Abstract
People tend to limit social contacts during times of increased health risks, leading to disruption of social networks thus changing the course of epidemics. To what extent, however, do people show such avoidance reactions? To test the predictions and assumptions of an agent-based model on the feedback loop between avoidance behavior, social networks, and disease spread, we conducted a large-scale (2,879 participants) incentivized experiment. The experiment rewards maintaining social relations and structures, and penalizes acquiring infections. We find that disease avoidance dominates networking decisions, despite relatively low penalties for infections; and that participants use more sophisticated strategies than expected (e.g., avoiding susceptible others with infectious neighbors), while they forget to maintain a beneficial network structure. Consequently, we observe low infection numbers, but also deterioration of network positions. These results imply that the focus on a more obvious signal (i.e., infection) may lead to unwanted side effects (i.e., loss of social cohesion).
| Original language | English |
|---|---|
| Article number | 22586 |
| Journal | Scientific Reports |
| Volume | 13 |
| Issue number | 1 |
| DOIs | |
| Publication status | Published - Dec 2023 |
Bibliographical note
Publisher Copyright:© 2023, The Author(s).
Funding
A special thanks goes to Alvaro Uzaheta and Christoph Stadtfeld from the Social Networks Lab at ETH Zurich for their feedback and our discussions that helped to shape that paper. We thank our colleagues from the Sociology department and the Infectious Disease Modeling Group at Utrecht University, as well as the other team members from the Social Networks Lab at ETH Zurich for comments that greatly improved the manuscript.
