| covid_patients {stagedtrees} | R Documentation |
Trajectories of hospitalized SARS-CoV-2 patients
Description
Dataset with observations from four variables (Sex, Age, ICU, death) for 10000 simulated SARS-CoV-2 hospital patients.
Usage
covid_patients
Format
A data frame with 10000 observations of 4 variables. The variables and their levels are as follows:
Sex: Female, Male
Age: 0-39, 40-49, 50-59, 60-69, 70-79, 80+
ICU: yes, no
death: yes, no
Details
The data are simulated from an event tree where conditional probabilities for ICU and death are taken from the results of Lefrancq et al. (2021). Lefrancq et al. (2021) estimated such probabilities from data on patients, recorded in the SI-VIC database, who started their hospitalization between 13 March and 30 November 2020.
Source
The data has been generated with the code in the Examples section. Conditional probabilities were copied from the tables in the Supplementary materials of Lefrancq et al. (2021). Marginal probabilities of gender and probabilities of age given gender were instead obtained from the linked GitHub repository https://github.com/noemielefrancq/Evolution-Outcomes-COVID19-France.
References
Leonelli, M. and Varando, G. (2023). Context-Specific Causal Discovery for Categorical Data Using Staged Trees. Proceedings of The 26th International Conference on Artificial Intelligence and Statistics, in Proceedings of Machine Learning Research 206:8871-8888 Available from https://proceedings.mlr.press/v206/leonelli23a.html.
Lefrancq N., Paireau J., Hozé N., Courtejoie N., Yazdanpanah Y., Bouadma L. (2021). Evolution of outcomes for patients hospitalised during the first 9 months of the SARS-CoV-2 pandemic in France: A retrospective national surveillance data analysis. The Lancet Regional Health - Europe, 5:100087.
Examples
library(stagedtrees)
data_model <- sevt(list(
Sex = c("Female", "Male"),
Age = c(
"0-39", "40-49", "50-59", "60-69",
"70-79", "80+"
),
ICU = c("yes", "no"),
death = c("yes", "no")
), full = TRUE)
data_model$prob <- list()
data_model$prob$Sex <- list("1" = c(Female = 0.45185, Male = 0.54815))
dist_age_male <- c(
0.01616346, # 0 - 39
0.04159445, # 40 - 49
0.10130439, # 50 - 59
0.16825686, # 60 - 69
0.25217550, # 70 - 79
0.42050534
) # 80+
dist_age_female <- c(
0.01688613, # 0 - 39
0.04271329, # 40 - 49
0.10131681, # 50 - 59
0.16841872, # 60 - 69
0.25289366, # 70 - 79
0.41777138
) # 80+
names(dist_age_male) <- data_model$tree$Age
names(dist_age_female) <- data_model$tree$Age
data_model$prob$Age <- list(
"1" = dist_age_female,
"2" = dist_age_male
)
data_model$prob$ICU <- list(
"1" = c(yes = 0.125, no = 1 - 0.125), # Female 0-39
"2" = c(yes = 0.149, no = 1 - 0.149), # Female 40-49
"3" = c(yes = 0.193, no = 1 - 0.193), # Female 50-59
"4" = c(yes = 0.225, no = 1 - 0.225), # Female 60-69
"5" = c(yes = 0.175, no = 1 - 0.175), # Female 70-79
"6" = c(yes = 0.037, no = 1 - 0.037), # Female 80+
"7" = c(yes = 0.197, no = 1 - 0.197), # Male 0-39
"8" = c(yes = 0.2687, no = 1 - 0.2687), # Male 40-49
"9" = c(yes = 0.3171, no = 1 - 0.3171), # Male 50-59
"10" = c(yes = 0.3415, no = 1 - 0.3415), # Male 60-69
"11" = c(yes = 0.274, no = 1 - 0.274), # Male 70-79
"12" = c(yes = 0.073, no = 1 - 0.073) # Male 80+
)
data_model$prob$death <- list(
################### FEMALE ################################
"1" = c(yes = 0.077, no = 1 - 0.077), # Female 0-39 ICU
"2" = c(yes = 0.004, no = 1 - 0.004), # Female 0-39 no-ICU
"3" = c(yes = 0.117, no = 1 - 0.117), # Female 40-49 ICU
"4" = c(yes = 0.017, no = 1 - 0.017), # Female 40-49 no-ICU
"5" = c(yes = 0.185, no = 1 - 0.185), # Female 50-59 ICU
"6" = c(yes = 0.030, no = 1 - 0.030), # Female 50-59 no-ICU
"7" = c(yes = 0.239, no = 1 - 0.239), # Female 60-69 ICU
"8" = c(yes = 0.058, no = 1 - 0.058), # Female 60-69 no-ICU
"9" = c(yes = 0.324, no = 1 - 0.324), # Female 70-79 ICU
"10" = c(yes = 0.124, no = 1 - 0.124), # Female 70-79 no-ICU
"11" = c(yes = 0.454, no = 1 - 0.454), # Female 80+ ICU
"12" = c(yes = 0.266, no = 1 - 0.266), # Female 80+ no-ICU
################# MALE ##################################
"13" = c(yes = 0.079, no = 1 - 0.079), # Male 0-39 ICU
"14" = c(yes = 0.008, no = 1 - 0.008), # Male 0-39 no-ICU
"15" = c(yes = 0.098, no = 1 - 0.098), # Male 40-49 ICU
"16" = c(yes = 0.016, no = 1 - 0.016), # Male 40-49 no-ICU
"17" = c(yes = 0.171, no = 1 - 0.171), # Male 50-59 ICU
"18" = c(yes = 0.030, no = 1 - 0.030), # Male 50-59 no-ICU
"19" = c(yes = 0.278, no = 1 - 0.278), # Male 60-69 ICU
"20" = c(yes = 0.067, no = 1 - 0.067), # Male 60-69 no-ICU
"21" = c(yes = 0.383, no = 1 - 0.383), # Male 70-79 ICU
"22" = c(yes = 0.150, no = 1 - 0.150), # Male 70-79 no-ICU
"23" = c(yes = 0.478, no = 1 - 0.478), # Male 80+ ICU
"24" = c(yes = 0.363, no = 1 - 0.363) # Male 80+ no-ICU
)
# covid_patients <- sample_from(data_model, 10000, seed = 123)
# usethis::use_data(covid_patients, overwrite = TRUE)