Description Usage Arguments Value Examples
This function is used to predict the total reported as well as unreported case counts, total recovered, and total deaths.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 
data 
(mandatory): input the training data set. If the model is Multinomial then the data matrix should contain the 3 columns Confirmed, Recovered, and Death. If the model is Poisson or Binomial, then the data should contain only the column Confirmed. Note that the names of the columns must be the same as stated above. 
data_init 
(mandatory): These are the initial data values provided by the user as a numeric vector of length six. The entries should be the Total Confirmed, Total Recovered, Total Death, Daily Confirmed, Daily Recovered, and Daily Death for the Starting Date. Note: If the starting total confirmed is 0, please replace it by 1. 
init_pars 
NULL (default): If not equal to NULL, then the user can give a user input initial parameters which should consist of the initial values of the time varying beta, proportion of testing for the different periods. 
N 
(mandatory): The population size. 
plot 
TRUE (default): If estimate = FALSE, this will give two plots. One is the panel plot for total cases, total recovered, total death, and total confirmed if the model is Multinomial. Otherwise it will give only a plot for total confirmed when the model is binomial or Poisson, and the second plot is the plot of untested, false negative, and reported cases. And when estimate = TRUE, it will give two other plots along with the previous two plots. One is the box plot for basic reproduction number and the other one is the trace plot for the convergence of the MCMC parameters. 
T_predict 
It is the number of days that we want to predict after the train
period. The value of T_predict should be greater than or equal to the number
of rows of 
period_start 
The total time period is divided into small periods depending on the lock down measures imposed by the government. So this is a numeric vector consisting of the start dates for the different time periods. 
estimate 
TRUE (default): If it is TRUE then it will run the MCMC algorithm to estimate the parameters. If it is FALSE, then the user needs to give input the parameter values in the pars argument. 
pars 
= NULL (default): If estimate = FALSE, then the user needs to input the parameter estimates. 
data_test 
NULL (default): Otherwise need to give the test data for comparing with the model estimates. 
auto.initialize 
TRUE (default): This is the option for using a mle based initial parameter. 
... 
arguments passed to the function SEIRfansy, model_initializeR and model_plotR which are used for initializing the parameters. The parameters are described below:

A list with class "SEIRfansyPredict", which contains the items described below:
mcmc_pars: a matrix of the mcmc draws for the parameters
plots: a list of ggplot objects
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39  library(dplyr)
train = covid19[which(covid19$Date == "01 April "):which(covid19$Date == "30 June "),]
test = covid19[which(covid19$Date == "01 July "):which(covid19$Date == "31 July "),]
train_multinom =
train %>%
rename(Confirmed = Daily.Confirmed,
Recovered = Daily.Recovered,
Deceased = Daily.Deceased) %>%
dplyr::select(Confirmed, Recovered, Deceased)
test_multinom =
test %>%
rename(Confirmed = Daily.Confirmed,
Recovered = Daily.Recovered,
Deceased = Daily.Deceased) %>%
dplyr::select(Confirmed, Recovered, Deceased)
N = 1341e6 # population size of India
data_initial = c(2059, 169, 58, 424, 9, 11)
pars_start = c(c(1,0.8,0.6,0.4,0.2), c(0.2,0.2,0.2,0.25,0.2))
phases = c(1,15,34,48,62)
cov19pred = SEIRfansy.predict(data = train_multinom, init_pars = pars_start,
data_init = data_initial, T_predict = 60, niter = 1e3,
BurnIn = 1e2, data_test = test_multinom, model = "Multinomial",
N = N, lambda = 1/(69.416 * 365), mu = 1/(69.416 * 365),
period_start = phases, opt_num = 1,
auto.initialize = TRUE, f = 0.15)
names(cov19pred)
class(cov19pred$prediction)
class(cov19pred$mcmc_pars)
names(cov19pred$plots)
plot(cov19pred, type = "trace")
plot(cov19pred, type = "boxplot")
plot(cov19pred, type = "panel")
plot(cov19pred, type = "cases")

Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.