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

Plot outcomes in the vertical transmission scenario

library(data.table)

library(ggplot2)
library(patchwork)
library(colorspace)

Load strategy frequency change data

files <- list.files(
  "data/results/morph_data",
  pattern = "vertical",
  full.names = TRUE
)

data_all <- lapply(files, fread)
data_all <- rbindlist(data_all)
# make a copy
df_strat <- copy(data_all)

# popsize
popsize <- 500
df_strat[, prop := N / popsize]

# get time since pathogen
sgen <- 3000
genmax <- 3500
df_strat[, gen_abs := gen - sgen]

# save for plotting and upload
fwrite(
  df_strat,
  file = "data/results/data_vertical_evo.csv"
)

# load saved data
df_strat <- fread("data/results/data_vertical_evo.csv")

Plot outcomes across parameter combinations

Plot evolutionary change in all replicates

# prepare figure comparing types of outcomes
p_compare <-
  ggplot(df_strat[gen %% 2 == 0 & (social_strat != "non-handler tracking"), ]) +
  geom_path(
    aes(
      gen_abs, prop,
      colour = social_strat,
      group = interaction(replicate, social_strat)
    ),
    size = 0.2
  ) +
  geom_vline(
    xintercept = c(0),
    lty = 2,
    size = 0.3,
    col = c("red")
  ) +
  facet_grid(
    cols = vars(p_v_transmit),
    rows = vars(costInfect),
    as.table = F,
    switch = c("y"),
    labeller = labeller(
      cost = function(x) sprintf("δE = %s", x),
      p_v_transmit = function(x) sprintf("p(V. Trans.) = %s", x)
    )
  ) +
  scale_colour_discrete_sequential(
    palette = "Viridis",
    l2 = 80,
    rev = FALSE,
    name = NULL,
    limits = c("agent avoiding", "agent tracking", "handler tracking"),
    labels = stringr::str_to_sentence,
    na.value = "lightgrey"
  ) +
  scale_x_continuous(
    # trans = ggallin::pseudolog10_trans,
    breaks = c(-500, 0, 500),
    labels = scales::comma_format(
      accuracy = 1
    ),
    name = "Gens. after pathogen intro.",
    sec.axis = sec_axis(
      trans = function(x) x + sgen,
      breaks = c(sgen, sgen + 500),
      labels = scales::comma_format(
        accuracy = 1
      ),
      name = "Generations"
    )
  ) +
  scale_y_continuous(
    breaks = c(0, 0.5, 1),
    labels = scales::percent,
    name = "% Individuals"
  ) +
  coord_cartesian(
    xlim = c(-500, 500),
    ylim = c(0, 1),
    expand = TRUE
  ) +
  theme_test(
    base_family = "Arial",
    base_size = 8
  ) +
  theme(
    legend.position = "top",
    legend.key.height = unit(1, "mm"),
    legend.key.width = unit(2, "mm"),
    axis.text.x = element_text(hjust = 0.5),
    axis.text.y = element_text(
      angle = 90, hjust = 0.5
    )
  )

# save for supplement
ggsave(
  p_compare,
  filename = "supplement/figures/fig_evo_change_vertical.png",
  height = 120,
  width = 120,
  units = "mm"
)

Plot infections in all replicates

# read in infections data
df_vertical_infections <- fread("data/results/data_infections_gen.csv")[
  scenario_tag == "vertical",
]
df_vertical_infections[, gen_abs := gen - sgen]

df_vertical_infections[, pathogen_eliminated := any(
  n_infected[(gen > sgen) & (gen < sgen + 500)] == 0
),
by = c("costInfect", "p_v_transmit", "replicate")
]

