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OptimAgent_GEMS
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BerylMusundi authored Sep 20, 2024
commit 4029ada11ddaa723ce3187e97c47b83b56216055
42 changes: 42 additions & 0 deletions model-metadata/OptimAgent_GEMS.yml
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team_name: OptimAgent
model_name: German Epidemic Micro-Simulation System
team_abbr: OptimAgent
model_abbr: GEMS
model_contributors:
- name: Aleksandr Bryzgalov
affiliation: Martin-Luther-University, Halle-Wittenberg
email: [email protected]
- name: Beryl Musundi
affiliation: Martin-Luther-University, sHalle-Wittenberg
email: [email protected]
- name: Johannes Ponge
affiliation: University of Muenster
email: [email protected]
- name: Janik Suer
affiliation: University of Muenster
email: [email protected]
- name: Tyll Krueger
affiliation: Wroclaw University of Science and Technology
email: [email protected]
- name: Mahreen Kahkashan
affiliation: Martin-Luther-University, Halle-Wittenberg
email: [email protected]
- name: Wolfgang Bock
affiliation: Linneaus University
email: [email protected]
- name: Johannes Horn
affiliation: Martin-Luther-University, Halle-Wittenberg
email: [email protected]
- name: Mirjam Kretzschmar
affiliation: Utrecht University
email: [email protected]
- name: Rafael Mikolycjzyk
affiliation: Martin-Luther-University, Halle-Wittenberg
email: [email protected]
- name: Alexander Kuhlmann
affiliation: Martin-Luther-University, Halle-Wittenberg
email: [email protected]
methods: An agent-based modelling framework tailored for the German population to simulate epidemics.
data_inputs: RKI
team_model_designation: primary
methods_long: GEMS is an agent-based mathematical modelling framework tailored for the German population. Individuals in the model have attributes such as age and gender and are associated with contact settings that include households, schools, workplaces and municipality. Disease progression is age-dependent and seasonality effects are included. The model is calibrated using reported cases of hospitalizations and used to forecast COVID-19 infections and hospitalizations.