Among the biggest challenges in the COVID-19 outbreak are the lack of triangulation of clinical, epidemiologic and immunological information for evidence-based response strategies. The consortium CORESMA (COVID-19-Outbreak Response combining E-health, Serolomics, Modelling, Artificial Intelligence and Implementation Research) combines an accelerated ad-hoc outbreak response to address the urgency and a sustainable strategy to serve beyond the current public health threat from COVID-19. Our approach is innovative in combining unique mHealth technology, multiplex serolomics, state of the art modelling, artificial intelligence and implementation research, also applying them to particularly vulnerable countries outside Europe.

CORESMA aims to immediately generate the most needed clinical and epidemiological data needed for defining targeted public health measures at national and global level, early enough to become effective during this outbreak. Furthermore, the consortium intends to develop and establish tools and methodologies to improve the global public health preparedness for outbreaks emerging in the future.


CORESMA started in April 2020 and will run until December 2023. The consortium is now coordinated by Berit Lange, Head of the Department of Epidemiology at the Helmholtz Centre for Infection Research (HZI), Germany. Members are European researchers from the Netherlands, Switzerland and Germany, as well as partners from China, Ivory Coast and Nepal.


  • To provide real-time clinical data to improve risk assessment and response, deploying an established mHealth Surveillance Outbreak Response Management and Analysis System (SORMAS) in Nepal, Ivory Coast, Ghana and Nigeria - countries likely to be affected more intensively than the EU (WP1).
  • To implement differential serolomics (multiplex serology) for population serum samples from Germany and Nepal for investigating pre-existing cross or partial immunity against COVID-19 and impact on susceptibility (WP2).
  • To apply comprehensive modelling, sampling and artificial intelligence on data from the first two work packages in order to assess predictors for severe outcome, transmission dynamics and intervention effectiveness (WP3).
  • To measure and improve the quality of epidemic containment measures through implementation research in countries particularly vulnerable to the COVID-19 epidemic, in order to tailor effective and efficient control measures to health systems realities in Nepal and Ivory Coast, and to reduce the intensity of importation into the EU (WP4).

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