# infection persistence
p_infections <-
  ggplot(df_vertical_infections) +
  geom_path(
    aes(gen_abs, n_infected / popsize,
      group = replicate,
      col = pathogen_eliminated
    ),
    linewidth = 0.2
  ) +
  geom_vline(
    xintercept = c(0),
    lty = 2,
    size = 0.3,
    col = c("red")
  ) +
  facet_grid(
    cols = vars(p_v_transmit),
    rows = vars(costInfect),
    as.table = F,
    switch = c("y"),
    labeller = labeller(
      cost = function(x) sprintf("δE = %s", x),
      p_v_transmit = function(x) sprintf("p(V. Trans.) = %s", x)
    )
  ) +
  scale_colour_manual(
    values = c(
      "FALSE" = "tomato",
      "TRUE" = "lightgrey"
    ),
    labels = c(
      "FALSE" = "Pathogen persists",
      "TRUE" = "Pathogen eliminated"
    ),
    name = NULL
  ) +
  scale_x_continuous(
    breaks = c(0, 250, 500),
    labels = scales::comma_format(
      accuracy = 1
    ),
    name = "Gens. after pathogen intro.",
    sec.axis = sec_axis(
      trans = function(x) x + sgen,
      breaks = c(sgen, sgen + 250, sgen + 500),
      labels = scales::comma_format(
        accuracy = 1
      ),
      name = "Generations"
    )
  ) +
  scale_y_continuous(
    breaks = c(0, 0.5, 1),
    labels = scales::percent,
    name = "% Individuals"
  ) +
  coord_cartesian(
    xlim = c(-30, 500),
    ylim = c(0, 1)
  ) +
  theme_test(
    base_family = "Arial",
    base_size = 8
  ) +
  theme(
    legend.position = "top",
    legend.key.height = unit(1, "mm"),
    legend.key.width = unit(2, "mm"),
    axis.text.x = element_text(hjust = 0.5),
    axis.text.y = element_text(
      angle = 90, hjust = 0.5
    )
  )

# save for supplement
ggsave(
  p_infections,
  filename = "supplement/figures/fig_infection_vertical.png",
  height = 120,
  width = 120,
  units = "mm"
)

Plot infections and strategy change for the default scenario

# infections in the default combination
p_infections_default <-
  ggplot(
    df_vertical_infections[costInfect == 0.25 & p_v_transmit == 0.2, ][order(-pathogen_eliminated)]
  ) +
  geom_path(
    aes(gen_abs, n_infected / popsize,
      group = replicate,
      col = pathogen_eliminated
    ),
    linewidth = 0.2
  ) +
  geom_vline(
    xintercept = c(0),
    lty = 2,
    size = 0.3,
    col = c("red")
  ) +
  scale_colour_manual(
    values = c(
      "FALSE" = "firebrick",
      "TRUE" = "lightblue"
    ),
    labels = c(
      "FALSE" = "Pathogen persists",
      "TRUE" = "Pathogen eliminated"
    ),
    name = NULL
  ) +
  scale_x_continuous(
    breaks = c(0, 250, 500),
    labels = scales::comma_format(
      accuracy = 1
    ),
    name = "Gens. after pathogen intro.",
    sec.axis = sec_axis(
      trans = function(x) x + sgen,
      breaks = c(sgen, sgen + 250, sgen + 500),
      labels = scales::comma_format(
        accuracy = 1
      ),
      name = "Generations"
    )
  ) +
  scale_y_continuous(
    breaks = c(0, 0.5, 1),
    labels = scales::percent,
    name = "% Individuals"
  ) +
  coord_cartesian(
    xlim = c(-30, 500),
    ylim = c(0, 1)
  ) +
  theme_test(
    base_family = "Arial",
    base_size = 8
  ) +
  theme(
    legend.position = "top",
    legend.key.height = unit(1, "mm"),
    legend.key.width = unit(2, "mm"),
    axis.text.x = element_text(hjust = 0.5),
    axis.text.y = element_text(
      angle = 90, hjust = 0.5
    )
  )

Plot evolutionary change and infections for the default parameter combination

Pick two focal simulations in the cost = 0.25, p(v transmit) = 0.2 scenario.

Prepare data on strategies and infections

df_focus <- df_strat[p_v_transmit == 0.2 & costInfect == 0.25, ]

# subset for parameters
df_vertical_infections <- df_vertical_infections[p_v_transmit == 0.2 &
  costInfect == 0.25, ]

# get sims where pathogen is eliminated
df_vertical_infections[scenario_tag == "vertical", pathogen_eliminated := any(
  n_infected[(gen > sgen) & (gen < (sgen + 500))] == 0
),
by = c("costInfect", "p_v_transmit", "replicate")
]

# select replicates to show in plot
df_vertical_infections_focus <- df_vertical_infections[
  replicate == first(replicate[pathogen_eliminated]) |
    replicate == first(replicate[!pathogen_eliminated])
]

df_focus <- df_focus[replicate %in% df_vertical_infections_focus$replicate, ]

