopen dataabout wastewater monitoring. The file’s format has been simplified as of 4 March 2021.

Changes in calculations

In 2020 two sampling locations were ended. Sampling for their catchment areas was taken over by two other locations. In the week of 5 October, the location at Aalst was taken over by Zaltbommel, and in the week of 7 December, the location at Lienden was taken over by Tiel. Because of this, for the weeks before these closures, the locations Tiel and Zaltbommel will have a decreased number of inhabitants served. These numbers, and those of locations Aalst and Tiel, can be found in the following 2020 version of the CBS table.

From 4 March 2021 the dashboard also shows wastewater monitoring results for municipalities that do not have their own wastewater treatment plant. This is possible by using the measurements from all wastewater treatment plants that serve a municipality. Using the population distribution statistics provided by CBS, we can attribute the number of virus particles in wastewater to a municipality that does not have its own wastewater treatment plant. The figure presented is per 100,000 inhabitants and can therefore be compared with other municipalities. This new method of analytics produces other values at national, regional and municipal level. See ‘Data analytics’ above for how these figures are calculated.

Information from general practitioners (GP) is published via open data by Nivel on a weekly basis (on Thursdays). The description of this dataset can be found here. A representative sample of around 350 GP practices across the Netherlands provide data once a week to Nivel. They record reported and observed symptoms and diagnoses of the consulting patients they see. Using this data, Nivel determines how many patients saw their GP in the past week for the first time for symptoms that could indicate COVID-19. Nivel determines this on the basis of the diagnostic codes reported by GPs (‘acute upper respiratory system infection’, ‘other respiratory infection(s)’, ‘influenza’, ‘pneumonia’, ‘other viral disease(s)’, ‘other infectious disease’, ‘fever’, ‘shortness of breath’, ‘coughing’) and any additional information provided that could indicate COVID-19.

To improve this indicator’s accuracy, Nivel also recalculates the data for the preceding weeks, so they include information that only became available at a later time. The open data reports the numbers per week.

This data is updated every three weeks and is published as an open data file by the National Institute for Public Health and the Environment (RIVM). The description of this data set can be found here. The information on the Coronavirus Dashboard is also updated on the day the new file is released. The data file contains information on the Dutch public’s behaviour in relation to the COVID-19 rules and is based on findings obtained by means of a survey.

Data is collected online via computer-assisted web interviewing (CAWI). This method involves posting a programmed questionnaire online. Invitations to complete the questionnaire are then sent by email, explaining the purpose of the survey and referring to RIVM as the commissioning authority. The questionnaire was drawn up by RIVM’s Corona Behavioural Unit.

Participants are selected from the Kantar Consumer Panel (TNS NIPObase) and are aged 16 and over. For each safety region, invitations are sent to a representative sample according to gender, age (5 categories) and highest level of education completed (3 categories) (based on marginal totals). Assuming a 50% response rate, a gross sample of n=9,780 is drawn.

People in the study sample have one week to fill in the questionnaire online. After this, data collection is closed and responses are checked for reliability. To make the survey findings more representative for the entire Dutch population, the results are weighted in two steps on the basis of Statistics Netherlands’ population figures:

  1. Firstly, the sample in each safety region is weighted according to the actual population distribution by age, sex and level of education for the region in question.
  2. Secondly, each safety region is given a weight in the total, according to its share of the Dutch population.

The data set is then sent to RIVM which uses a standardised method to calculate values for the indicators included in the open data file.

The dashboard only shows data from the behavioural study that has been measured consistently over time. Differences compared to previous measurements are reported only if the way in which the measurements were taken was similar. Take the use of face masks on public transport, for example. The method used to measure this changed between rounds 3 and 4. The results of previous survey rounds therefore cannot be compared to that of round 4, and these earlier results are no longer presented together in the same historical chart. Regional differences are visible only if the group of respondents for a given question is representative for the overall population of a region. The complete set of survey results for each round is available on the RIVM website.

The CoronaMelder app has been developed by the Ministry of Health, Welfare and Sport, in partnership with the National Institute for Public Health and the Environment (RIVM) and the municipal health services (GGDs). CoronaMelder sends users an alert if, for at least 15 minutes, they have been within close range of another app user who has since tested positive for coronavirus. It also tells you what you should do if you receive an alert.

The more people who use CoronaMelder, the more people who will get an alert if they have been in contact with an infected person. The app uses Bluetooth to measure the distance between app users. It does not record locations, names, phone numbers, email addresses or any other contact details. The app doesn’t know who you are, who the other user is and where you are or were. The CoronaMelder website explains how the app works in detail.

Ongoing research is being carried out into the use of the app. Read the most recent report about this on the CoronaMelder website. The data about the app on the Coronavirus Dashboard is updated every day. You can find a detailed description of the dataset here.

This dashboard was developed in a short amount of time. So far, we have therefore not been able to publish the code for inputting the data and constructing the .JSON files. However, this will be done soon. The web application code was published on Github on 5 June 2020. The data calculations are available via Github as well.

The tiles on the dashboard show the term “value of” to indicate the date to which the number refers, while “obtained” is used to refer to the date when the information was received. The purpose of providing this information is to give users a better idea of how up-to-date the data is, which is especially relevant for retrospective indicators. For instance, RIVM publishes the reproduction number (R) once a week, but the most recent R is always for a date two weeks in the past. “Value of” is followed by that date two weeks ago, and “obtained” by the date on which the most recent R was published (the current week).

On 1 January 2021 several new municipalities were formed by merging existing municipalities. (Click here for policy documents concerning municipal mergers (in Dutch).) As of January 7 the National Institute for Public Health and the Environment (RIVM) has processed the data it provides for the Coronavirus Dashboard in accordance with this municipal boundary reform. The municipal and regional maps on the dashboard have been modified to reflect the changes. Historical data from 2020 has for municipalities that no longer exist, has been added to the municipalities they have merged with.

The seven-day rolling average is the average number of positive cases over the last seven days. It is computed by adding up all the daily positive cases and dividing this number by seven (the number of days in the week). A graph based on the daily number of positive cases shows more fluctuations. General trends are easier to visualise using the seven-day rolling average.