Social network-based distancing strategies to flatten the COVID-19 curve in a post-lockdown world
Last updated: 1st May, 2020
This is a repository to house the code for the paper `Social network-based distancing strategies to flatten the COVID-19 curve in a post-lockdown world'. The full version of the working paper is available on arXiv.
Code by Marion Hoffman and Per Block.
Abstract: Social distancing and isolation have been introduced widely to counter the COVID-19 pandemic. However, more moderate contact reduction policies become desirable owing to adverse social, psychological, and economic consequences of a complete or near-complete lockdown. Adopting a social network approach, we evaluate the effectiveness of three targeted distancing strategies designed to ‘keep the curve flat’ and aid compliance in a post-lockdown world. These are limiting interaction to a few repeated contacts, maintaining similarity across contacts, and the strengthening of communities via triadic strategies. We simulate stochastic infection curves that incorporate core elements from infection models, ideal-type social network models, and statistical relational event models. We demonstrate that strategic reduction of contact can strongly increase the efficiency of social distancing measures, introducing the possibility of allowing some social contact while keeping risks low. This approach provides nuanced policy advice for effective social distancing that can mitigate adverse consequences of social isolation.
License
This work is free. You can redistribute it and/or modify it under the terms of the MIT license. It comes without any warranty, to the extent permitted by applicable law.
Suggested citation:
Block, P., Hoffman, M., Raabe, I. J., Dowd, J. B., Rahal, C., Kashyap, R., & Mills, M. C. (2020). Social network-based distancing strategies to flatten the COVID 19 curve in a post-lockdown world. arXiv preprint arXiv:2004.07052.