Open AccessComputing for Numeracy: How Safe is Your COVID-19 Social Bubble?
Author(s) -
Charles B. Connor
Publication year - 2021
Publication title -
numeracy
Language(s) - English
Resource type - Journals
ISSN - 1936-4660
DOI - 10.5038/1936-4660.14.1.1382
Subject(s) - numeracy , bayesian probability , event (particle physics) , calculator , pandemic , social isolation , covid-19 , bubble , computer science , psychology , social psychology , medicine , artificial intelligence , pedagogy , literacy , physics , disease , quantum mechanics , pathology , parallel computing , infectious disease (medical specialty) , psychotherapist , operating system
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.