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Routine health data in real-time (SUMO)

SUMO is a system that processes and analyses routine health data in real-time for the use in public health research.

SUMO has been under continuous development, by close collaboration between epidemiologists, software developers and data scientists at the Robert Koch Institute (RKI). The system is being piloted since April 2020. The SUMO Team develops data based applications to support stakeholders in public health, and fosters public health and methodological research. The results of our research aim to advance and improve the automated data processing.

Routinely collected and anonymised emergency department data are the first data to be provided for SUMO. Through the collaboration with the AKTIN emergency department register and the ESEG project, data from over 20 emergency departments in Germany have been collected and used for syndromic surveillance. In the emergency department situation report (SitRep) data on emergency department utilisation are analysed and published on a weekly basis.

Current emergency department situation report

situation report (16.6.2021) (PDF, 919 KB, File does not meet accessibility standards.)


The base of our research is the integration and provision as well as the merging of different data sources and the display of data quality. The integration of additional emergency departments and other data sources is already planned. We value close collaboration between epidemiologists, computer scientists and data scientists for pipeline and application development. This collaboration ensures that the automatically pre-processed data and the developed methods can be used for meaningful applications and that research results can be reproduced at any time. It is our aim to share our findings publicly along with the data and tools we use.


  • weekly emergency department situation report (SitRep)
  • applications to assist our research (e. g. tools to develop and evaluate syndrome definitions)
  • quality reports (for participating emergency departments)

Methodological Research

  • development, optimisation and evaluation of syndrome definitions
  • automated learning of syndrome definitions (using supervised learning methods)
  • optimisation of data quality in real-time processing
  • methods development for surveillance of seasonal variations, monitoring of selected health phenomena, and impact analyses of public health measures

Software Development

  • automation of a generic pipeline for real-time data processing
  • test development for quality assurance
  • integration of expert knowledge into the pipeline


We collaborate closely with the AKTIN Emergency Department Data Registry and the ESEG project partners. We are grateful for the trust and cooperation of all participating emergency departments.
We welcome every emergency department that is interested in cooperating. Please contact Wiebke Schirrmeister.

This infographics shows the data flow from the data sources via the standardization and epidemiological classification to through the research and application layers.


  • Sonia Boender (Epidemiologist)
  • Madlen Schranz (Epidemiologist)
  • Linus Grabenhenrich (Epidemiologist)
  • Theresa Kocher (Software Developer, Data Scientist)
  • Birte Wagner (Data Scientist)
  • Alexander Ullrich (Data Scientist)

Date: 16.06.2021