Comparison

# Create our population
iso3c <- "ATG"
pop <- hypatia:::get_population(iso3c)

# Scale it for speed
pop$n <- as.integer(pop$n / 10)

# Create our equivalent parameters
R0 <- 2
time_period <- 200
tt_contact_matrix <- 0

parameters <- hypatia::get_parameters(
  population = pop$n,
  contact_matrix_set = squire::get_mixing_matrix(iso3c = iso3c),
  iso3c = iso3c,
  R0 = R0,
  time_period = time_period
)

Run our simulation - default timesteps = 200

df_1 <- hypatia::run_simulation(pop, parameters)

PLOT RESULTS

First setup output data

subtitle <- paste(
  "Simulation for 1 run and 200 data points per run")

df <- dplyr::bind_rows(df_1, .id = "group")

statesnamevector <- c("human_S_count",
                           "human_E_count",
                           "human_IMild_count",
                           "human_IAsymp_count",
                           "human_ICase_count",
                           "human_IOxGetLive_count",
                           "human_IOxGetDie_count",
                           "human_IOxNotGetLive_count",
                           "human_IOxNotGetDie_count",
                           "human_IMVGetLive_count",
                           "human_IMVGetDie_count",
                           "human_IMVNotGetLive_count",
                           "human_IMVNotGetDie_count",
                           "human_IRec_count",
                           "human_R_count",
                           "human_D_count")

# Convert to long format
df <- tidyr::pivot_longer(tibble::as_tibble(df), all_of(statesnamevector))

strname <- "Hypatia Model Simulation"
subtitle <- paste(
    "Simulation for 1 run and 1000 data points")
time <- seq(1, time_period, 1)

Plot data

ggplot2::ggplot(
df, ggplot2::aes(x = timestep, y = value,
                 group = interaction(group, name), colour = name)) +
  ggplot2::geom_line(size = 0.5) +
  ggplot2::theme_bw() +
  ggplot2::labs(title = strname, subtitle = subtitle) +
  ggplot2::labs(y = "States", x = "time") +
  ggplot2::theme(
    legend.justification = c("right", "top"),
    legend.box = c("horizontal", "vertical")) +
  ggplot2::theme(
    text = ggplot2::element_text(color = "#444444"),
    plot.title = ggplot2::element_text(size = 26, color = "#333333"),
    plot.subtitle = ggplot2::element_text(size = 13),
    axis.title.x = ggplot2::element_text(size = 16, color = "#333333"),
    axis.title.y = ggplot2::element_text(angle = 0, vjust = .5))

Does it look similar to the squire output:

out <- squire::run_explicit_SEEIR_model(
  population = pop$n, 
  country = "Antigua and Barbuda", 
  contact_matrix_set = squire::get_mixing_matrix(iso3c = "ATG"),
  time_period = 200, 
  replicates = 1,
  day_return = TRUE, 
  dt = 0.01
)

plot(out)
#> Warning in plot.squire_simulation(out): Summary statistic estimated from <10
#> replicates
#> Warning in plot.squire_simulation(out): Confidence bounds estimated from <10
#> replicates

Get the actual comparative outputs. - remove state IAsymp as this is not in SQUIRE

df_1 <- dplyr::select(df_1, -human_IAsymp_count)

hyp <- tidyr::pivot_longer(df_1, -1)
hyp <- dplyr::rename(hyp, t = timestep, compartment = name, y = value)
hyp <- dplyr::mutate(hyp, compartment = gsub("(^human_)(\\w*)(_count)", "\\2", compartment),
         model = "hypatia")
hyp <- dplyr::select(hyp, c("t", "compartment", "y", "model"))

sq <- squire::format_output(out, unique(hyp$compartment))
sq <- dplyr::mutate(sq, model = "squire")
sq <- dplyr::select(sq, c("t", "compartment", "y", "model"))

ggplot2::ggplot(rbind(sq, hyp), ggplot2::aes(t, y, color = model)) +
  ggplot2::geom_line() +
  ggplot2::facet_wrap(~compartment, scales = "free")