In kimfinale/pfilterCOVID:

Selection of stochastic time series

# Rt data set; Rt_dataset_I0=100_20220105.rds, Rt_dataset_I0=10_20220105.rds
tstamp <- "20220105"
dat <- readRDS(paste0("outputs/Rt_dataset_I0=100_", tstamp, ".rds"))

library(ggplot2)
library(tidyverse)
oderes <- dat$ode[, c("time", "daily_infected", "daily_confirmed")]
oderes %>% pivot_longer(cols = -time) -> oderes

stochres <- dat$stoch_perfect_obs$daily_confirmed[, 1:20]
stochres$time <- dat$time

stochres %>% pivot_longer(cols = -time) -> stochres

plt <- ggplot() +
  geom_line(data = stochres, aes(time, value, group=name), color="grey50") +
  geom_line(data = oderes, aes(time, value, color=name), size=1.2) + 
  labs(x="time", y="", color="")

plt   
# ggsave("plots/daily_case.png", plt)

plot(dat$ode$time, dat$ode$daily_confirmed, type = "l", ylim = c(0, 1000))
stochid_I0_100 <- c(2, 4, 19)
stochid_I0_10 <- c(5, 12, 17)
for (i in stochid_I0_100) {
  cat("i = ", i, "\n")
  lines(0:200, dat$stoch_perfect_obs$daily_confirmed[,i])
  # text(1:200, dat$stoch_perfect_obs$daily_infected[,1], label = i)
  readline(prompt="Press [enter] to continue")
}
# 2, 4, 19 selected to represent low, medium, and high incidence for I0=100
# 5, 12, 17 selected to represent low, medium, and high incidence for I0=10

Selection of imperfect observation of confirmed cases

# Rt data set; Rt_dataset_I0=100_20220105.rds, Rt_dataset_I0=10_20220105.rds
# 2, 4, 19 selected to represent low, medium, and high incidence for I0=100
# 5, 12, 17 selected to represent low, medium, and high incidence for I0=10

tstamp <- "20220105"
dat <- readRDS(paste0("outputs/Rt_dataset_I0=100_", tstamp, ".rds"))
stochid_I0_100 <- c(2, 4, 19)
stochid_I0_10 <- c(5, 12, 17)
probid <- c(2, 4, 6) # probability for 0.3, 0.5, 0.7

plot(dat$ode$time, dat$ode$daily_confirmed, type = "l", ylim = c(0, 1000))

for (i in stochid_I0_100) {
  # for (j in probid) {
  for (j in c(4)) {
    cat("i = ", i, "j = ", j, "\n")
    lines(0:200, dat$stoch_partial_obs$daily_detected[[j]][,i])
    readline(prompt="Press [enter] to continue")
  }
}

library(ggplot2)
library(tidyverse)
oderes <- dat$ode[, c("time", "daily_infected", "daily_confirmed")]
oderes %>% pivot_longer(cols = -time) -> oderes

stochres <- dat$stoch_perfect_obs$daily_confirmed[, 1:20]
stochres$time <- dat$time

stochres %>% pivot_longer(cols = -time) -> stochres

plt <- ggplot() +
  geom_line(data = stochres, aes(time, value, group=name), color="grey50") +
  geom_line(data = oderes, aes(time, value, color=name), size=1.2) + 
  labs(x="time", y="", color="")

plt   
# ggsave("plots/daily_case.png", plt)

plot(dat$ode$time, dat$ode$daily_confirmed, type = "l", ylim = c(0, 1000))
stochid_I0_100 <- c(2, 4, 19)
stochid_I0_10 <- c(5, 12, 17)
for (i in stochid_I0_100) {
  cat("i = ", i, "\n")
  lines(0:200, dat$stoch_perfect_obs$daily_confirmed[,i])
  # text(1:200, dat$stoch_perfect_obs$daily_infected[,1], label = i)
  readline(prompt="Press [enter] to continue")
}

kimfinale/pfilterCOVID documentation built on July 30, 2023, 12:15 a.m.