An epidemic curve (epi curve) is a histogram showing the number of new disease cases over time, plotted by date of symptom onset. The shape of the curve reveals the outbreak pattern: a sharp peak indicates a point source, successive waves suggest propagated transmission, and a plateau indicates continuous exposure. It is fundamental to epidemiological investigation and public health surveillance.
""" anyplot.ai
histogram-epidemic: Epidemic Curve (Epi Curve)
Library: plotnine 0.15.4 | Python 3.13.13
Quality: 91/100 | Updated: 2026-06-02
"""
import os
import numpy as np
import pandas as pd
from plotnine import (
aes,
annotate,
element_blank,
element_line,
element_rect,
element_text,
geom_col,
geom_line,
geom_text,
geom_vline,
ggplot,
labs,
scale_fill_manual,
scale_x_date,
scale_y_continuous,
theme,
theme_minimal,
)
# Theme tokens (Imprint palette — theme-adaptive chrome)
THEME = os.getenv("ANYPLOT_THEME", "light")
PAGE_BG = "#FAF8F1" if THEME == "light" else "#1A1A17"
ELEVATED_BG = "#FFFDF6" if THEME == "light" else "#242420"
INK = "#1A1A17" if THEME == "light" else "#F0EFE8"
INK_SOFT = "#4A4A44" if THEME == "light" else "#B8B7B0"
INK_MUTED = "#6B6A63" if THEME == "light" else "#A8A79F"
# Imprint categorical palette — positions 1-3 for stacked case types; position 4 for cumulative line
IMPRINT_PALETTE = ["#009E73", "#C475FD", "#4467A3", "#BD8233", "#AE3030", "#2ABCCD", "#954477", "#99B314"]
# Data — two-wave epidemic model (point-source → propagated transmission)
np.random.seed(42)
start_date = pd.Timestamp("2024-01-15")
dates = pd.date_range(start_date, periods=90, freq="D")
days = np.arange(90)
confirmed_rate = (
45 * np.exp(-0.5 * ((days - 18) / 5) ** 2) + 25 * np.exp(-0.5 * ((days - 55) / 10) ** 2) + np.random.poisson(2, 90)
)
probable_rate = (
15 * np.exp(-0.5 * ((days - 20) / 6) ** 2) + 10 * np.exp(-0.5 * ((days - 57) / 11) ** 2) + np.random.poisson(1, 90)
)
suspect_rate = (
8 * np.exp(-0.5 * ((days - 22) / 7) ** 2) + 5 * np.exp(-0.5 * ((days - 60) / 12) ** 2) + np.random.poisson(1, 90)
)
confirmed = np.maximum(confirmed_rate.astype(int), 0)
probable = np.maximum(probable_rate.astype(int), 0)
suspect = np.maximum(suspect_rate.astype(int), 0)
df = pd.DataFrame(
{
"onset_date": np.tile(dates, 3),
"case_count": np.concatenate([confirmed, probable, suspect]),
"case_type": ["Confirmed"] * 90 + ["Probable"] * 90 + ["Suspect"] * 90,
}
)
df["case_type"] = pd.Categorical(df["case_type"], categories=["Suspect", "Probable", "Confirmed"], ordered=True)
daily_totals = df.groupby("onset_date")["case_count"].sum().reset_index()
daily_totals["cumulative"] = daily_totals["case_count"].cumsum()
max_daily = daily_totals["case_count"].max()
max_cumulative = int(daily_totals["cumulative"].max())
daily_totals["cumulative_scaled"] = daily_totals["cumulative"] / max_cumulative * max_daily
lockdown_date = pd.Timestamp("2024-02-10")
vaccination_date = pd.Timestamp("2024-03-01")
interventions = pd.DataFrame(
{"date": [lockdown_date, vaccination_date], "label": ["Lockdown", "Vaccination\ncampaign"]}
