Modelling threshold-dependent economic closures

library(daedalus)
library(data.table)
library(ggplot2)

response_threshold <- 1000

Initially run the model with no response, and then with an elimination response activated when total hospitalisations reach 1000 or after 30 days, whichever is sooner.

data_baseline <- daedalus(
  "Canada",
  daedalus_infection("influenza_1918", rho = 0.0), # prevent re-infection
  response_threshold = response_threshold,
  response_strategy = "none"
)

# get the model timeseries
data_baseline <- get_data(data_baseline)
data_baseline$scenario <- "no_response"

# run the model with a heavy elimination intervention
data_intervention <- daedalus(
  "Canada",
  daedalus_infection("influenza_1918", rho = 0.0), # prevent re-infection
  response_threshold = response_threshold,
  response_strategy = "elimination"
)

# get the model timeseries
data_intervention <- get_data(data_intervention)
data_intervention$scenario <- "elimination"

Plot the total hospital occupancy for both scenarios to view the effect of interventions.

data <- rbindlist(list(data_baseline, data_intervention))

# sum over age and econ strata as total is more relevant
data <- data[compartment == "hospitalised", .(value = sum(value)),
  by = c("time", "compartment", "scenario")
]

# check actual outcomes of interest - these don't look as good
ggplot(data) +
  geom_line(aes(time, value, colour = scenario)) +
  geom_hline(
    yintercept = response_threshold, linetype = "dotted"
  ) +
  labs(y = "Total hospital occupancy", x = "Days", col = "Scenario") +
  theme(legend.position = "top")

Note that the effect of response strategies that introduce closures does not appear to be very large — this is because the full range of interventions associated with each strategy is yet to be implemented.