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). 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 hospitals, 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.

Changes in the numbers

From April 2 to April 8, there was a temporary break with the injection of AstraZeneca with people under 60 years of age. This partial pause meant that it was uncertain how many injections were given with AstraZeneca. That is why no injections for AstraZeneca were counted on the dashboard in that period. However, vaccination with AstraZeneca has continued in people over 60 years of age. The RIVM has calculated afterwards how many injections were taken with AstraZeneca. These numbers were added to the dashboard on April 13.

In the previously calculations, 5% waste was taken into account. After an analysis by the RIVM, it can be assumed that the data known to the GGD show that the waste is less than 1%. Based on this analysis, it can be assumed that this percentage is also a better assumption for institutions and general practitioners than the 5% used previously. By adjusting this retroactively, the previously calculated figures have turned out higher.

Due to this recalculation, the dates on which the 3 millionth, the 2 millionth and the millionth injections were taken were adjusted on the vaccination page in the overview of events on 16 April.

At the end of March, RIVM calculated more accurately how many injections were given with which vaccine. As of 1 April, the graph "Delivered and available vaccines & injections in total" has changed slightly as a result. From 1 to 24 March, a too low number of injections was calculated for AstraZeneca, due to an error in the programming, a number of injection days at the GPs and institutions were not counted even though injections were taken. This bug was fixed on the dashboard on 6 April.

On March 24 and 25, a too high number of injections in settings was reported on the dashboard. Inadvertently, injections were included that were placed on the BES, due to an error in the coding. This bug was fixed on March 26.

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. The RIVM keeps track of how many people have started the vaccination and how many people have completed the vaccination. A vaccination is started when someone has had a first injection. A vaccination is completed when someone has had the last necessary injection. Someone is then fully vaccinated. Depending on the type of vaccine, this is immediately after the first injection or after the second injection.

Where do these figures come from?
The figures come from RIVM’s behavioural survey. RIVM provides these figures as open data.

How are the figures established?

The figures represent the combined number of survey participants who have already been vaccinated or want to be vaccinated. As part of the behavioural survey participants are asked whether they have received a letter inviting them to get vaccinated. The ‘percentage of people already vaccinated or willing to be’ shown on the Dashboard reflects the sum total of participants who gave one of the following four answers: (1) received letter and already vaccinated; (2) received letter and made an appointment; (3) received letter and intend to make an appointment; and (4) have not received letter but want to be vaccinated.

Changes in the figures

Up to and including the 11th round of the survey, which was conducted from 20 to 26 April 2021, information was collected about the willingness to be vaccinated among participants who had not yet received a letter inviting them to be vaccinated. Only those who had not yet received an invitation letter were asked whether they wanted to be vaccinated against COVID‑19. As more people began to receive their letters, this question began to yield misleading results.

Starting with the 12th round of the survey, conducted from 11 to 17 May 2021, this question was modified as described in the section ‘How are the figures established?’ above.

Data on deliveries and doses in stock is provided by the National Institute for Public Health and the Environment (RIVM). This is not yet available as open data.

On the dashboard, the graph of ‘Delivered and available doses & doses administered in total’ shows the total number of vaccines that have been delivered and checked. Newly delivered doses are always first subjected to a number of checks. After this, the doses can be administered.

Previous calculations accounted for 5% waste. GGD data shows, however, that there has been less than 1% waste at GGD vaccination sites. On the basis of this information, RIVM assumes vaccine waste at doctor’s offices and other facilities to also be less than the previous estimate of 5%. By retroactively applying these new numbers, calculations have turned out to be higher than previously thought.

Vaccines for the Caribbean parts of the Kingdom (Aruba, Curaçao, St Maarten, Bonaire, Saba and St Eustatius) are not included in the figures in this graph either.

The graph of ‘Delivered and available doses & doses administered in total’ shows lower figures than the other graphs and figures on this page because these other graphs and figures do not take account of wastage and do count the vaccines for the Caribbean part of the Kingdom.