Plot evolutionary change and infections

p_evo <-
  ggplot() +
  geom_col(
    data = df_focus,
    aes(gen_abs, prop, fill = social_strat),
    width = 5
  ) +
  geom_line(
    data = df_vertical_infections_focus,
    aes(gen_abs, n_infected / popsize, col = "infected")
  ) +
  geom_vline(
    xintercept = c(0),
    lty = 2,
    size = 0.3,
    col = c("red")
  ) +
  facet_grid(
    cols = vars(replicate),
    labeller = labeller(
      replicate = c(
        "1" = "Pathogen persistance",
        "2" = "Pathogen eliminated"
      )
    ),
    switch = c("y")
  ) +
  scale_fill_discrete_sequential(
    palette = "Viridis",
    l2 = 70, alpha = 1, c1 = 20, c2 = 40,
    rev = FALSE, name = NULL,
    limits = c("agent avoiding", "agent tracking", "handler tracking"),
    labels = stringr::str_to_sentence,
    na.value = "lightgrey"
  ) +
  scale_colour_manual(
    values = c(infected = "firebrick"),
    labels = "% Infected",
    name = NULL
  ) +
  scale_x_continuous(
    breaks = c(-500, 0, 500),
    labels = scales::comma_format(
      accuracy = 1
    ),
    name = "Gens. after pathogen intro.",
    sec.axis = sec_axis(
      trans = function(x) x + sgen,
      breaks = c(sgen - 500, sgen, sgen + 250, sgen + 500),
      labels = scales::comma_format(
        accuracy = 1
      ),
      name = "Generations"
    )
  ) +
  scale_y_continuous(
    breaks = c(0, 0.5, 1),
    labels = scales::percent,
    name = "% Individuals"
  ) +
  coord_cartesian(
    xlim = c(-100, 510),
    ylim = c(0, 1),
    expand = FALSE
  ) +
  theme_test(
    base_family = "Arial",
    base_size = 8
  ) +
  theme(
    legend.position = "top",
    legend.key.height = unit(1, "mm"),
    legend.key.width = unit(2, "mm"),
    axis.text.x = element_text(hjust = 0.5),
    axis.text.y = element_text(
      angle = 90, hjust = 0.5
    )
  )

Summarise elimination by parameters

# read in infections data
df_vertical_infections <- fread("data/results/data_infections_gen.csv")[
  scenario_tag == "vertical",
]
df_vertical_infections[, gen_abs := gen - sgen]

df_vertical_infections[, pathogen_eliminated := any(
  n_infected[(gen > sgen) & (gen < sgen + 250)] == 0
),
by = c("costInfect", "p_v_transmit", "replicate")
]

# count outcomes
df_outcomes <- unique(
  df_vertical_infections[, c(
    "costInfect",
    "p_v_transmit", "pathogen_eliminated",
    "replicate"
  )]
)

df_outcomes <- df_outcomes[, .N, by = c(
  "pathogen_eliminated",
  "costInfect", "p_v_transmit"
)]

# plot count of replicates with elimination
plot_summarise_vertical <-
  ggplot(df_outcomes) +
  geom_col(
    aes(x = 1, y = N / 10, fill = pathogen_eliminated),
    position = "stack"
  ) +
  facet_grid(
    cols = vars(p_v_transmit),
    rows = vars(costInfect),
    as.table = F,
    switch = c("y"),
    labeller = labeller(
      costInfect = function(x) sprintf("δE = %s", x),
      p_v_transmit = function(x) sprintf("p(vert.) = %s", x)
    )
  ) +
  scale_fill_discrete_diverging(
    palette = "Blue-Red 3",
    rev = TRUE,
    l1 = 50, l2 = 80,
    labels = c("Pathogen persists", "Pathogen eliminated"),
    name = NULL
  ) +
  scale_y_continuous(
    breaks = c(0.5, 1),
    labels = scales::percent
  ) +
  labs(
    x = NULL,
    y = "% Replicates"
  ) +
  coord_cartesian(
    expand = FALSE
  ) +
  theme_test(
    base_family = "Arial",
    base_size = 8
  ) +
  theme(
    legend.position = "bottom",
    axis.ticks.x = element_blank(),
    axis.text.x = element_blank(),
    legend.justification = "left",
    legend.key.width = unit(2, "mm"),
    legend.key.height = unit(1, "mm"),
    axis.text.y = element_text(
      angle = 90, hjust = 0.5
    )
  )

Prepare main text figure

fig_eco_evo_vertical <-
  wrap_plots(
    p_evo, plot_summarise_vertical,
    design = "AAB"
  ) &
    plot_annotation(
      tag_levels = c("A", 1)
    ) &
    theme(
      legend.position = "bottom",
      plot.tag = element_text(
        face = "bold",
        size = 10
      )
    )

ggsave(
  fig_eco_evo_vertical,
  filename = "figures/fig_eco_evo_vertical.png",
  height = 80, width = 180, units = "mm"
)