• Hackathons
  • Features
  • Blog

Disease Surveillance in Forced Migration

Forced migration poses great health risks to persons being displaced. We use Copernicus to track migration flows for which we use vertical and horizontal radar data from Sentinel 1.

  • 0 Raised
  • 0 Juries


  • Germany


  • 3. Understanding and forecasting forced migration



Our ultimate goal is to monitor forced migratory flows within specific regions in order to improve resource allocation and management within refugee camps to protect migrant health. 

By integrating different data sources from various satellites, our goal is to predict migration flows that have been caused by natural disasters or civil conflicts, so refugee camps can better prepare to provide the right support to migrants. By doing so, we expect to reduce the risk of infectious disease outbreaks given the precarious infrastructures that are usually encountered along the migrant's journey during displacement events. In addition, this could help prepare better responses in the case of extreme weather conditions when there is an increase in demand for blankets against the cold or water to prevent dehydration when facing high temperatures. Further, this could help international organizations, and clinics have better resource allocation and management thus tremendously reducing costs and waste in addition to saving more lives. 

We utilize vertical and horizontal radar data from Copernicus Sentinel-1 to monitor population dynamics of a campsite. We also utilize the land surface temperature to highlight anomalies in ground temperature to alert camp officers of potential extreme weather conditions. We are evaluating other sources of Copernicus Sentinel data to understand how to provide alerts for floods and potential disease outbreaks due to extreme weather conditions.

Our Team:

Pedro Guzmán is a synthetic biologist, neuroscientist, and entrepreneur based in Berlin with expertise in medical diagnostics and cell and gene therapies.

Karthik Muthuswamy is a data scientist based in Mannheim with expertise in machine learning, statistics, and data science.

Max Hofacker is a geodesist researching autonomous navigation algorithm at the University of the Bundeswehr Munich.

Ekaterina Pavlova is a Java developer based in Munich with expertise in finance and theory of probability.

Galuh Sahid is a Senior Machine Learning Engineer based in Berlin. She is certified as a Google Developer Expert.