In the ‘Deliveries’ graph the Dashboard shows the number of vaccine doses that the Netherlands has received and expects to receive. RIVM assumes that each vaccine vial contains a specific number of doses – BioNTech/Pfizer: 6 doses per vial; Moderna: 10 doses per vial; AstraZeneca: 11 doses per vial; and Janssen: 5 doses per vial. For some vaccines this may differ from the manufacturer’s indications.

The graph of ‘Delivered and available doses & doses administered in total’ shows lower figures than the other graphs and figures on this page because these other graphs and figures do not take account of wastage and do count the vaccines for the Caribbean part of the Kingdom.

The total stock counts include both available and not yet available quantities per vaccine type stored at central storage facility. The available stock is made up of the free stock and the not available stock. The available stock includes free stock and safety stock. The stock that is not yet available is the vaccines that still have to be checked. The size of the stocks fluctuates. Shortly after a new delivery, the total stock of a vaccine is relatively high, just before the next delivery comes out of control, the available stock is low.

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.

Hospital admissions per age group
RIVM supplies the weekly totals of the admissions that took place in that week in an open database per age group (source NICE Foundation, edited by RIVM). The description of this dataset can be found here.

In order to be able to compare the age groups, the number of hospital admissions per 1,000,000 people from a specific age group is calculated. The population size per age group is determined on the basis of the Statistics Netherlands (CBS) distribution.

For example: the total number of hospital admissions in the week from 8 to 14 February 2021 is 256. The total size of the 80-89 age group in the Netherlands is 692.252. The number of admissions per 1,000,000 from the age group 80-89 is then 1,000,000 / 692.252 * 256 = 369,8.

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.

ICU admissions per age group
RIVM supplies the weekly totals of the admissions that took place in that week in an open database per age group (source NICE Foundation, edited by RIVM). The description of this dataset can be found here.

In order to be able to compare the age groups, the number of ICU admissions per 1,000,000 people from a specific age group is calculated. The population size per age group is determined on the basis of the Statistics Netherlands (CBS) distribution.

For example: the total number of ICU admissions in the week from 8 to 14 February 2021 is 11. The total size of the 80-89 age group in the Netherlands is 692.252. The number of admissions per 1,000,000 from the age group 80-89 is then 1,000,000 / 692.252 * 11 = 15,9.

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.

Where do these figures come from?
The National Institute for Public Health and the Environment (RIVM) supplies open data on positive test results via GGD test locations.

How are the figures determined?
The percentage of tests that have a positive result is based on tests conducted by the GGD and for which the results are known. The seven-day rolling average is calculated with a two-day delay. This is because the results of tests conducted in the last 48 hours may not yet be available. This way we can be sure that most of the results are available and that the percentage of positive tests is accurately represented.

Where do these figures come from?
Twice a week (on Tuesdays and Fridays), the National Institute for Public Health and the Environment (RIVM) supplies open data on the number of infectious people.

How are the figures determined?
Someone who is infected with coronavirus can transmit the virus to other people. How long they are infectious for differs from one person to another. The precise number of infectious people is unknown, but RIVM calculates the range within which this number probably lies using data from the Pienter Corona study and the number of hospital admissions.

Changes in the figures
From 1 June 2020 until 12 October 2020 the number of infected people was estimated on the basis of data from the first round of the Pienter Corona study, the number of hospital admissions and the number of confirmed cases. As of 13 October 2020, this figure is estimated using data from the second round of the Pienter Corona study and the number of hospital admissions. In the second round of this study, a larger group of people was tested to find out if they were infected with coronavirus, and more insight was gained into the degree of people’s infectiousness.

Where do these figures come from?
Twice a week (on Tuesdays and Fridays), the National Institute for Public Health and the Environment (RIVM) supplies open data on the reproduction number. The reproduction number (R) is not an exact value but a reliable estimate.

How are the figures determined?
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, this date can be estimated. If someone has no symptoms, RIVM will use the date on which they would normally have experienced symptoms for the first time.