)
wave1_idx = daily_totals.loc[daily_totals["onset_date"] < "2024-03-01", "case_count"].idxmax()
wave2_idx = daily_totals.loc[daily_totals["onset_date"] >= "2024-03-01", "case_count"].idxmax()
wave1_date = daily_totals.loc[wave1_idx, "onset_date"]
wave1_peak = daily_totals.loc[wave1_idx, "case_count"]
wave2_date = daily_totals.loc[wave2_idx, "onset_date"]
wave2_peak = daily_totals.loc[wave2_idx, "case_count"]
cumul_label = f"Cumulative: {max_cumulative:,} cases →"
# Plot
title = "histogram-epidemic · python · plotnine · anyplot.ai"
plot = (
ggplot(df, aes(x="onset_date", y="case_count"))
+ geom_col(aes(fill="case_type"), width=1.0)
+ geom_line(
data=daily_totals,
mapping=aes(x="onset_date", y="cumulative_scaled"),
color=IMPRINT_PALETTE[3],
size=1.5,
alpha=0.9,
)
+ geom_vline(
data=interventions, mapping=aes(xintercept="date"), linetype="dashed", color=INK_SOFT, size=0.5, alpha=0.8
)
+ geom_text(
data=interventions,
mapping=aes(x="date", label="label"),
y=max_daily * 0.88,
ha="left",
nudge_x=1.5,
size=4,
color=INK_MUTED,
fontstyle="italic",
)
+ annotate(
"text",
x=dates[-1],
y=daily_totals["cumulative_scaled"].iloc[-1] * 1.03,
label=cumul_label,
ha="right",
va="bottom",
size=3.5,
color=IMPRINT_PALETTE[3],
fontstyle="italic",
fontweight="bold",
)
+ annotate(
"text",
x=wave1_date,
y=wave1_peak + 3,
label=f"Wave 1 peak\n{wave1_peak} cases/day",
ha="center",
va="bottom",
size=3.5,
color=INK,
fontweight="bold",
)
+ annotate(
"text",
x=wave2_date,
y=wave2_peak + 3,
label=f"Wave 2 peak\n{wave2_peak} cases/day",
ha="center",
va="bottom",
size=3.5,
color=INK,
fontweight="bold",
)
+ scale_fill_manual(
values={"Confirmed": IMPRINT_PALETTE[0], "Probable": IMPRINT_PALETTE[1], "Suspect": IMPRINT_PALETTE[2]}
)
+ scale_x_date(date_breaks="2 weeks", date_labels="%b %d")
+ scale_y_continuous(expand=(0, 0, 0.12, 0))
+ labs(x="Date of Symptom Onset", y="Number of New Cases (per day)", fill="Case Classification", title=title)
+ theme_minimal()
+ theme(
figure_size=(8, 4.5),
text=element_text(size=7, color=INK),
axis_title=element_text(size=10, color=INK),
axis_text=element_text(size=8, color=INK_SOFT),
axis_text_x=element_text(rotation=45, ha="right", color=INK_SOFT),
plot_title=element_text(size=13, color=INK, fontweight="bold"),
legend_text=element_text(size=8, color=INK_SOFT),
legend_title=element_text(size=8, color=INK),
legend_position="top",
legend_background=element_rect(fill=ELEVATED_BG, color=INK_SOFT),
panel_border=element_blank(),
panel_grid_major_x=element_blank(),
panel_grid_minor=element_blank(),
panel_grid_major_y=element_line(color=INK, size=0.3, alpha=0.15),
axis_line_x=element_line(color=INK_SOFT, size=0.5),
axis_line_y=element_line(color=INK_SOFT, size=0.5),
plot_background=element_rect(fill=PAGE_BG, color=PAGE_BG),
panel_background=element_rect(fill=PAGE_BG),
)
)
# Save
plot.save(f"plot-{THEME}.png", dpi=400, width=8, height=4.5, units="in", verbose=False)
Part of Epidemic Curve (Epi Curve) on anyplot.ai.