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Vorlesungsverzeichnis >> Medizinische Fakultät (Med) >>
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Particle Simulators for Infection Tracking- 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:
Vorbesprechung: 4.11.2020, 16:15 - 17:45 Uhr
- Voraussetzungen / Organisatorisches
- Link to the online introduction/Vorbesprechung: https://fau.zoom.us/j/96876884625?pwd=bm1aZWc4UnYyYllnMFc1bkszVWUrZz09
- ECTS-Informationen:
- Credits: 5
- Prerequisites
- Useful knowledge:
Python, data analytics.
- Contents
- Background:
Particle models can be used to simulate population behaviour. Using the simulated behaviours, we can analyse the different components that affect spreads 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 what the social, cultural, and geographic factors that affect the efficiency of various interventions are. And more importantly, we can evaluate the economical and social impacts of the selected intervention strategies.
Aim:
Implement an intervention analysis 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/2020/07/21/seminar-particle-simulators-for-infection-tracking/
Für diese Lehrveranstaltung ist eine Anmeldung erforderlich.
Die Anmeldung erfolgt von Dienstag, 15.9.2020, 08:00 Uhr bis Donnerstag, 12.11.2020, 18:00 Uhr über: StudOn.
- Verwendung in folgenden UnivIS-Modulen
- Startsemester WS 2020/2021:
- Advanced Context Recognition (ACR)
- Institution: Lehrstuhl für Digital Health
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