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: seaborn 0.13.2 | Python 3.13.13
Quality: 87/100 | Updated: 2026-06-02
"""
import os
import matplotlib.dates as mdates
import matplotlib.lines as mlines
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
# Theme tokens
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"
ANYPLOT_AMBER = "#DDCC77"
# Imprint categorical palette — canonical order, first series always #009E73
IMPRINT_PALETTE = ["#009E73", "#C475FD", "#4467A3", "#BD8233", "#AE3030", "#2ABCCD", "#954477", "#99B314"]
# Data
np.random.seed(42)
dates_range = pd.date_range("2024-01-15", periods=120, freq="D")
confirmed_base = np.concatenate(
[
np.linspace(2, 35, 30),
np.linspace(35, 80, 15),
np.linspace(80, 120, 10),
np.linspace(120, 60, 20),
np.linspace(60, 25, 15),
np.linspace(25, 45, 10),
np.linspace(45, 15, 20),
]
)
confirmed_counts = np.maximum(0, confirmed_base + np.random.normal(0, 8, 120)).astype(int)
probable_counts = np.maximum(0, confirmed_counts * 0.35 + np.random.normal(0, 3, 120)).astype(int)
suspect_counts = np.maximum(0, confirmed_counts * 0.20 + np.random.normal(0, 2, 120)).astype(int)
# Long-form DataFrame for sns.histplot stacking
rows = []
for i, date in enumerate(dates_range):
rows.extend([(date, "Confirmed")] * confirmed_counts[i])
rows.extend([(date, "Probable")] * probable_counts[i])
rows.extend([(date, "Suspect")] * suspect_counts[i])
cases_df = pd.DataFrame(rows, columns=["onset_date", "case_type"])
daily_totals = confirmed_counts + probable_counts + suspect_counts
cumulative = np.cumsum(daily_totals)
# Configure theme
sns.set_theme(
style="ticks",
rc={
"figure.facecolor": PAGE_BG,
"axes.facecolor": PAGE_BG,
"axes.edgecolor": INK_SOFT,
"axes.labelcolor": INK,
"text.color": INK,
"xtick.color": INK_SOFT,
"ytick.color": INK_SOFT,
"grid.color": INK,
"grid.alpha": 0.15,
"legend.facecolor": ELEVATED_BG,
"legend.edgecolor": INK_SOFT,
},
)
# Plot — 16:9 landscape canvas (3200 × 1800 px)
fig, ax = plt.subplots(figsize=(8, 4.5), dpi=400, facecolor=PAGE_BG)
# Weekly bins (~17 bins for 120-day outbreak); spec recommends weekly for > 3 months
bin_edges = mdates.date2num(pd.date_range("2024-01-14", periods=19, freq="7D"))
palette = {
"Confirmed": IMPRINT_PALETTE[0], # #009E73 green
"Probable": IMPRINT_PALETTE[1], # #C475FD lavender
"Suspect": IMPRINT_PALETTE[2], # #4467A3 blue
}
sns.histplot(
data=cases_df,
x="onset_date",
hue="case_type",
hue_order=["Confirmed", "Probable", "Suspect"],
multiple="stack",
palette=palette,
bins=bin_edges,
edgecolor=PAGE_BG,
linewidth=0.5,
legend=True,
ax=ax,
)
y_max = ax.get_ylim()[1]
# Peak period — amber shading only, no text label (reduces visual competition)
ax.axvspan(pd.Timestamp("2024-02-25"), pd.Timestamp("2024-03-25"), alpha=0.13, color=ANYPLOT_AMBER, zorder=0)
# Cumulative cases on secondary axis
ax2 = ax.twinx()
ax2.plot(dates_range, cumulative, color=INK_SOFT, linewidth=2.0, alpha=0.75, zorder=3)
ax2.set_ylabel("Cumulative Cases", fontsize=10, color=INK_SOFT, labelpad=8)
ax2.tick_params(axis="y", labelsize=8, colors=INK_SOFT)
ax2.spines["right"].set_color(INK_SOFT)
ax2.spines["top"].set_visible(False)
ax2.spines["left"].set_visible(False)
ax2.spines["bottom"].set_visible(False)
# Intervention markers — staggered labels to avoid overlap with peak shading
intervention_dates = [
(pd.Timestamp("2024-02-20"), "Travel\nRestrictions", 4),
(pd.Timestamp("2024-03-25"), "Vaccination\nCampaign", 4),
]
for date, label, day_offset in intervention_dates:
ax.axvline(date, color=INK_MUTED, linewidth=1.4, linestyle="--", alpha=0.85, zorder=5)
ax.annotate(
label,
xy=(date + pd.Timedelta(days=day_offset), y_max * 0.84),
fontsize=7,
fontweight="semibold",
ha="center",
va="top",
color=INK,
bbox={
"boxstyle": "round,pad=0.3",
"facecolor": ELEVATED_BG,
"edgecolor": INK_SOFT,
"linewidth": 0.8,
"alpha": 0.92,
},
)
# Style
title = "histogram-epidemic · python · seaborn · anyplot.ai"
ax.set_title(title, fontsize=12, fontweight="medium", color=INK, pad=10)
ax.set_xlabel("Date of Symptom Onset", fontsize=10, color=INK)
ax.set_ylabel("New Cases (Weekly)", fontsize=10, color=INK)
ax.tick_params(axis="both", labelsize=8, colors=INK_SOFT)
ax.xaxis.set_major_locator(mdates.WeekdayLocator(byweekday=0, interval=2))
ax.xaxis.set_major_formatter(mdates.DateFormatter("%b %d"))
plt.setp(ax.get_xticklabels(), rotation=45, ha="right")
sns.despine(ax=ax)
ax.yaxis.grid(True, alpha=0.15, linewidth=0.8)
ax.set_axisbelow(True)
# Combine legend: histogram series + cumulative line
legend = ax.get_legend()
handles = list(legend.legend_handles)
labels = [t.get_text() for t in legend.get_texts()]
legend.remove()
handles.append(mlines.Line2D([], [], color=INK_SOFT, linewidth=2.0, alpha=0.75))
labels.append("Cumulative Cases")
ax.legend(
handles=handles, labels=labels, fontsize=8, loc="upper left", framealpha=0.92, edgecolor=INK_SOFT, fancybox=False
)
# Margins: room for rotated x-tick labels at bottom, secondary axis label at right
fig.subplots_adjust(bottom=0.20, right=0.87)
# Save
plt.savefig(f"plot-{THEME}.png", dpi=400, facecolor=PAGE_BG)
Part of Epidemic Curve (Epi Curve) on anyplot.ai.