Systems Science and Obesity

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Systems Science and Obesity

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Description

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About this course: 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…

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When you enroll for courses through Coursera you get to choose for a paid plan or for a free plan

  • Free plan: No certicification and/or audit only. You will have access to all course materials except graded items.
  • Paid plan: Commit to earning a Certificate—it's a trusted, shareable way to showcase your new skills.

About this course: 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. Topics will include: a) the epidemiology of obesity across time and place, b) theories to explain population obesity, c) the role of environments and economic resources in obesity c) basic concepts and tools of systems science, d) modeling energy-balance related behaviors in context, e) agent-based models, systems dynamic models, and social network models

Created by:  Johns Hopkins University
  • Taught by:  Jessica Jones-Smith, Assistant Professor

    Human Nutrition
  • Taught by:  Rahmatollah Beheshti, Postdoctoral Fellow

    Department of Civil Engineering
  • Taught by:  Daniel Taber, Assistant Professor

    School of Public Health (UT Health)
  • Taught by:  Kayla de la Haye, Assistant Professor of Preventative Medicine

    Keck School of Medicine (USC)
  • Taught by:  Thomas Glass, Professor

    Epidemiology
Level Beginner Commitment 4 weeks of study, 2-3 hours/week Language English How To Pass Pass all graded assignments to complete the course. User Ratings 4.5 stars Average User Rating 4.5See what learners said Coursework

Each course is like an interactive textbook, featuring pre-recorded videos, quizzes and projects.

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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


WEEK 1


Module 1
Obesity, the facts of the case, and human populations through a systems science lens.


7 videos expand


  1. Video: Welcome to the Course
  2. Video: Obesity, The Facts of the Case (Or Why Obesity Pushes Our Buttons) - Tom Glass
  3. Video: Beyond the Usual Suspect: Why This Case Is Hard To Crack
  4. Video: Explanations: The Food Supply, A System of Interest
  5. Video: Obesity in Human Populations Through a Systems Science Lens - Tom Glass
  6. Video: Taking a Systems Perspective: 5 Key Features
  7. Video: What is so "Systemy" About Obesity?

Graded: Module 1

WEEK 2


Module 2
Complex systems, system dynamic models, and developing a stock and flow. This module includes a lab lesson.


6 videos, 1 reading expand


  1. Video: What Is a Complex System?
  2. Video: Nuts and Bolts of System Dynamics Models
  3. Video: Developing a Stock and Flow Model
  4. Video: Policy Resistance
  5. Video: Coding tutorial for systems dynamics model
  6. Video: From stock and flow diagram to developing a SD model in Vensim
  7. Reading: Important: Models for Lab Assessment

Graded: Module 2 Quiz
Graded: Lab 1 Quiz

WEEK 3


Module 3
Social networks and obesity, neighborhoods and noshing, and prices and poverty.


9 videos expand


  1. Video: What Are Social Networks?
  2. Video: Social Network Analysis
  3. Video: Obesity & Social Networks
  4. Video: Neighborhoods and Noshing: Foraging and the Food Environment
  5. Video: Why Should We Care About Neighborhoods for a Systems Science of Obesity
  6. Video: Foraging Theory: A Unifying Framework?
  7. Video: Lecture 6: What's price (and income) got to do with it?
  8. Video: Less money, more problems?
  9. Video: Taxman: Can Price Manipulations Be Used to Promote Healthful Eating

Graded: Module 3 Quiz

WEEK 4


Module 4
Agent based modeling and conceptual motivation for agent based modeling. This module includes a lab lesson.


8 videos, 2 readings expand


  1. Video: Agent-based modeling: A simpler way to understand complexity
  2. Video: Why agent-based modeling?
  3. Video: Development of a Simple Model with Environment, Peer Networks, and Agent Behaviors
  4. Video: Food Foraging ABM:Embedded in a GIS-Based Environment
  5. Video: Lab 2 Part 1: Tutorial on building ABMs in Netlogo environment
  6. Video: Lab 2 Part 2: ABM for Food Store; Food Foraging ABM
  7. Video: Lab 2 Part 3: Income-Diet model example with extensions
  8. Reading: Important: Models For Lab Assessment 2
  9. Video: Congratulations!
  10. Reading: Learn more about the Global Obesity Prevention Center

Graded: Module 4 Quiz
Graded: Lab Quiz 2
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