Bihar.R

onset <- as.Date(c("2020-03-22", "2020-03-22",
"2020-03-24", "2020-03-26", "2020-03-26", "2020-03-26",
"2020-03-27", "2020-03-27", "2020-03-27",
"2020-03-29", "2020-03-29", 
"2020-03-30", "2020-03-30", "2020-03-30", "2020-03-30", 
"2020-04-01", "2020-04-01", "2020-04-01", "2020-04-01", "2020-04-01", "2020-04-01", "2020-04-01", "2020-04-01", 
"2020-04-02", 
"2020-04-03", "2020-04-03", "2020-04-03", "2020-04-03", "2020-04-03", 
"2020-04-04", "2020-04-06",  "2020-04-06", 
"2020-04-08", "2020-04-08", "2020-04-08", "2020-04-08", "2020-04-08", "2020-04-08", 
"2020-04-09", 
"2020-04-10", "2020-04-10", "2020-04-10", "2020-04-10", "2020-04-10", "2020-04-10", "2020-04-10", "2020-04-10", "2020-04-10", "2020-04-10", "2020-04-10", "2020-04-10", "2020-04-10", "2020-04-10", "2020-04-10", "2020-04-10", "2020-04-10", "2020-04-10", "2020-04-10", "2020-04-10", "2020-04-10", 
"2020-04-12", "2020-04-12", "2020-04-12", "2020-04-12", 
"2020-04-14", "2020-04-14",
"2020-04-15", "2020-04-15", "2020-04-15", "2020-04-15", 
"2020-04-16", "2020-04-16", "2020-04-16", "2020-04-16"))
 
library(incidence)
i1 <- incidence(onset)
i1
plot(i1, border = "white")
mu <- 4.7 # mean in days days
sigma <- 2.9 # standard deviation in days
library(earlyR)

res <- get_R(i1, si_mean = mu, si_sd = sigma)
res
plot(res)
plot(res, "lambdas", scale = length(onset) + 1)
abline(v = onset, lwd = 3, col = "grey")
abline(v = today, col = "blue", lty = 2, lwd = 2)
points(onset, seq_along(onset), pch = 20, cex = 3)
R_val <- sample_R(res, 1000)
summary(R_val)
quantile(R_val)
quantile(R_val, c(0.025, 0.975))
hist(R_val, border = "grey", col = "navy",
     xlab = "Values of R",
     main = "Sample of likely R values")
si <- res$si
si
library(projections)

future_i <- project(i1, R = R_val, n_sim = 1000, si = res$si, n_days = 30)
future_i
mean(future_i)
plot(future_i)
predicted_n <- colSums(future_i)
summary(predicted_n)