corical {bnRep} | R Documentation |
corical Bayesian Network
Description
Risk-benefit analysis of the AstraZeneca COVID-19 vaccine in Australia using a Bayesian network modelling framework.
Format
A discrete Bayesian network to perform risk-benefit analysis of vaccination. The probabilities were given in the referenced paper. The vertices are:
- Age
(0-9, 10-19, 20-29, 30-39, 40-49, 50-59, 60-69, 70+);
- AZVaccineDoses
(One, Two, Three, Four);
- BackgroundCSVTOver6Weeks
(Yes, No);
- BackgroundPVTOver6Weeks
(Yes, No);
- Covid19AssociatedCSVT
(Yes, No);
- Covid19AssociatedPVT
(Yes, No);
- DieFromBackgroundCSVT
(Yes, No);
- DieFromBackgroundPVT
(Yes, No);
- DieFromCovid19
(Yes, No);
- DieFromCovid19AssociatedCSVT
(Yes, No);
- DieFromCovid19AssociatedPVT
(Yes, No);
- DieFromVaccineAssociatedTTS
(Yes, No);
- IntensityOfCommunityTransmission
(None, ATAGI Low, ATAGI Med, ATAGI High, One Percent, Two Percent, NSW 200 Daily, NSW 1000 Daily, VIC 1000 Daily, QLD 1000 Daily);
- RiskOfSymptomaticInfection
(Yes, No);
- RiskOfSymptomaticInfectionUnderCurrentTransmissionAndVaccinationStatus
(Yes, No);
- SARSCoV2Variant
(Alpha Wild, Delta);
- Sex
(Male, Female);
- VaccineAssociatedTTS
(Yes, No);
- VaccineEffectivenessAgainstDeathIfInfected
(Effective, Not Effective);
- VaccineEffectivenessAgainstSymptomaticInfection
(Effective, Not Effective);
Value
An object of class bn.fit
. Refer to the documentation of bnlearn
for details.
References
Lau, C. L., Mayfield, H. J., Sinclair, J. E., Brown, S. J., Waller, M., Enjeti, A. K., ... & Litt, J. (2021). Risk-benefit analysis of the AstraZeneca COVID-19 vaccine in Australia using a Bayesian network modelling framework. Vaccine, 39(51), 7429-7440.