The backend of EMT comprises two complementary approaches to:
⇒ SIMULATION: The Small-Scale Model (SSM) aims to predict the distribution of incoming asylum-seekers/unrecognised refugees arriving to neighbouring countries of conflict origins. It uses a generalised and automated simulation development approach and the Flee agent-based simulation code, which is optimised for simplicity and flexibility. The SSM synthesises data from the United Nations High Commissioner for Refugees (UNHCR), the Armed Conflict Location and Event Data Project (ACLED), OpenStreetMap and population data using the City Population database or other population sources. The conflict model is constructed, run and validated by comparing the simulation results to the existing camp registrations obtained from UNHCR.
⇒ FORECASTING: The Large-Scale Model (LSM) produces monthly predictions of asylum applications in the EU for a variety of bilateral (i.e., from country of origin to the EU Member State) cases. It uses state of the art machine learning approaches, including neural network architectures and time series analysis. Its techniques allow for correlation analysis between raw data sources and simulation. Furthermore, the LSM provides intuitions on attitudes towards migration among populations in all European destination countries, using the Twitter Sentiment Analysis model data as input, and the most influential or relevant determinants of attitudes towards migration. The LSM combines a set of different inputs and methods from Topic Modeling by monitoring national press and asylum seeker data from Eurostat (the official EU statistics office).