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: plotly 6.7.0 | Python 3.13.13
Quality: 84/100 | Updated: 2026-06-02
"""
import os
import numpy as np
import pandas as pd
import plotly.graph_objects as go
THEME = os.getenv("ANYPLOT_THEME", "light")
# Imprint palette — theme-independent data colors
IMPRINT_PALETTE = ["#009E73", "#C475FD", "#4467A3", "#BD8233", "#AE3030", "#2ABCCD", "#954477", "#99B314"]
# Theme-adaptive chrome
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"
GRID = "rgba(26,26,23,0.15)" if THEME == "light" else "rgba(240,239,232,0.15)"
# Data — two influenza-like illness seasons over 104 weeks (weekly bins)
# Seasonal cycling pattern: distinct winter peaks, low endemic summer baseline
np.random.seed(17)
weeks = pd.date_range("2022-09-05", periods=104, freq="W-MON")
t = np.arange(104)
# Season 1 peak: ~week 20 (mid-Jan 2023); Season 2 peak: ~week 72 (mid-Jan 2024)
season1 = 320 * np.exp(-0.5 * ((t - 20) / 7) ** 2)
season2 = 275 * np.exp(-0.5 * ((t - 72) / 8) ** 2)
endemic = 35 + 12 * np.sin(2 * np.pi * t / 52 - np.pi / 2)
base_cases = np.maximum(0, season1 + season2 + endemic + np.random.poisson(8, 104))
confirmed = np.round(base_cases * 0.58).astype(int)
probable = np.round(base_cases * 0.30).astype(int)
suspect = np.round(base_cases * 0.12).astype(int)
cumulative = np.cumsum(confirmed + probable + suspect)
# Convert to strings for kaleido JSON serialization
weeks_str = [d.strftime("%Y-%m-%d") for d in weeks]
fig = go.Figure()
fig.add_trace(
go.Bar(
x=weeks_str,
y=confirmed,
name="Confirmed",
marker={"color": IMPRINT_PALETTE[0], "line": {"color": PAGE_BG, "width": 0.4}},
hovertemplate="%{y} cases<extra></extra>",
)
)
fig.add_trace(
go.Bar(
x=weeks_str,
y=probable,
name="Probable",
marker={"color": IMPRINT_PALETTE[1], "line": {"color": PAGE_BG, "width": 0.4}},
hovertemplate="%{y} cases<extra></extra>",
)
)
fig.add_trace(
go.Bar(
x=weeks_str,
y=suspect,
name="Suspect",
marker={"color": IMPRINT_PALETTE[2], "line": {"color": PAGE_BG, "width": 0.4}},
hovertemplate="%{y} cases<extra></extra>",
)
)
# Cumulative burden line on secondary y-axis
fig.add_trace(
go.Scatter(
x=weeks_str,
y=cumulative,
name="Cumulative",
yaxis="y2",
mode="lines",
line={"color": IMPRINT_PALETTE[3], "width": 2.5},
hovertemplate="%{y:,} total<extra></extra>",
)
)
# Intervention annotation lines: vaccination campaign rollouts before each season peak
for evt_date, evt_label, evt_color in [
(weeks_str[13], "Flu vaccine rollout", IMPRINT_PALETTE[4]), # Dec 2022, before Season 1 peak
(weeks_str[60], "Flu vaccine rollout", IMPRINT_PALETTE[5]), # Oct 2023, before Season 2 peak
]:
fig.add_shape(
type="line",
x0=evt_date,
x1=evt_date,
y0=0,
y1=0.78,
yref="paper",
line={"color": evt_color, "width": 2, "dash": "dashdot"},
)
fig.add_annotation(
x=evt_date,
y=0.85,
yref="paper",
text=f"<b>{evt_label}</b>",
showarrow=True,
arrowhead=0,
arrowwidth=1.5,
arrowcolor=evt_color,
ax=0,
ay=-30,
font={"size": 10, "color": evt_color, "family": "Arial"},
align="center",
bgcolor=ELEVATED_BG,
bordercolor=evt_color,
borderwidth=1,
borderpad=3,
)
fig.update_layout(
autosize=False,
title={
"text": "histogram-epidemic · python · plotly · anyplot.ai",
"font": {"size": 16, "color": INK, "family": "Arial"},
"x": 0.5,
"xanchor": "center",
"y": 0.93,
"yanchor": "top",
},
hovermode="x unified",
xaxis={
"title": {"text": "Week of Symptom Onset", "font": {"size": 12, "color": INK}},
"tickfont": {"size": 10, "color": INK_SOFT},
"tickformat": "%b %Y",
"dtick": "M3",
"tickangle": -30,
"showgrid": False,
"zeroline": False,
"showline": False,
"spikemode": "across",
"spikethickness": 1,
"spikecolor": INK_SOFT,
"spikedash": "dot",
},
yaxis={
"title": {"text": "Weekly New Cases", "font": {"size": 12, "color": INK}},
"tickfont": {"size": 10, "color": INK_SOFT},
"showgrid": True,
"gridcolor": GRID,
"gridwidth": 1,
"zeroline": False,
"showline": False,
"rangemode": "tozero",
},
yaxis2={
"title": {"text": "Cumulative Cases", "font": {"size": 12, "color": IMPRINT_PALETTE[3]}},
"tickfont": {"size": 10, "color": IMPRINT_PALETTE[3]},
"overlaying": "y",
"side": "right",
"showgrid": False,
"zeroline": False,
"rangemode": "tozero",
},
barmode="stack",
bargap=0.15,
paper_bgcolor=PAGE_BG,
plot_bgcolor=PAGE_BG,
font={"color": INK, "family": "Arial"},
legend={
"font": {"size": 10, "color": INK_SOFT},
"orientation": "h",
"traceorder": "normal",
"yanchor": "bottom",
"y": 1.02,
"xanchor": "center",
"x": 0.5,
"bgcolor": ELEVATED_BG,
"bordercolor": INK_SOFT,
"borderwidth": 1,
},
margin={"l": 80, "r": 80, "t": 90, "b": 100},
)
fig.write_image(f"plot-{THEME}.png", width=800, height=450, scale=4)
fig.write_html(f"plot-{THEME}.html", include_plotlyjs="cdn")
Part of Epidemic Curve (Epi Curve) on anyplot.ai.