Keywords
COVID-19, social bubble, infection, bayes, Bayesian, event tree, probability tree, classroom
Abstract
The COVID-19 pandemic has led many people to form social bubbles. These social bubbles are small groups of people who interact with one another but restrict interactions with the outside world. The assumption in forming social bubbles is that risk of infection and severe outcomes, like hospitalization, are reduced. How effective are social bubbles? A Bayesian event tree is developed to calculate the probabilities of specific outcomes, like hospitalization, using example rates of infection in the greater community and example prior functions describing the effectiveness of isolation by members of the social bubble. The probabilities are solved for two contrasting examples: members of an assisted living facility and members of a classroom, including their teacher. A web-based calculator is provided so readers can experiment with the Bayesian event tree and learn more about these probabilities by modeling their own social bubble.
DOI
https://doi.org/10.5038/1936-4660.14.1.1382
Recommended Citation
Connor, Charles. "Computing for Numeracy: How Safe is Your COVID-19 Social Bubble?." Numeracy 14, Iss. 1 (2021): Article 12. DOI: https://doi.org/10.5038/1936-4660.14.1.1382
Creative Commons License
This work is licensed under a Creative Commons Attribution-Noncommercial 4.0 License
Included in
Data Science Commons, Immunology and Infectious Disease Commons, Statistics and Probability Commons
