The RESPINOW-Hub is a collaborative project on nowcasting and short-term forecasting of the incidence of respiratory diseases and the resulting healthcare burden in Germany. The aims of the project are twofold:

The project covers data on multiple respiratory diseases (invasive pneumococcal disease, RSV, seasonal influenza) as well as combined indicators from syndromic surveillance.

Currently, the project is in a beta phase, where much of the computational infrastructure is operational, but prediction models are still in development.

While this platform does not address SARS-Cov2/COVID-19, it is inspired by projects on COVID-19, e.g., the German COVID-19 Hospitalization Nowcast Hub and the European COVID-19 Forecast Hub.


The RESPINOW-Hub is run by the Chair of Statistical Methods and Econometrics at Karlsruhe Institute of Technology. Furthermore, researchers from the following institutions are involved:

Researchers from the following institutions participate as external collaborators:

The data

In the following, the data sources underlying the RESPINOW-Hub are summarized.

Arbeitsgemeinschaft Influenza

The Arbeitsgemeinschaft Influenza sentinel surveillance system consists of more than 600 general practitioners, who on a voluntary basis provide information on the number of consultations for respiratory infections. Reporting is done directly to RKI either electronically (SEED-ARE) or by fax. We use the consultation incidence for acute respiratory infections (ARI; ICD-10 codes J00 – J22, B34.9 and J44.0) per 100,000 inhabitants. This indicator is not specific to one pathogen and thus forms part of syndromic surveillance. Data are in principle available per age group and region (with certain pairs of German states merged), but we currently only use data at the national level.

Further information:

Clinical Virology Network


Further information:


Around 70-80 hospitals from a large hospital operator (Helios Kliniken GmbH) report new hospitalizations due to severe acute respiratory infections (SARI). This hospital sentinel system covers 13 out of 16 German states and 5-6% of all hospitalizations in Germany. SARI is defined according to a set of ICD-10 diagnostic codes (J09 – J22), meaning that the indicator is syndromic not specific to one pathogen.

Further information:


For a large number of communicable diseases, laboratory-confirmed cases are notifiable in Germany. Local health authorities (Gesundheitsämter) receive reports from general practitioners and laboratories, and via state-level administrations (Landesbehöden) forward them to Robert Koch-Institute. The SurvStat system aggregates the resulting data. In our platform, data on invasive pneumococcal disease, RSV (only covering the state of Saxony) and seasonal influenza are displayed. Data are available by state and age group (note that we aggregated certain pairs of states so regions agee with the regions of Arbeitsgemeinschaft Influenza).

It should be noted that laboratory analyses are on only performed for a fraction of all patients, meaning that SurvStat only covers part of the actual disease incidence. Reporting completeness can vary over time and depends on many aspects, including healthcare seeking behaviour and reimbursement policies.

Further information:

Virological Surveillance (National Reference Center)

Roughly 20% of the sentinel GP practices participating in Arbeitsgemeinschaft Influenza are equipped to perform nose swabs. They collect samples according to a set of symptoms and an age stratification. Samples are tested for a variety of pathogens, but we only display data on RSV and seasonal influenza.

Further information:

The models

Most models are still under development and do not feed into the operational platform yet. Currently, the following models are displayed (in alphabetical order):

KIT-epinowcast This nowcasting model combines a latent random walk model for the actual epidemic curve with a parametric delay model. Inference is done in a Bayesian fashion. The implementation is based on the R package epinowcast and can be found here.
KIT-hhh4 This is a simple seasonal count time series model as implemented in the R packages surveillance and hhh4addon. It is built on top of the KIT-simple_nowcast and currently only applied to SARI data (influenza having too irregular seasonality to be captured well by this model). The implementation is available here.
KIT-simple_nowcast This nowcasting model is based on simple multiplication factors based on recently observed reporting delays. Uncertainty intervals are based on past nowcast errors using a parametric negative binomial model. A model description (for a slightly more complex setting of daily data) is available in the supplement of Wolffram et al (2023). The implementation can be found here.
RIVM-KEW This model is a simplified version of the model presented by van de Kassteele, Eilers and Wallinga (2019). The reported counts by date and delay are described by a negative binomial distribution. The expected values are modelled by a two-dimensional P-spline surface and other covariates. This surface is extrapolated for all dates and delays outside the reporting triangle. Model fitting is done using the mgcv package in R.