Depicting the number of coronavirus 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 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 coronavirus cases reported to the municipal health service (GGD). This information is then used to calculate the reproduction number.

How up to date are the figures?
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. The more recent the day, the less reliable the figure is since not all the data for that day is available yet.

Changes in the figures
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.

Where do these figures come from?
General data on mortality (mortality monitor) is provided by the national office Statistics Netherlands (CBS).

How are the figures determined?
The figures are updated weekly. The predicted number of deaths per week have been taken from the mortality projections made once every year by Statistics Netherlands (CBS).

Where do these figures come from?

RIVM provides these figures as open data.

How are the figures determined?

The GGD carries out contact tracing in relation to people who have tested positive for coronavirus. This includes looking at the setting in which someone may have become infected. RIVM has defined 21 possible settings. For the overview on the dashboard these 21 settings have been grouped into eight categories. The overview below shows the eight categories and the settings they cover.

1. At home and visiting

  • Home setting (household members, including non-cohabiting partners)
  • Home visits (by or to family, friends, etc.)

2. Work

  • Work setting

3. School and childcare

  • School and childcare

4. Healthcare

  • Primary healthcare / family doctor (GP)
  • Secondary healthcare / hospital
  • Other healthcare
  • Nursing home or residential care home for the elderly
  • Residential care home for people with disabilities
  • Other residential care home
  • Daycare for the elderly and people with disabilities

5. Gatherings

  • Celebrations (party, birthday, drinks, wedding, etc.)
  • Student society/activities
  • Leisure (e.g. sports club)
  • Religious gatherings
  • Choir
  • Funeral

6. Travel

  • Fellow traveller / travel / holiday
  • Flights

7. Restaurant, café or bar

  • Restaurant, café or bar

8. Other

  • Other

If many people test positive in a safety region, this can put severe pressure on the contact tracing process. This may mean that the GGD cannot carry out regular contact tracing and that there is therefore insufficient data for that region. In this case there will be no information on the dashboard for that region for that particular period.

Watch the GGD GHOR video (in Dutch) about contact tracing

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.

Where do these figures come from?
On Thursday of each week, the Netherlands Institute for Health Services Research (NIVEL) reports the figures from general practitioners on patients with COVID-19-like symptoms. This data is available in the form of an open data file.

How are these figures determined?
Once a week, a representative sample of around 350 GP practices across the Netherlands provide data concerning the symptoms of patients they have seen to NIVEL’s Primary Care Database. Using this data, NIVEL determines the number of patients who have reported COVID-19-like symptoms in the past week. NIVEL determines this on the basis of the diagnostic codes reported by GPs and any additional information provided that is suggestive of COVID-19. These diagnostic codes are:

  • acute infection of the upper airways
  • other infections of the airways
  • influenza
  • pneumonia
  • other viral disease
  • other infectious disease
  • fever
  • shortness of breath
  • cough

In order to achieve greater accuracy, each week NIVEL also recalculates the data from the preceding weeks. It also includes data that is only published at a later date.

Where do these figures come from?
Once every three weeks the National Institute for Public Health and the Environment (RIVM) delivers the results of a behavioural survey. This data is available in the form of an open data file.

How are these figures determined?
Every three weeks the RIVM conducts a survey to determine the extent to which people comply with and support the coronavirus measures. Around 5,000 people aged 16 and over participate in this survey.

Changes in the figures
Since the 12th round of the survey, which was conducted from 11 to 17 May 2021, the RIVM has amended the questions about two rules.

Curfew
The curfew was lifted on 28 April 2021. Accordingly, RIVM no longer measures compliance with and support for this rule. On the dashboard we show the figures relating to the curfew up to 26 April.

Stay at home if you have symptoms
Up to and including the 11th round of the survey, RIVM may have incorrectly included some people in the group that ignored the basic rule of staying at home if they had symptoms. These were people who had symptoms suggestive of COVID-19 and who did leave their homes, but only after they had tested negative. In other cases, people may have had an urgent medical reason for leaving their homes without first testing negative. While these individuals may not have stayed at home, they did in fact correctly observe the rule. RIVM has therefore rephrased the question relating to this rule.

