R/covid3.R

In bnRep: A Repository of Bayesian Networks from the Academic Literature

#' covid Bayesian Networks
#'
#'
#' Uncovering hidden and complex relations of pandemic dynamics using an AI driven system.
#' @usage NULL
#'
#' @format
#' A discrete Bayesian network to classify the severity of covid-19 given different symptoms (Generic BN). The probabilities were available from a repository. The vertices are:
#' \describe{
#' \item{CovidSeverity}{(1. 1, 2. 2, 3. 3, 4. 4, 5. 5, 6. 6);}
#' \item{Cough}{(1. 0, 2. 1);}
#' \item{Diarrhea}{(1. 0, 2. 1);}
#' \item{Fatigue}{(1. 0, 2. 1);}
#' \item{Fever}{(1. 0, 2. 1);}
#' \item{Headache}{(1. 0, 2. 1);}
#' \item{LossOfSmell}{(1. 0, 2. 1);}
#' \item{LossOfTaste}{(1. 0, 2. 1);}
#' \item{MuscleSore}{(1. 0, 2. 1);}
#' \item{RunnyNose}{(1. 0, 2. 1);}
#' \item{Sob}{(1. 0, 2. 1);}
#' \item{SoreThroat}{(1. 0, 2. 1);}
#'  }
#'
#' @return An object of class \code{bn.fit}. Refer to the documentation of \code{bnlearn} for details.
#' @keywords NULL
#' @importClassesFrom bnlearn bn.fit
#' @references Demirbaga, U., Kaur, N., & Aujla, G. S. (2024). Uncovering hidden and complex relations of pandemic dynamics using an AI driven system. Scientific Reports, 14(1), 15433.
"covid3"