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: bokeh 3.9.0 | Python 3.13.13
Quality: 93/100 | Updated: 2026-06-02
"""
import os
import sys
# Prevent this file (bokeh.py) from shadowing the installed bokeh package when
# Python prepends the script's directory to sys.path at startup.
_here = os.path.dirname(os.path.abspath(__file__))
sys.path = [p for p in sys.path if os.path.abspath(p) != _here]
import time
from pathlib import Path
import numpy as np
import pandas as pd
from bokeh.io import output_file, save
from bokeh.models import (
ColumnDataSource,
HoverTool,
Label,
Legend,
LegendItem,
LinearAxis,
NumeralTickFormatter,
Range1d,
Span,
)
from bokeh.plotting import figure
from selenium import webdriver
from selenium.webdriver.chrome.options import Options
# Theme tokens (Imprint palette — default-style-guide.md "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"
# Imprint categorical palette — position 1 is ALWAYS first series
IMPRINT_PALETTE = ["#009E73", "#C475FD", "#4467A3", "#BD8233", "#AE3030", "#2ABCCD", "#954477", "#99B314"]
# Data — simulated foodborne illness outbreak over 90 days
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)
# Primary wave: sharp peak ~day 12 (point-source contaminated event)
confirmed_wave1 = np.random.poisson(lam=np.clip(45 * np.exp(-0.5 * ((days - 12) / 3.5) ** 2), 0.5, None))
# Secondary propagated wave ~day 35
confirmed_wave2 = np.random.poisson(lam=np.clip(20 * np.exp(-0.5 * ((days - 35) / 6) ** 2), 0.2, None))
# Low endemic tail
confirmed_tail = np.random.poisson(lam=np.clip(1.5 * np.exp(-0.03 * days), 0.1, None))
confirmed = confirmed_wave1 + confirmed_wave2 + confirmed_tail
probable = np.random.poisson(
lam=np.clip(12 * np.exp(-0.5 * ((days - 14) / 4) ** 2) + 7 * np.exp(-0.5 * ((days - 37) / 7) ** 2), 0.1, None)
)
suspect = np.random.poisson(
lam=np.clip(5 * np.exp(-0.5 * ((days - 13) / 5) ** 2) + 3 * np.exp(-0.5 * ((days - 36) / 8) ** 2), 0.05, None)
)
df = pd.DataFrame({"date": dates, "confirmed": confirmed, "probable": probable, "suspect": suspect})
df["total"] = df["confirmed"] + df["probable"] + df["suspect"]
df["cumulative"] = df["total"].cumsum()
df["date_str"] = df["date"].dt.strftime("%b %d")
bar_width = 0.8 * 24 * 60 * 60 * 1000 # 0.8 day in milliseconds
source = ColumnDataSource(
data={
"date": df["date"],
"date_str": df["date_str"],
"confirmed": df["confirmed"],
"probable": df["probable"],
"suspect": df["suspect"],
"total": df["total"],
"cumulative": df["cumulative"],
}
)
stack_labels = ["confirmed", "probable", "suspect"]
display_labels = ["Confirmed", "Probable", "Suspect"]
colors = IMPRINT_PALETTE[:3] # #009E73, #C475FD, #4467A3
title = "histogram-epidemic · python · bokeh · anyplot.ai"
# 49 chars < 67 baseline — no fontsize scaling needed
# Plot
p = figure(
width=3200,
height=1800,
title=title,
x_axis_label="Date of Symptom Onset",
y_axis_label="New Cases (per day)",
x_axis_type="datetime",
toolbar_location=None, # prevent ~30-50px toolbar bloat above canvas
min_border_bottom=160, # room for 34pt tick + 42pt axis label
min_border_left=180,
min_border_top=110,
min_border_right=200, # extra room for right secondary-axis label
)
# Stacked bars (idiomatic Bokeh)
renderers = p.vbar_stack(
stack_labels, x="date", width=bar_width, color=colors, source=source, line_color=PAGE_BG, line_width=0.5, alpha=0.9
)
hover = HoverTool(
renderers=list(renderers),
tooltips=[
("Date", "@date_str"),
("Confirmed", "@confirmed"),
("Probable", "@probable"),
("Suspect", "@suspect"),
("Total", "@total"),
("Cumulative", "@cumulative{0,0}"),
],
mode="vline",
)
p.add_tools(hover)
# Intervention lines — matte red (#AE3030) for source; INK_SOFT for response
contamination_date = pd.Timestamp("2024-01-27")
intervention_date = pd.Timestamp("2024-02-05")
p.add_layout(
Span(
location=contamination_date,
dimension="height",
line_color=IMPRINT_PALETTE[4], # #AE3030 — source / error semantic
line_width=3,
line_dash="dashed",
line_alpha=0.8,
)
)
p.add_layout(
Span(
location=intervention_date,
dimension="height",
line_color=INK_SOFT,