Because RIVM has rephrased the question, we only show the data based on the new question on the dashboard. The older data is still available in the RIVM’S open data file.

Where does the data come from?
The data for the CoronaMelder app come from the Ministry of Health, Welfare and Sport. This data is available as open data.

How are the figures calculated?
Alerts for infection

The dashboard shows how many CoronaMelder users who tested positive alerted other app users that they may be infected. This figure, taken from the GGD portal, is based on the number of codes shared each day.

Number of downloads
We also show how many people have downloaded the CoronaMelder app. This figure is calculated by adding together the daily statistics for downloads via:

  • Google Play Store (Android)
  • App Store Connect (iOS)
  • AppGallery Connect (Huawei)

Where do the figures come from?
Confirmed cases
The National Institute for Public Health and the Environment (RIVM) supplies the figures as open data.

Hospital and ICU admissions
RIVM supplies these figures as open data. For each age group, the graph shows the total number of admissions per week (source NICE, processed by RIVM).

Age distribution of the Dutch population
Figures on the age distribution of the population come from open data from Statistics Netherlands (CBS).

How are these figures arrived at?
Confirmed cases
The figures in the graph showing positive tests per age group come from a different data source than the figures on confirmed cases. because there are no ages in the dataset on positive tests. Different dates are used in the two datasets.

The dates used in the age distribution data set are:

  • the first day of illness
  • if this is unknown, then the date of the first positive laboratory test result
  • if this date is also unknown, the date on which the municipal health services (GGD) were notified of the positive result.

The data set uses the date reported by RIVM. This date is usually later than the actual date of the positive test result. This explains why there are differences between the graph that shows the total number of confirmed cases and the graph that shows positive cases per age group. The figures in the age distribution graph are seven-day rolling averages. In addition, sometimes the age of someone who tests positive is unknown.

The graph showing the age distribution of positive tests gives averages over the preceding 7 days. In order to be able to compare age groups, we calculate numbers per 100,000 people.

Hospital and ICU admissions
The data contains the weekly totals of the admissions that took place in that week per age group (source NICE Foundation, edited by RIVM). The graphs for hospital and ICU admissions show these weekly totals. To be able to compare age groups, we calculate numbers per 1,000,000 people from a specific age group. We determine the population size per age group on the basis of the CBS classification.

Example
On 1 October 2020, the 7-day average of the number of positive tests in people aged 90 and older was 26.14. There are a total of 129,831 people aged 90 and older in the Netherlands. So the number of positive tests per 100,000 in the 90 and older age group is 100,000 / 129,831 * 26.14 = 20.13.

Calculating this for each age group and depicting the figures in one graph creates a clear picture of each age group, and allows us to compare the groups with one another.

The national average shown in the age graph is the average of all those cases whose age is known. For example, comparing the 90+ age group with the national average, the national average is made up of all age groups, including the 90+ group.

In the age distribution graph, the data from the last few days is not yet complete. RIVM supplements the data retrospectively and may also make corrections.

Where do the numbers come from?
The population numbers per municipality come from the national office Statistics Netherlands (CBS), reference date 1 January 2020.

View the figures from Statistics Netherlands (CBS) on the population numbers

How are these figures arrived at?
To be able to compare municipalities, we convert some figures into figures per 100,000 people. We do this so we can compare larger municipalities and smaller municipalities. If we did not do that, municipalities such as Amsterdam and Rotterdam would always have high figures, because many more people live there compared to smaller municipalities.

Example:
In Katwijk, a total of 47 people had a positive test result on 3 May 2021.
According to figures from Statistics Netherlands, 65,753 people live in Katwijk.
The calculation we make is 47 / 65,753 * 100,000 ≈ 71.5.
On 3 May, 71.5 people per 100,000 people had a positive test result in Katwijk.

