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Vorlesungsverzeichnis >> Medizinische Fakultät (Med) >>
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Agent Simulators for Individual Behaviour- Dozentinnen/Dozenten
- Prof. Dr. Oliver Amft, Dr. rer. nat. Luis Ignacio Lopera Gonzalez
- Angaben
- Seminar
Online
4 SWS, benoteter Schein, Anwesenheitspflicht, ECTS-Studium, ECTS-Credits: 5, Sprache Englisch
Zeit:
- Voraussetzungen / Organisatorisches
- Link to the online introduction/Vorbesprechung April 12, 16:15-17:45, https://fau.zoom.us/j/97705596817?pwd=QlFjRlErMEJXWWlqVkFid01tUGJDdz09
- ECTS-Informationen:
- Credits: 5
- Prerequisites
- Useful knowledge:
Python, data analytics.
- Contents
- Background:
Agent models can be used to simulate population behaviour. Using the simulated behaviours, we can analyse the different components that affect the spread of infections. Our current experience with the pandemic has shown that different regions might have a different response to similar intervention strategies. Using the simulator, we can study the social, cultural, and geographic factors that affect the efficiency of various interventions are. And more importantly, we can evaluate the economic and social impacts of the selected intervention strategies.
Aim:
Implement agent behaviours for public spaces and evaluate effects on the population in terms of safe space utilisation, the number of isolations, and the duration of infection wave.
Learning Objectives:
Gain an overview of dynamic system modelling. Explore and understand the features of human behaviour in public places. Apply particle modelling to analyse infection propagation. Create simulator modules to model people behaviour in public space scenarios.
Examination:
Final project presentation, demonstrator and final report.
- Literature
- Up-to-date literature recommendations are provided during the lectures.
- Zusätzliche Informationen
- Schlagwörter: ACR
Erwartete Teilnehmerzahl: 20, Maximale Teilnehmerzahl: 20
www: https://www.cdh.med.fau.de/2021/03/29/seminar-agent-simulators-for-individual-behaviour/
Für diese Lehrveranstaltung ist eine Anmeldung erforderlich.
Die Anmeldung erfolgt von Montag, 29.3.2021, 08:00 Uhr bis Freitag, 23.4.2021, 18:00 Uhr über: StudOn.
- Institution: Lehrstuhl für Digital Health
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