Social network-based distancing strategies to flatten the COVID 19 curve in a post-lockdown world
Per Block@block_per
How can we devise smart social distancing measures that keep the curve flat?
Our new pre-print introduces a network science approach to alleviate the social and economic costs of social distancing while keeping infections low. 1/8
https://arxiv.org/abs/2004.07052
With complete or near-complete lock-downs deep into their second month in many places,
strategies that allow social and economic life to move closer to a pre-covid world without risking a destructive second wave are sorely needed. 2/8
We introduce a novel approach to the spread of infection
using core elements from infection models, ideal-type social network models, and statistical relational event models.
Our model explicitly allows for individuals making smart choices… 3/8
…whom to interact with based on their local knowledge of social contact in their immediate vicinity.
We suggest strategies that every individual can adopt to curb spread for all of society.
These strategies are based on the insight that infection curves … 4/8
…are closely related to the network concept of path length.
Illustration shows the spread of disease along network ties in networks with same number of people and connections,
but different path lengths from infection source. 5/8
3 strategies we test are
(i) limiting interaction to a few repeated contacts,
(ii) maintaining similarity across contacts, and
(iii) the strengthening of triadic communities.
Each individual-level strategy strongly impacts macro-level disease spread. 6/8
Simulating infection curves on the same network topology with different interaction strategies shows strong effect of each strategy
with repeated contact being most efficient in limiting spread. 7/8