covidtech: covidtech Bayesian Network
| covidtech | R Documentation |
covidtech Bayesian Network
Description
The YODO algorithm: An eļ¬cient computational framework for sensitivity analysis in Bayesian networks.
Format
A discrete Bayesian network to model the relationship between the use of technology and the psychological effects of forced social isolation during the COVID-19 pandemic. The Bayesian network is learned as in the referenced paper. The vertices are:
- AGE
Age of respondent (<25, >=25);
- GENDER
Gender of respondent (Male, Female);
- BELONGINGNESS
How often the word we is used (Low, Medium, High);
- ANG_IRR
Perceived level of anger/irritability (Low, Medium, High);
- SOCIAL
Perceived social support (Low, Medium, High);
- ANXIETY
Level of anxiety (Low, Medium, High);
- BOREDOM
Level of boredom (Low, Medium, High);
- LONELINESS
Perceived loneliness (Low, Medium, High);
- TECH_FUN_Q
Use of communication technology for fun in quarantine (Low, Medium, High);
- TECH_FUN_PQ
Use of communication technology for fun pre-quarantine (Low, Medium, High);
- TECH_WORK_Q
Use of communication technology for work in quarantine (Low, High);
- TECH_WORK_PQ
Use of communication technology for work pre-quarantine (Low, High);
- OUTSIDE
Times outside per week (0, 1, >=2);
- SQUARE_METERS
Home square meters (<80, >=80);
- FAMILY_SIZE
Number of individuals at home (1, 2, >=3);
- DAYS_ISOLATION
Days since lockdown (0-10, 11-20, >20);
- REGION
Region of residence (Lombardy, Other);
- OCCUPATION
Occupation (Other, Smartworking, Student, Office work);
Value
An object of class bn.fit. Refer to the documentation of bnlearn for details.
References
Ballester-Ripoll, R., & Leonelli, M. (2023). The YODO algorithm: An efficient computational framework for sensitivity analysis in Bayesian networks. International Journal of Approximate Reasoning, 159, 108929.
