There is a bug on the use of parameter K. When the last week is incomplete and K = 0 the last week to correct delay is the last complete week. But when K>0, it should forecast the current (and incomplete) week and the following K-1 weeks, but it seems to forecast K weeks beyond the current. As as example, when K=1, it should forecast the current (and incomplete) week but it forecasts two weeks ahead, the current one + 1.
data(sragBH)
sragBH20 <- sragBH %>%
filter(
# Excluding typing date before symptoms onset
DT_SIN_PRI <= DT_DIGITA,
# Excluding no typing date
!is.na(DT_DIGITA),
# cases reported in 2020
year(DT_DIGITA) == 2020
)
max(sragBH20$DT_DIGITA)
weekdays(max(sragBH20$DT_DIGITA))
epiweek(max(sragBH20$DT_DIGITA))
# Incomplete week (recording ends on Thursday)
# Nowcasting ignores an incomplete week (OK)
nowk0 = nowcasting_inla(dataset = sragBH20,
date_onset = DT_SIN_PRI,
date_report = DT_DIGITA,
silent = T)
nowk0$total %>%
mutate(
epiweek = epiweek(dt_event)
) %>% tail()
# Week to forecast: week 53 only, but it forecast weeks 53/20 and 1/21
nowk1 = nowcasting_inla(dataset = sragBH20,
date_onset = DT_SIN_PRI,
date_report = DT_DIGITA,
K = 1,
silent = T)
nowk1$total %>%
mutate(
epiweek = epiweek(dt_event)
) %>% tail()
# Weeks to forecast: week 53 and 1, but it forecast weeks 53/20, 1 and 2/21
nowk2 = nowcasting_inla(dataset = sragBH20,
date_onset = DT_SIN_PRI,
date_report = DT_DIGITA,
K = 2,
silent = T)
nowk2$total %>%
mutate(
epiweek = epiweek(dt_event)
) %>% tail()
There is a bug on the use of parameter K. When the last week is incomplete and K = 0 the last week to correct delay is the last complete week. But when K>0, it should forecast the current (and incomplete) week and the following K-1 weeks, but it seems to forecast K weeks beyond the current. As as example, when K=1, it should forecast the current (and incomplete) week but it forecasts two weeks ahead, the current one + 1.