eSAIR {eSIR} | R Documentation |
Extended state-space SIR with a subset of the population showing antibody positivity
Description
In this function we allow it to characterize time-varying immunization among a subset of the population that have been tested positive in an antibody assessment. We expanded the SIR model by adding a time-varying antibody-positive proportion \alpha_t
.
Usage
eSAIR(
Y,
R,
alpha0 = NULL,
change_time = NULL,
begin_str = "01/13/2020",
T_fin = 200,
nchain = 4,
nadapt = 10000,
M = 500,
thn = 10,
nburnin = 200,
dic = FALSE,
death_in_R = 0.02,
casename = "eSAIR",
beta0 = 0.2586,
gamma0 = 0.0821,
R0 = beta0/gamma0,
gamma0_sd = 0.1,
R0_sd = 1,
file_add = character(0),
add_death = FALSE,
save_files = FALSE,
save_mcmc = FALSE,
save_plot_data = FALSE,
eps = 1e-10
)
Arguments
Y |
the time series of daily observed infected compartment proportions. |
R |
the time series of daily observed removed compartment proportions, including death and recovered. |
alpha0 |
a vector of values of the dirac delta function |
change_time |
the change points over time corresponding to |
begin_str |
the character of starting time, the default is "01/13/2020". |
T_fin |
the end of follow-up time after the beginning date |
nchain |
the number of MCMC chains generated by |
nadapt |
the iteration number of adaptation in the MCMC. We recommend using at least the default value 1e4 to obtained fully adapted chains. |
M |
the number of draws in each chain, with no thinning. The default is M=5e2 but suggest using 5e5. |
thn |
the thinning interval between mixing. The total number of draws thus would become |
nburnin |
the burn-in period. The default is 2e2 but suggest 2e5. |
dic |
logical, whether compute the DIC (deviance information criterion) for model selection. |
death_in_R |
the numeric value of average of cumulative deaths in the removed compartments. The default is 0.4 within Hubei and 0.02 outside Hubei. |
casename |
the string of the job's name. The default is "eSAIR". |
beta0 |
the hyperparameter of average transmission rate, the default is the one estimated from the SARS first-month outbreak (0.2586). |
gamma0 |
the hyperparameter of average removed rate, the default is the one estimated from the SARS first-month outbreak (0.0821). |
R0 |
the hyperparameter of the mean reproduction number R0. The default is thus the ratio of |
gamma0_sd |
the standard deviation for the prior distrbution of the removed rate |
R0_sd |
the standard deviation for the prior disbution of R0, the default is 1. |
file_add |
the string to denote the location of saving output files and tables. |
add_death |
logical, whether add the approximate death curve to the plot, default is false. |
save_files |
logical, whether to save plots to file. |
save_mcmc |
logical, whether save ( |
save_plot_data |
logical, whether save the plotting data or not. |
eps |
a non-zero controller so that all the input |
Value
casename |
the predefined |
incidence_mean |
mean cumulative incidence, the mean prevalence of cumulative confirmed cases at the end of the study. |
incidence_ci |
2.5%, 50%, and 97.5% quantiles of the incidences. |
out_table |
summary tables including the posterior mean of the prevalance processes of the 3 states compartments ( |
plot_infection |
plot of summarizing and forecasting for the infection compartment, in which the vertial blue line denotes the last date of data collected ( |
plot_removed |
plot of summarizing and forecasting for the removed compartment with lines similar to those in the |
spaghetti_plot |
20 randomly selected MCMC draws of the first-order derivative of the posterior prevalence of infection, namely |
first_tp_mean |
the date t at which |
first_tp_mean |
the date t at which |
first_tp_ci |
fwith |
second_tp_mean |
the date t at which |
second_tp_ci |
with |
dic_val |
the output of |
gelman_diag_list |
Since version 0.3.3, we incorporated Gelman And Rubin's Convergence Diagnostic using |
Examples
NI_complete <- c(
41, 41, 41, 45, 62, 131, 200, 270, 375, 444, 549, 729,
1052, 1423, 2714, 3554, 4903, 5806, 7153, 9074, 11177,
13522, 16678, 19665, 22112, 24953, 27100, 29631, 31728, 33366
)
RI_complete <- c(
1, 1, 7, 10, 14, 20, 25, 31, 34, 45, 55, 71, 94,
121, 152, 213, 252, 345, 417, 561, 650, 811, 1017,
1261, 1485, 1917, 2260, 2725, 3284, 3754
)
N <- 58.5e6
R <- RI_complete / N
Y <- NI_complete / N - R # Jan13->Feb 11
change_time <- c("02/08/2020")
alpha0 <- c(0.2) # 20% of the susceptible population were found immunized
res.antibody <- eSAIR(Y, R,
begin_str = "01/13/2020", death_in_R = 0.4,
alpha0 = alpha0, change_time = change_time,
casename = "Hubei_antibody", save_files = TRUE, save_mcmc = FALSE,
M = 5e2, nburnin = 2e2
)
res.antibody$plot_infection
change_time <- c("01/16/2020")
alpha0 <- c(0.2)
NI_complete2 <- c(41, 45)
RI_complete2 <- c(1, 1)
N2 <- 1E3
res3 <- eSAIR(
RI_complete2 / N2,
NI_complete2 / N2,
begin_str = "01/13/2020",
T_fin = 4,
alpha0 = alpha0,
change_time = change_time,
dic = FALSE,
casename = "Hubei_q",
save_files = FALSE,
save_mcmc = FALSE,
save_plot_data = FALSE,
M = 50,
nburnin = 1
)
closeAllConnections()