In pratikunterwegs/patho-move-evol: Analysis code for the _pathomove_ model.

Plot infection related outcomes

library(data.table)
library(glue)
library(ggplot2)
library(colorspace)
library(patchwork)

# some parameters
sgen <- 3000
genmax <- 5000
popsize <- 500

# load data
data_infections_gen <- fread("data/results/data_infections_gen.csv")

# subset data
data_infections_gen <- data_infections_gen[scenario_tag == "default"]
data_infections_gen <- data_infections_gen[costInfect == 0.25 &
  regen_time == 50 &
  dispersal == 2]

# absolute generations
data_infections_gen[, gen_abs := gen - sgen]

Plot infections per generation

# make figures
fig_gen_infect <-
  ggplot(data_infections_gen[gen >= sgen & gen %% 50 == 0]) +
  stat_bin2d(
    data = data_infections_gen[gen >= sgen],
    aes(
      gen_abs, n_infected / popsize
    ),
    linewidth = 0.1,
    linetype = "dashed",
    show.legend = F
  ) +
  geom_hline(
    yintercept = 0.5,
    col = "grey",
    linewidth = 0.2
  ) +
  stat_summary(
    aes(
      gen_abs, n_infected / popsize
    ),
    fill = "firebrick",
    shape = 21, size = 0.2,
    show.legend = FALSE
  ) +
  scale_fill_continuous_sequential(
    palette = "Reds",
    l1 = 70, l2 = 100,
    c1 = 50, c2 = 10
  ) +
  scale_x_continuous(
    breaks = c(-500, 0, 500, 2000),
    labels = scales::comma_format(
      accuracy = 1
    ),
    name = "Gens. after pathogen intro."
  ) +
  scale_y_continuous(
    labels = scales::percent,
    breaks = c(0.2, 0.5, 0.8),
    name = "% Population infected"
  ) +
  coord_cartesian(
    ylim = c(0.2, 0.8)
  ) +
  theme_test(
    base_family = "Arial",
    base_size = 8
  ) +
  theme(
    axis.text.y = element_text(
      angle = 90,
      hjust = 0.5,
      size = 6
    )
  )

Plot SIR model outcomes

# spillover data and rounding
popsize <- 500
replicates <- 10

data_sir_models <- fread("data/results/data_sir_models.csv")

# select scenarios
data_sir_models <- data_sir_models[scenario == "Novel pathogen"]

# data_sir_models$mean = data_sir_models$mean / 500
data_sir_models <- data_sir_models[class == "NR" & gamma == 1 &
  beta == 5 & threshold %in% c(1, 10)]

# select scenario
data_default <- data_sir_models[regen_time == 50 & costInfect == 0.25]

# round time, with larger bins later in time
# ie, increments of 0, 0.25, 0.5, 1, 2, 5, 10 etc.
data_default[, tround := plyr::round_any(time, 0.25)]
data_default[, tround := fifelse(
  time > 5, plyr::round_any(time, 0.5),
  tround
)]
data_default[, type := factor(type, levels = c("pre", "post"))]

# save data default sir
fwrite(
  data_default,
  file = "data/results/data_default_sir.csv"
)

# load saved data to save time
data_default <- fread("data/results/data_default_sir.csv")
data_default[, type := factor(type, levels = c("pre", "post"))]

# plot the progress of disease in default network sir models
fig_sir <-
  ggplot(data_default) +
  geom_hline(
    yintercept = 0.5,
    col = "grey",
    size = 0.2
  ) +
  geom_jitter(
    aes(
      time, mean / popsize,
      col = type,
      group = interaction(repl, sc_repl, threshold)
    ),
    size = 0.1,
    show.legend = F
  ) +
  stat_summary(
    aes(
      tround, mean / popsize,
      fill = type,
      shape = type,
      group = interaction(type, threshold, beta)
    ),
    position = position_dodge(width = 0.1),
    show.legend = F
  ) +
  facet_wrap(
    ~threshold,
    labeller = labeller(
      threshold = c(
        "1" = "1 encounter",
        "10" = "10 encounters"
      )
    )
  ) +
  scale_colour_discrete_diverging(
    c1 = 30,
    l1 = 50, l2 = 80,
    palette = "Blue-Red 2",
    rev = FALSE,
    breaks = c("pre", "post"),
    labels = c("Pre-pathogen intro.", "Post-pathogen intro.")
  ) +
  scale_fill_discrete_diverging(
    palette = "Blue-Red 2",
    rev = FALSE,
    breaks = c("pre", "post"),
    labels = c("Pre-pathogen intro.", "Post-pathogen intro.")
  ) +
  scale_shape_manual(
    values = c(
      "pre" = 21,
      "post" = 24
    ),
    breaks = c("pre", "post"),
    labels = c("Pre-pathogen intro.", "Post-pathogen intro.")
  ) +
  scale_y_continuous(
    labels = scales::percent,
    breaks = c(0, 0.5, 1)
  ) +
  theme_test(
    base_family = "Arial",
    base_size = 8
  ) +
  theme(
    legend.position = "top",
    strip.background = element_blank(),
    strip.text = element_text(
      face = "bold"
    ),
    axis.text.y = element_text(
      angle = 90,
      hjust = 0.5,
      size = 6
    )
  ) +
  coord_cartesian(
    xlim = c(0, 6),
    ylim = c(0, 1)
  ) +
  labs(
    x = "SIR model time",
    y = "% Agents infected",
    fill = NULL,
    colour = NULL,
    shape = NULL
  )

Save figure: infection related outcomes

# add layers
plot_disease <- wrap_plots(
  fig_gen_infect, fig_sir,
  guides = "collect",
  design = "ABB"
) &
  plot_annotation(
    tag_levels = "A"
  ) &
  theme(
    plot.tag = element_text(face = "bold"),
    legend.position = "top"
  )

ggsave(
  plot_disease,
  filename = "figures/fig_disease.png",
  width = 160, height = 70, units = "mm"
)