Use Cases

The recent Covid-19 outbreak has had a tremendous impact on the world, and many countries are struggling to help incoming patients and at the same time, rapidly enact new public health measures such as lock downs. Many of these decisions are guided by the outcomes of so-called Susceptible Exposed-Infectious-Recovered (SEIR) models that operate on a national level. We introduce the Flu And Corona virus Simulator (FACS)1, a simulation tool that models the viral spread at the sub-national level, incorporating geospatial data sources to extract buildings and residential areas within a predefined region.

In the last few years, a huge amount of people were forced to leave their homes. One of the major issues hereby is to forecast where these displaced people will arrive, which would allow decision makers and NGOs to allocate humanitarian resources accordingly. To predict possible destinations of refugees coming from conflict regions, we have developed a simulation framework. This framework relies on agent-based simulations, and makes use of real world data from UNHCR, ACLED, and Bing Maps. Applying this simulation framework to three major African conflict regions, we obtain results which consistently predict more than 75% of the refugee destinations correctly after 12 days.

Social networks are omnipresent in our daily life. Facebook has as much as 1.4 billion daily active users and on Twitter around 500 million tweets are posted every day. Messages spreading on social networks have a significant influence on our social and economic behaviour. One of the main goals of this use case is to understand the spread of messages as well as the influence of social networks. As a second goal, we plan to identify false/malicious messages, which intend to change the behaviour of a substantial number of users. Additionally, we aim to develop countermeasures on the algorithmic level in order to prevent the spread of such false messages on a large scale. Finally, we will develop a highly scalable simulation framework for such stochastic processes in real-world networks, in order to be able to analyse and predict the impact of these processes on the society.

The vision of HiDALGO’s urban air pollution application is to create cleaner air in cities by using high performance computing (HPC) and mathematical technologies. To this end, the project will provide policy makers and society with an easy-to-use computational tool as a service that accurately and quickly forecasts air pollution in cities with very high resolution. Furthermore, a traffic control system will be developed as well to minimize air pollution while considering traffic flow constraints.