ggsurveillance

ggsurveillance is an R package with helpful tools and ggplot extensions for epidemiology, especially infectious disease surveillance and outbreak investigation. All functions provide tidy functional interfaces for easy integration with the tidyverse. For documentation and vignettes see: ggsurveillance.biostats.dev

This packages provides:

• geom_epicurve() : A ggplot geom for plotting epicurves

  • including stat_bin_date() for date interval (week, month etc.) based binning of case numbers with perfect alignment with i.e. reporting week.
  • including scale_y_cases_5er() for better (case) count axis breaks and positioning.
  • including geom_vline_year(), which automatically detects the turn of the year(s) from the date or datetime axis and draws a vertical line.

• align_dates_seasonal() : Align surveillance data for seasonal plots (e.g. flu season).

• create_agegroups(): Create reproducible age groups with highly customizable labels.

• geom_epigantt() : A geom for epigantt plots. Helpful to visualize overlapping time intervals for contact tracing (i.e. hospital outbreaks).

  • including scale_y_discrete_reverse() which reverses the order of the categorical scale.

• and more: geometric_mean() , expand_counts()

Creating Epicurves

library(ggplot2)
library(tidyr)
library(outbreaks)
library(ggsurveillance)

sars_canada_2003 |> #SARS dataset from outbreaks
  pivot_longer(starts_with("cases"), 
               names_prefix = "cases_", 
               names_to = "origin") |>
  ggplot(aes(x = date, weight = value, fill = origin)) +
  geom_epicurve(date_resolution = "week") +
  scale_x_date(date_labels = "W%V'%g", date_breaks = "2 weeks") +
  scale_y_cases_5er() +
  scale_fill_brewer(type = "qual", palette = 6) +
  theme_classic()

Align surveillance data for seasonal comparison

library(ggplot2)
library(dplyr)
library(ggsurveillance)

influenza_germany |>
  filter(AgeGroup == "00+") |>
  align_dates_seasonal(dates_from = ReportingWeek,
                       date_resolution = "isoweek",
                       start = 28) -> df_flu_aligned

ggplot(df_flu_aligned, aes(x = date_aligned, y = Incidence)) +
  stat_summary(
    aes(linetype = "Historical Median (Min-Max)"), data = . %>% filter(!current_season), 
    fun.data = median_hilow, geom = "ribbon", alpha = 0.3) +
  stat_summary(
    aes(linetype = "Historical Median (Min-Max)"), data = . %>% filter(!current_season), 
    fun = median, geom = "line") +
  geom_line(
    aes(linetype = "2024/25"), data = . %>% filter(current_season), colour = "dodgerblue4", linewidth = 2) +
  labs(linetype = NULL) +
  scale_x_date(date_labels = "%b'%y") +
  theme_bw() +
  theme(legend.position = c(0.2,0.8))

Create Epigantt plots to visualize exposure intervals in outbreaks

library(dplyr)
library(tidyr)
library(ggplot2)
library(ggsurveillance)

# Transform to long format
linelist_hospital_outbreak |>
  pivot_longer(
    cols = starts_with("ward"),
    names_to = c(".value", "num"),
    names_pattern = "ward_(name|start_of_stay|end_of_stay)_([0-9]+)",
    values_drop_na = TRUE
  ) -> df_stays_long

linelist_hospital_outbreak |>
  pivot_longer(cols = starts_with("pathogen"), values_to = "date") -> df_detections_long

# Plot
ggplot(df_stays_long) +
  geom_epigantt(aes(y = Patient, xmin = start_of_stay, xmax = end_of_stay, color = name)) +
  geom_point(aes(y = Patient, x = date, shape = "Date of pathogen detection"), data = df_detections_long) +
  scale_y_discrete_reverse() +
  theme_bw() +
  theme(legend.position = "bottom")