In the following we provide brief explanations of relevant terms. A similar list (in German) is available on the website of Arbeitsgemeinschaft Influenza.

Acute respiratory infection (ARI) Acute respiratory infection (ARI) is a summary term for illness from various respiratory pathogens and refers to a combination of symptoms (“acute pharyngitis, bronchitis or pneumonia with or without fever”). The exact definition is based on ICD-10 diagnostic codes (J00 – J22, B34.9 and J44.0; see e.g., here, page 86; in German). More information can also be found here on the website of Robert-Koch-Institut.
Collaborative forecasting Forecasting infectious disease spread and the resulting healthcare burden is challenging and experience shows that different models may lead to rather different forecasts. It is thus considered good practice to use several independently operated forecasting models in parallel; see e.g., this editorial by Reich et al (2023). This enables the identification of reliable models and the combination of different forecasts into so-called ensembles predicitons (see below).
Ensemble prediction Ensemble predicitons are combinations of forecasts from different models or methods. These have been found to be more more reliable than individual models in many disease forecasting efforts (see e.g., Cramer et al , 2022). They are also very commonly used in other domains like meteorological and economic forecasting.
Nowcasting Most epidemiological indicators are subject to reporting delays, meaning that events which happened in a given week only appear in the respective data set some time later. This means that the most recent data points are often incomplete and will still be subject to upwards corrections. This can lead to an artificial dip and the wrong impression of a downward trend. Statistical nowcasting serves to anticipate thiese corrections and thus determine relevant trends in real time. A good overview on the topic is given in the paper by Günther et al (2020).
Invasive pneumococcal disease Pneumococcal diseases are caused by the bacterium Streptococcus pneumoniae. While a large part of the population is colonized by Streptococcus pneumoniae, i.e., carries the bacterium in their body, this does usually not lead to illness. Invasive pneumococcal disease (IPD) refers to symptomatic cases where the bacterium can be isolated from normally sterile sites.
Respiratory Syncytial Virus (RSV) Respiratory syncytial virus (RSV) is a common respiratory disease which typically causes mild forms of infection, but can be more severe for young children and elderly persons. Vaccines against RSV have become available in recent years.
Sentinel surveillance Sentinel surveillance (see also here in German) refers to a surveillance scheme where selected general practitioners, hopsitals etc. provide more detailed information on the occurrence of infectious diseases than required in the mandatory reporting schemes. Based on the catchment areas of these healthcare providers, estimates for disease activity or healthcare burden in the entire population can be obtained.
Severe Acute Respiratory Infection (SARI) Severe acture respiratory infections represent a subset of respiratory infections with particularly strong symptoms. The exaxt definition is again formulated in terms of ICD-10 diagnostic codes (J09 – J22; see e.g., here, page 86; in German). Hospitalizations due to SARI are subject to sentinel surveillance in Germany, see above and e.g., here.
Short-term forecast Forecasts of infectious disease spread are typically only feasible for rather short time horizons (see e.g., here for a detailed account). Depending on the characteristics of a disease and the epidmeiological situation this duration typically reaches from a few days to several weeks. Based on experience, we consider 2-3 weeks a reasonable maximum forecast horizon for the respiratory diseases in question. Beyond these horizons, so-called scenario projections are used to make statements under various sets of assumptions (see e.g., Howerton et al 2023).
Syndromic surveillance Syndromic surveillance is a summary term for surveillance techniques that are based on symptoms of affected persons rather than the identification of the causative pathogen. The surveillance of ARI and SARI are examples of syndromic surveillance.

Getting involved

The RESPINOW-Hub is open to new collaborators. If you are interested in the modelling of respiratory diseases (whether based in Germany or abroad), do not hesitate to get in touch.


RESPINOW-Hub is part of the RESPINOW Consortium, which in turn is part of the MONID network, funded by the German Ministry of Education and Research.

Contact / impressum

Responsible: Dr. Johannes Bracher, Institute of Economics, Karlsruhe Institute of Technology, Tel +49 721 60848114, You can also contact us at

RESPINOW-Hub is part of the RESPINOW Consortium, which in turn is part of the MONID network, funded by the German Ministry of Education and Research.

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