joshwlambert
commented
2 months ago Note This response uses the time-varying-cfr branch on {simulist} which will soon be merged into main.
To increase the number of individuals that get hospitalised then increase the hosp_risk (the largest value is 1 which is every infection goes to hospital) and if you want the fatality risk for hospitalised individuals to increase, increase the hosp_death_risk (1 is again the largest value). Here is an example where both of those risks are high and shows that, even with stochasticity, these risks should be represented in the line list data.
library(simulist)
# load data required to simulate line list
contact_distribution <- epiparameter::epidist(
disease = "COVID-19",
epi_dist = "contact distribution",
prob_distribution = "pois",
prob_distribution_params = c(mean = 2)
)
#> Citation cannot be created as author, year, journal or title is missing
infect_period <- epiparameter::epidist(
disease = "COVID-19",
epi_dist = "infectious period",
prob_distribution = "gamma",
prob_distribution_params = c(shape = 1, scale = 1)
)
#> Citation cannot be created as author, year, journal or title is missing
# get onset to hospital admission from {epiparameter} database
onset_to_hosp <- epiparameter::epidist_db(
disease = "COVID-19",
epi_dist = "onset to hospitalisation",
single_epidist = TRUE
)
#> Using Linton N, Kobayashi T, Yang Y, Hayashi K, Akhmetzhanov A, Jung S, Yuan
#> B, Kinoshita R, Nishiura H (2020). "Incubation Period and Other
#> Epidemiological Characteristics of 2019 Novel Coronavirus Infections
#> with Right Truncation: A Statistical Analysis of Publicly Available
#> Case Data." _Journal of Clinical Medicine_. doi:10.3390/jcm9020538
#> <https://doi.org/10.3390/jcm9020538>..
#> To retrieve the short citation use the 'get_citation' function
# get onset to death from {epiparameter} database
onset_to_death <- epiparameter::epidist_db(
disease = "COVID-19",
epi_dist = "onset to death",
single_epidist = TRUE
)
#> Using Linton N, Kobayashi T, Yang Y, Hayashi K, Akhmetzhanov A, Jung S, Yuan
#> B, Kinoshita R, Nishiura H (2020). "Incubation Period and Other
#> Epidemiological Characteristics of 2019 Novel Coronavirus Infections
#> with Right Truncation: A Statistical Analysis of Publicly Available
#> Case Data." _Journal of Clinical Medicine_. doi:10.3390/jcm9020538
#> <https://doi.org/10.3390/jcm9020538>..
#> To retrieve the short citation use the 'get_citation' function
set.seed(12345)
# example with single hospitalisation risk for entire population
linelist <- sim_linelist(
contact_distribution = contact_distribution,
infect_period = infect_period,
prob_infect = 0.5,
onset_to_hosp = onset_to_hosp,
onset_to_death = onset_to_death,
hosp_risk = 0.9,
hosp_death_risk = 0.9
)
head(linelist)
#> id case_name case_type sex age date_onset date_admission outcome
#> 1 1 Christopher Brooks probable m 55 2023-01-01 2023-01-01 died
#> 2 2 Chantell Tarar suspected f 56 2023-01-03 2023-01-03 died
#> 3 3 Rifaah el-Mirza suspected m 64 2023-01-01 <NA> recovered
#> 4 4 Charlie Weems confirmed m 69 2023-01-04 2023-01-10 died
#> 5 5 Autumn Castaneda confirmed f 42 2023-01-04 <NA> recovered
#> 6 8 Bakar el-Koroma suspected m 42 2023-01-01 2023-01-09 died
#> date_outcome date_first_contact date_last_contact ct_value
#> 1 2023-01-08 <NA> <NA> NA
#> 2 2023-01-16 2023-01-01 2023-01-04 NA
#> 3 <NA> 2022-12-27 2023-01-02 NA
#> 4 2023-01-13 2023-01-04 2023-01-06 24.5
#> 5 <NA> 2023-01-06 2023-01-09 24.5
#> 6 2023-01-09 2023-01-01 2023-01-04 NA
nrow(linelist)
#> [1] 4481
sum(!is.na(linelist$date_admission)) / nrow(linelist)
#> [1] 0.9000223
sum(linelist$outcome == "died") / nrow(linelist)
#> [1] 0.8136577Created on 2024-04-17 with reprex v2.1.0
The reason the proportion of infections that go on to die is lower than the hosp_death_risk is because the non_hosp_death_risk (the case fatality risk for individuals that are not hospitalised is left as its default value (0.05)).
Please let me know if this is clear and answers your question, and if you would like this to be better documented in the package please say.
avallecam
No rush for this one. I'll use
outbreaks::mers_korea_2015for my burning need.But tried a quick one to get a decent dataset suitable to estimate CFR. I tried combinations of
hosp_riskandhosp_death_riskbut got mostly none. For this, would it be better to simulate like 1000 datasets to get an extreme scenario? Again, it's not urgent, but I'm curious.Created on 2024-04-17 with reprex v2.1.0