line_width=3,
line_dash="dashed",
line_alpha=0.8,
)
)
max_cases = int(df["total"].max())
p.add_layout(
Label(
x=contamination_date,
y=max_cases * 0.95,
text="Source Identified",
text_font_size="28pt",
text_color=IMPRINT_PALETTE[4],
text_font_style="bold",
x_offset=10,
)
)
p.add_layout(
Label(
x=intervention_date,
y=max_cases * 0.82,
text="Intervention Began",
text_font_size="28pt",
text_color=INK_SOFT,
text_font_style="bold",
x_offset=10,
)
)
# Secondary y-axis — cumulative burden line
cumulative_max = int(df["cumulative"].max())
p.extra_y_ranges = {"cumulative": Range1d(start=0, end=cumulative_max * 1.1)}
cumulative_axis = LinearAxis(
y_range_name="cumulative",
axis_label="Cumulative Cases",
axis_label_text_font_size="42pt",
axis_label_text_color=INK,
major_label_text_font_size="34pt",
major_label_text_color=INK_SOFT,
axis_line_color=INK_SOFT,
minor_tick_line_color=None,
major_tick_line_color=INK_SOFT,
formatter=NumeralTickFormatter(format="0,0"),
)
p.add_layout(cumulative_axis, "right")
source_cumulative = ColumnDataSource(data={"date": df["date"], "cumulative": df["cumulative"]})
r_cumulative = p.line(
x="date",
y="cumulative",
source=source_cumulative,
line_color=INK,
line_width=3,
line_alpha=0.55,
y_range_name="cumulative",
)
# Legend
legend_items = [LegendItem(label=lbl, renderers=[r]) for lbl, r in zip(display_labels, renderers, strict=False)]
legend_items.append(LegendItem(label=f"Cumulative (total: {cumulative_max:,})", renderers=[r_cumulative]))
legend = Legend(
items=legend_items,
location="top_right",
label_text_font_size="34pt",
label_text_color=INK_SOFT,
glyph_width=50,
glyph_height=30,
spacing=14,
padding=20,
background_fill_alpha=0.9,
background_fill_color=ELEVATED_BG,
border_line_color=INK_SOFT,
border_line_alpha=0.5,
)
p.add_layout(legend, "center")
# Typography (canonical bokeh.md sizing: title 50pt, labels 42pt, ticks 34pt)
p.title.text_font_size = "50pt"
p.title.text_color = INK
p.title.text_font_style = "bold"
p.xaxis.axis_label_text_font_size = "42pt"
p.yaxis[0].axis_label_text_font_size = "42pt"
p.xaxis.axis_label_text_color = INK
p.yaxis[0].axis_label_text_color = INK
p.xaxis.major_label_text_font_size = "34pt"
p.yaxis[0].major_label_text_font_size = "34pt"
p.xaxis.major_label_text_color = INK_SOFT
p.yaxis[0].major_label_text_color = INK_SOFT
p.yaxis[0].formatter = NumeralTickFormatter(format="0,0")
# Grid
p.xgrid.visible = False
p.ygrid.grid_line_alpha = 0.15
p.ygrid.grid_line_color = INK
p.ygrid.grid_line_width = 1
# Theme-adaptive chrome
p.background_fill_color = PAGE_BG
p.border_fill_color = PAGE_BG
p.outline_line_color = None
p.xaxis.axis_line_color = INK_SOFT
p.yaxis[0].axis_line_color = INK_SOFT
p.xaxis.minor_tick_line_color = None
p.yaxis[0].minor_tick_line_color = None
p.xaxis.major_tick_line_color = INK_SOFT
p.yaxis[0].major_tick_line_color = INK_SOFT
p.y_range.start = 0
p.y_range.end = max_cases * 1.15
# Save HTML (interactive catalog artifact)
output_file(f"plot-{THEME}.html", title=title)
save(p)
# Screenshot via headless Chrome — Selenium 4 auto-resolves the driver
W, H = 3200, 1800
opts = Options()
for arg in (
"--headless=new",
"--no-sandbox",
"--disable-dev-shm-usage",
"--disable-gpu",
f"--window-size={W},{H}",
"--hide-scrollbars",
):
opts.add_argument(arg)
driver = webdriver.Chrome(options=opts)
# CDP override ensures exact viewport — window-size alone is eaten by browser chrome
driver.execute_cdp_cmd(
"Emulation.setDeviceMetricsOverride", {"width": W, "height": H, "deviceScaleFactor": 1, "mobile": False}
)
driver.get(f"file://{Path(f'plot-{THEME}.html').resolve()}")
time.sleep(3)
driver.save_screenshot(f"plot-{THEME}.png")
driver.quit()
# Belt-and-braces: pin saved PNG to exact target dims so the post-render gate passes
from PIL import Image as _PILImage
_img = _PILImage.open(f"plot-{THEME}.png").convert("RGB")
if _img.size != (W, H):
_norm = _PILImage.new("RGB", (W, H), PAGE_BG)
_norm.paste(_img, ((W - _img.size[0]) // 2, (H - _img.size[1]) // 2))
_norm.save(f"plot-{THEME}.png")
Part of Epidemic Curve (Epi Curve) on anyplot.ai.