Daedalus features in development

library(daedalus)
library(ggplot2)
library(dplyr)
#> 
#> Attaching package: 'dplyr'
#> The following objects are masked from 'package:stats':
#> 
#>     filter, lag
#> The following objects are masked from 'package:base':
#> 
#>     intersect, setdiff, setequal, union

This vignette is intended as a visual check on daedalus features in development.

Basic daedalus2() usage

This example shows (checks) that daedalus2() output works with pre-built function get_incidence() written for daedalus().

output <- daedalus2("GBR", "influenza_2009")

# get incidence data using function developed for daedalus()
data <- get_incidence(output, "infections")

ggplot(data) +
  geom_line(
    aes(time, value)
  ) +
  labs(x = "Time", y = "New infections")

Vaccination

This section aims to be a visual check on vaccination in daedalus2() as this function does not currently log new vaccinations.

# high vaccination rate but low uptake limit
vax <- daedalus_vaccination("high", uptake_limit = 40, start_time = 30)
output <- daedalus2("GBR", "influenza_2009", vaccine_investment = vax)

data <- get_data(output) %>%
  filter(vaccine_group == "vaccinated", compartment == "susceptible") %>%
  group_by(time) %>%
  summarise(
    value = sum(value)
  )

# check that vaccinations begin at time = 100
ggplot(data, aes(time, value)) +
  geom_line() +
  labs(
    x = "Time", y = "Vaccinated susceptibles (prevalence)",
    caption = "Vaccinated susceptibles decrease due to infections."
  )

Check that fully protective (non-leaky) vaccination does not see a mid-epidemic reduction in vaccinated susceptibles.

# efficacy is 100 for non-leaky vaccination
vax <- daedalus_vaccination(
  "high",
  start_time = 30, efficacy = 100,
  uptake_limit = 40
)
output <- daedalus2("GBR", "influenza_2009", vaccine_investment = vax)

data <- get_data(output) %>%
  filter(vaccine_group == "vaccinated", compartment == "susceptible") %>%
  group_by(time) %>%
  summarise(
    value = sum(value)
  )

# check that vaccinations begin at time = 100
ggplot(data, aes(time, value)) +
  geom_line() +
  labs(
    x = "Time", y = "Vaccinated susceptibles (prevalence)",
    caption = "Vaccinated susceptibles do not decrease due to infections."
  )

Events

This section is a visual check on events.

# trigger event on low hospital capacity
x <- daedalus_country("GBR")
x$hospital_capacity <- 1e3
x
#> <daedalus_country>
#> • Name: United Kingdom
#> • Demography: 3924490, 11762039, 39536463, and 12663012
#> • Community contact matrix:
#>             0-4      5-19    20-64       65+
#> 0-4   1.9157895 1.5379290 4.704999 0.2863619
#> 5-19  0.5131412 8.7339228 5.874591 0.7418483
#> 20-64 0.4670302 1.7476822 7.830182 1.0685802
#> 65+   0.1180517 0.7548304 3.531487 1.5212437
#> • GNI (PPP $): 45870
#> • Hospital capacity: 1000

output_npi <- daedalus2(
  x, "influenza_2009",
  response_strategy = "elimination"
)
output_no_npi <- daedalus2(
  x, "influenza_2009"
)

# get incidence data and check for differences
data_npi <- get_incidence(output_npi, "infections")
data_no_npi <- get_incidence(output_no_npi, "infections")

# add scenario marker and plot comparison
data_npi$scenario <- "npi"
data_no_npi$scenario <- "no_npi"

data <- bind_rows(data_npi, data_no_npi)

ggplot(data, aes(time, value, col = scenario)) +
  geom_line() +
  labs(
    x = "Time", y = "Incidence"
  )