Systems science has been instrumental in breaking new scientific ground in diverse fields such as meteorology, engineering and decision analysis. However, it is just beginning to impact public health. This seminar is designed to introduce students to basic tools of theory building and data analysis in systems science and to apply those tools to better understand the obesity epidemic in human populations. There will also be a lab in which students will use a simple demonstration model of food acquisition behavior using agent-based modeling on standard (free) software (netlogo). The central organizing idea of the course is to examine the obesity epidemic at a population level as an emergent properties of complex, nested systems, with attention to feedback processes, multilevel interactions, and the phenomenon of emergence. While the emphasis will be on obesity, the goal will be to explore ways in which the systems approach can be applied to other non-communicable diseases both nationally and internationally.
Offered By
Systems Science and Obesity
Johns Hopkins UniversityAbout this Course
Offered by

Johns Hopkins University
The mission of The Johns Hopkins University is to educate its students and cultivate their capacity for life-long learning, to foster independent and original research, and to bring the benefits of discovery to the world.
Syllabus - What you will learn from this course
Module 1
Obesity, the facts of the case, and human populations through a systems science lens.
Module 2
Complex systems, system dynamic models, and developing a stock and flow. This module includes a lab lesson.
Module 3
Social networks and obesity, neighborhoods and noshing, and prices and poverty.
Module 4
Agent based modeling and conceptual motivation for agent based modeling. This module includes a lab lesson.
Reviews
- 5 stars72.02%
- 4 stars20.72%
- 3 stars4.66%
- 2 stars2.07%
- 1 star0.51%
TOP REVIEWS FROM SYSTEMS SCIENCE AND OBESITY
It is a great course, new approach to obesity epidemic.
Useful course with excellent learning materials. Thank you!
It is only that the language is little hard for non native speakers
Very interesting and useful tools for analyzing complex networks and designing interventions.
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