In Amsterdam, a total of 455 people had a positive test result on 3 May 2021.
According to figures from Statistics Netherlands, 872,757 people live in Amsterdam.
We make the same calculation: 455 / 872,757 * 100,000 = 52.1.
So, 52.1 people per 100,000 people in Amsterdam had a positive test result on 3 May.

Amsterdam has more inhabitants than Katwijk, but by converting to positive test results per 100,000 people, we can compare the two municipalities. On May 3, 2021, more people per 100,000 inhabitants had had a positive test result in Katwijk that day than in Amsterdam.

Because the number per 100,000 people is a calculation, it is possible the number has a decimal point.

Where do the numbers come from?
The population numbers per municipality come from the national office Statistics Netherlands (CBS), reference date 1 January 2020.

View the figures from Statistics Netherlands (CBS) on the population numbers

How are these figures arrived at?
To find out what the situation is like in a safety region, we look at how many people from a safety region were admitted to hospital in the past 7 days per 1 million inhabitants. We do this so we can better compare large and small safety regions with one another. If we didn’t do this, densely populated safety regions such as the safety region Utrecht would have higher figures, because more people live there than in less densely populated safety regions.

Example:
In the safety region Groningen, a total of 38 people were hospitalized in the period from 3 to 9 May 2021.
According to figures from the CBS, there were 585.866 people living in the safety region Groningen on that date.
The calculation we then make is 38 / 585.866 * 1.000.000 ≈ 64.9.
So on from 3 to 9 May, a total of 64.9 people per 1.000,000 inhabitants were hospitalized in the safety region Groningen.

In the safety region Utrecht, a total of 71 people were hospitalized in the period from 3 to 9 May 2021.
According to figures from the CBS, there were 1.354.834 people living in the safety region Utrecht on that date.
The calculation we then make is 71 / 1.354.834 * 1.000.000 = 52.4.
So on from 3 to 9 May, a total of 52.4 people per 1.000,000 inhabitants were hospitalized in the safety region Utrecht.

More people live in the safety region Utrecht than in the safety region Groningen, but by calculating the hospitalizing’s per 1.000.000 people we are able to compare the two safety regions. From 3 to 9 May 2021 more people per 1.000.000 inhabitants in the safety Groningen were admitted to hospital than in the safety region Utrecht.

Because the number per 1.000.000 people is a calculated rate and therefore does not express an exact number of people, it can be rounded off to the nearest tenth (one decimal place).

In various graphs on the dashboard, in addition to the figures per day, we also show seven-day rolling averages. The graphs based on daily figures fluctuate more strongly with the day-to-day differences. By adding up all figures over the last seven days and dividing them by seven, an average of the past seven days is created. Together, these averages (the rolling average) give a clear picture of the trend.

Example
On Monday 10 May, the average over the past seven days was the average of 3 to 9 May, inclusive. We added up all the figures for those days and divided the total by seven. On Tuesday 11 May we did the same, but over the days from 4 to 10 May, inclusive. This means that every day we get a new average over the past seven days, and we are able to examine the trend closely.

On the dashboard we use ‘value of’ to indicate the date to which the figures refer, while ‘obtained on’ is used to refer to the date on which we received the data. This clearly shows how up-to-date the data is, in particular for indicators that look back over time.

An example of a subject where ‘obtained on’ tells us more about how current the data is than ‘value of’ is the reproduction number (R). RIVM publishes this number twice a week, but the R number is always for a date two weeks in the past.

  • 'value of’ indicates a date two weeks ago
  • ‘obtained on’ indicates the date on which RIVM published the most up-to-date value of R.

So, for example, this will appear on the dashboard: ‘Value for Thursday 13 May obtained on Friday 28 May’.

On 1 January 2021 a number of municipalities were merged, forming new municipalities. As of 7 January, RIVM has processed the data it provides for the Coronavirus Dashboard in accordance with these new municipal boundaries. We have modified the municipal and safety region maps on the dashboard to reflect the boundary changes. We have added the 2020 figures from municipalities that no longer exist to the figures of the municipalities that they have been merged with.