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Paradoxes in the co-evolution of contagions and institutions

Cite this dataset

St-Onge, Jonathan et al. (2024). Paradoxes in the co-evolution of contagions and institutions [Dataset]. Dryad. https://doi.org/10.5061/dryad.3ffbg79s8

Abstract

Epidemic models study the spread of undesired agents through populations, be it infectious diseases through a country, misinformation in social media, or pests infesting a region. In combating these epidemics, we rely neither on global top-down interventions, nor solely on individual adaptations. Instead, interventions commonly come from local institutions such as public health departments, moderation teams on social media platforms, or other forms of group governance. Classic models, which are often individual or agent-based, are ill-suited to capture local adaptations. We leverage developments of institutional dynamics based on cultural group selection to study how groups attempt local control of an epidemic by taking inspiration from the successes and failures of other groups. Incorporating institutional changes into epidemic dynamics reveals paradoxes: a higher transmission rate can result in smaller outbreaks as does decreasing the speed of institutional adaptation. When groups perceive a contagion as more worrisome, they can invest in improved policies and, if they maintain these policies long enough to have impact, lead to a reduction in endemicity. By looking at the interplay between the speed of institutions and the transmission rate of the contagions, we find rich co-evolutionary dynamics that reflect the complexity of known biological and social contagions.

README: Paradoxes in the co-evolution of contagions and institutions

https://doi.org/10.5061/dryad.3ffbg79s8

We use group-based models as set of master equations to study the impact of institutional policies on contagion. We find that incorporating institutional changes into epidemic dynamics reveals paradoxes: a higher transmission rate can result in smaller outbreaks as does decreasing the speed of institutional adaptation.

See README.md within the included zip file for more details. 

Code/Software

The code to reproduce the results of this paper can be found at jstonge/hello-gmes/src/examples. The code depends on the Git submodule jstonge/InstitutionalDynamics.jl, which contain the model written in Julia. Make sure to follow the instructions on the README to get started. In addition to the script to reproduce the results of the paper, the repository contains code to run the associated Observable Framework app (a live version can be found here). If you are interested in the full data pipeline of the project, please consult the Makefile where we keep track in details of the flow of our data.

Funding

Alfred P. Sloan Foundation

European Commission, Award: 945413

National Science Foundation, Award: EPS-201947

National Institute of Food and Agriculture, Award: VT0095CG