Welcome to the Contagion Models module! This module explores how ideas, behaviors, diseases, and innovations spread through social networks and populations. Building on threshold models from our segregation module, we’ll examine both biological and social contagion processes using computational modeling approaches.
Contagion models help us understand how things spread—from infectious diseases to social movements, from rumors to technological innovations. Through agent-based modeling, we’ll explore different mechanisms of transmission, the role of network structure, and intervention strategies for controlling or promoting different types of contagion. We’ll see how Granovetter’s threshold models Granovetter (1978) provide a foundation for understanding collective behavior and social mobilization. We will also will talk about the spread of behavior in social networks, including concepts like complex contagion and the role of weak vs. strong ties.
Module Duration: 2 weeks
Student Learning Objectives (SLOs)¶
By the end of this module, students will be able to:
- Construct data-driven, mathematical, statistical, and/or software models, analyzing their results to answer questions, solve problems, support arguments, draw conclusions, make predictions, and/or identify possible causal relationships.
- Identify and use tools appropriate for solving a given problem, such as algebra, calculus, and other mathematical tools; spreadsheets, databases, and data-analysis software; domain-specific software; and/or writing one’s own software.
- Distinguish between different types of contagion (biological, social, behavioral)
- Explain the role of network structure in contagion processes
- Analyze the dynamics of epidemic curves and tipping points
- Understand concepts like basic reproduction number (R₀) and herd immunity
- Connect threshold models to collective behavior and social movements
- Evaluate how individual thresholds aggregate to produce collective outcomes
- Implement SIR and SEIR epidemiological models in NetLogo
- Model contagion on different network topologies
- Create threshold-based models for collective behavior
- Analyze the effects of intervention strategies on spread dynamics
- Visualize and interpret contagion simulation results
- Evaluate the effectiveness of public health interventions
- Assess the parallels and differences between biological and social contagion
- Critique assumptions in contagion models and their real-world applicability
- Analyze the ethical implications of contagion research and policy
- Compare model predictions with real-world contagion phenomena
- Present findings from contagion simulation experiments clearly
- Discuss the policy implications of different intervention strategies
- Explain complex contagion concepts to non-technical audiences
- Connect model insights to contemporary public health and social issues
📋 Weekly Breakdown¶
Week 5: Tuesday, September 30
Heckman Library 406C
Session A: Collective behavior: from contagion to thresholds.
- Summary:
- Historical approaches: Le Bon’s crowd psychology, modern collective behavior theory.
- Threshold models: Granovetter’s framework for understanding collective action.
- Applications: riots, social movements, technology adoption.
- Slides: Collective Behavior
Session B (SRG): Discussion of readings.
- Summary:
- Mark Granovetter, “Threshold Models of Collective Behavior” (AJS, 1978).
- Deliverable: SRG prep sheet due (per role).
Week 5: Thursday, October 2
Heckman Library 406C
Session A: Contagion models: a brief overview.
- Summary:
- Overview of contagion models: SIR, SEIR, and agent-based approaches.
- Discussions on limitations: norms, emotions, networks.
- Slides: Contagion Models
Session B (Lab): Contagion Model in NetLogo.
Week 6: Tuesday, October 7
We won’t have class on Tuesday, due to the Tech Week.
Week 6: Thursday, October 9
Heckman Library 406C
Session A (SRG): Discussion of readings.
- Summary:
- Centola, D. (2010). The Spread of Behavior in an Online Social Network Experiment. Science, 329(5996), 1194–1197. 10.1126/science.1185231
- Centola, D., & Macy, M. (2007). Complex Contagions and the Weakness of Long Ties. American Journal of Sociology, 113(3), 702–734. 10.1086/521848
- Deliverable: SRG prep sheet due (per role).
Session B (Lab): Modeling the SI and SIR models.
- Summary:
- We will build the SI and SIR models in NetLogo during this class. The starting point is the code from previous lecture.
📝 Assignments & Due Dates (Weeks 3–4)¶
Due: 10/16 before class | Points: 20 points
Prompt (1-2 pages):
Contagion model implementation & analysis
- Implement a contagion model in NetLogo using the SEIR framework.
- Analyze the model’s behavior under different parameters (e.g., transmission rate, recovery rate).
- Write your Lab Memo. You can download the template in here.
- Make sure you add the codes you’ve changed, as well as interface modifications.
- Submit your Lab Memo in PDF format through Moodle.
Week 6 (Oct 7 & Oct 9)¶
| Assignment Type | Details | Due Date | Weight |
|---|---|---|---|
| 📖 SRG Prep Sheet #5 | Centola Spread of Behavior in an Online Social Network readings | Thu Oct 9 (start of class) | Participation |
| 🎓 Project Ideas Brainstorming | Meet with your teams & brainstorm initial ideas | Thu Oct 9 | Project milestone (optional) |
📚 Reading and Extra Materials¶
Required Readings¶
Mark Granovetter (1978)
“Threshold Models of Collective Behavior”
American Journal of Sociology, 83(6), 1420-1443.
Key concepts:
- Individual thresholds for participation
- Collective outcomes from individual decisions
- Applications to riots, strikes, and social movements
- Mathematical formalization of social influence
Discussion questions:
- How do individual threshold distributions affect collective outcomes?
- What role does information play in threshold models?
- Can threshold models explain the unpredictability of social movements?
Charles Tilly (2004)
Social Movements, 1768–2004
Paradigm Publishers, Chapter 1.
Key concepts:
- Historical evolution of social movements
- WUNC displays (Worthiness, Unity, Numbers, Commitment)
- Repertoires of contention
- Political opportunity structures
Discussion questions:
- How have social movement tactics evolved historically?
- What makes some movements successful while others fail?
- How do digital technologies change movement organization?
Supplementary Materials¶
- How Behavior Spreads: The Science of Complex Contagions (Damon Centola) Link
- Nicholas Christakis, “Social Contagion” Link
- Nicholas Christakis: The hidden influence of social networks Link
- Feeling Their Vibes? Uncovering the Mystery of Emotional Contagion 🧠💫 Link
- Are Your Emotions Contagious? | On Mirror Neurons Link
- Centola, D. (2018). “How Behavior Spreads”. Chapter 3: “Social Contagion”
- Watts, D. J. (2002). “A simple model of global cascades on random networks”
- González-Bailón, S. (2011). “The dynamics of protest recruitment through online social networks”
- Steinert-Threlkeld, Z. C. (2017). “Spontaneous collective action: Peripheral mobilization during the Arab Spring”
- Virus (Biology section) - Basic epidemic spreading
- Rebellion (Social Science section) - Threshold-based uprising
- Diffusion on a Directed Network - Information spread
- Preferential Attachment - Network formation dynamics
- Granovetter, M. (1978). Threshold models of collective behavior. American Journal of Sociology, 83(6), 1420–1443.
- Centola, D. (2010). The Spread of Behavior in an Online Social Network Experiment. Science, 329(5996), 1194–1197. 10.1126/science.1185231
- Centola, D., & Macy, M. (2007). Complex Contagions and the Weakness of Long Ties. American Journal of Sociology, 113(3), 702–734. 10.1086/521848
- Watts, D. J. (2002). A simple model of global cascades on random networks. Proceedings of the National Academy of Sciences, 99(9), 5766–5771. 10.1073/pnas.082090499