Social network-based distancing strategies to flatten the COVID-19 curve in a post-lockdown world
https://www.nature.com/articles/s41562-020-0898-6
Our class has a clear focus in networks, crowds, and markets. It’s crosslisted in the departments of information science, sociology, and economies. There’s a multi-disciplinary application to the many problems and concepts that we go over. The very first concept that we went over was graphs. In my opinion, it’s one of the most versatile ways to represent data ranging from modeling populations to nuances in classical music and everything in between. This blog post focuses on a research article that uses social network-based distancing strategies to counter the COVID-19 pandemic. I believe it’s quite relevant as a majority of Americans work from home or attend remote learning classes at their respective institutions. It’s been over half a year since this disease has impacted the world. Socially, psychologically, and economically it has caused waves throughout every age group and every part of society. We haven’t seen a pandemic this serious in over a century and it’s imperative for us to be safe. In a world where technology dominates our everyday lives, it’s interesting to see how scientists do research to limit interactions and modeling that using graph theory.
The graph below is a visual representation of two example networks that have the same number of individuals and social interactions. The key differences are the paths taken. The bold lines are the shortest infection path from the infection source to the last infected individual. We hear the phrase flatten the curve very often but having the graph helps understand how important it is to social distance. Similar to the models being done to contain COVID-19 spread at Cornell by professor Peter Frazier, these models try to take into account real world contact networks and model different strategies over time. The researchers propose three different strategies. The first is seek similarity. Individuals choose their contact partners based on similarity of a predetermined individual characteristic such as living in the same neighborhood or classmates. The second is strengthen communities. Individuals consider with whom their contact partners usually interact with and as such an individual should limit interaction with those they aren’t mutually connected with. The third and final is build bubbles through repeated contact. Individuals decide whom they usually interact with and slowly restrict those interactions. The models and graphs that are present in the research paper help explore and visualize these three strategies. The walk away with each one slowing the spread of the virus as compared to no intervention or non-strategic social distancing. From this paper, I understand the importance of having several different test cases in the research that one would do in order to narrow down the most effective ones. In addition, it’s good to see that the concepts that we learn in class are constantly being used in the real world to solve very pressing issues.
