creating and interpreting epidemic curves

Overview
An epidemic curve (or epi curve) is a histogram (bar chart) that shows the distribution of cases over time. The time intervals are displayed on the x axis (the horizontal axis), and case counts are displayed on the y axis (the vertical axis). The result is a visual representation of illness onset in cases associated with an outbreak. The epi curve is an essential tool in an outbreak investigation and a key feature of descriptive epidemiology. It can provide useful information on the size, pattern of spread, time trend, and exposure period of the outbreak, and is often included in the epidemiological (epi) summary. The epi curve should be continuously updated as the outbreak progresses.

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Basic epi curves
Whether creating an epidemic curve on paper or in a software program, these are important things to consider:

Plot the number of cases for each date/time on the graph. Include pre-outbreak time to show visually when the outbreak began. Since the epi curve is depicting a continuous variable, the bars should touch each other (unless there are periods of time with no cases).
Clearly label the x-axis (date/time of illness onset) and the y-axis (number of cases).
Give the epi curve a title that provides enough detail so that the figure can stand alone.
If depicting any cases other than confirmed cases (e.g., probable, suspect cases), these should be differentiated on the epi curve.
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Advanced epi curves
Epi curves can be a powerful way to tell a meaningful story about the outbreak, even without lengthy text to explain the figure. While the general structure of epi curves are similar, there are ways to use the outbreak data to enhance the message.

Using different scales for the x-axis
The unit of time on the x-axis is usually based on the incubation period of the disease and the length of time over which cases are distributed. For example, for a disease with a short incubation period (e.g., Bacilius cereus) and cases distributed over a short period of time (hours), the scale for the x-axis may be more meaningful by hour rather than by day. Several epi curves with different units on the x-axis can be drawn to determine which portrays the data best. This is particularly useful when the disease and/or incubation is unknown.

Dealing with missing onset dates
A case may have many dates associated with their illness: illness onset date, report date, hospital admission date, specimen collection date, laboratory testing date, etc. While the general practice is to use illness onset date for all cases, these may not be readily available – for example, when a case could not be reached for an interview. To address this issue, the earliest available date for the case should be used to approximate the case’s illness onset.

The epi curve should clearly indicate in the footnotes that a set of criteria to establish an estimate has been used. As new information becomes available (e.g., updated dates), the epi curve should be updated

1. Lecture (6):
Creating and Interpreting
Epidemic Curves
Aida Mohey
Professor, University of Alexandria
Head of Community Medicine department
[email protected]

2. Learning Objectives
1.Understand the purposeof Epidemic Curves
2.Collectand OrganizeOutbreak Data
3.InterpretKey Features of Epidemic Curves
4.Analyze Control Measures and Outcomes

3. Part 1: Introduction to Epidemic Curves

4. 1. Definition and Purpose of an Epidemic Curve
What is an Epidemic Curve?
▪An epidemic curve (often called
an "epi curve") is a visual
representation of the number of
cases of a disease over time
during an outbreak.
▪Typically displayed as a
histogram
▪It plots time on the x-axis (e.g.,
days or weeks) and number of
cases on the y-axis.

5. Why is it Important?
1.Identifies the Pattern of Spread: The shape of the epidemic curve helps
reveal the mode of transmission, whether it’s a point source (a single
exposure), continuous common source (ongoing exposure), or propagated
spread (person-to-person). Each of these patterns produces a different
curve shape, aiding in pinpointing the type of outbreak.
2.Estimating Key Dates: By observing the curve’s peak, epidemiologists can
estimate when exposure (estimate the probable period of exposure) likely
happened and when the outbreak might end.
3.Evaluating Control Measures: By monitoring changes in the curve after
implementing control measures, such as quarantine or improved
sanitation, public health teams can measure the effectiveness of these
interventions.

6. 2. Curve Types and Interpretations
▪Each type of epidemic curve provides clues about
how an outbreak is spreading.
▪This helps public health professionals to
implement targeted control measures.

7. A. Point Source Epidemic Curve
•Description: A point source curve has a single, sharp
peak, where cases rise quickly, peak sharply, and then
drop. This shape indicates that all cases were likely
exposed to the pathogen at roughly the same time.
•Example: A community dinner is held where a
contaminated dish is served. All individuals exposed to
the pathogen at this event may develop symptoms
within a short time frame, causing a rapid increase and
then a decrease in cases.
•Interpretation: The single peak suggests that the
outbreak’s source is a one-time event. This could be
due to a contaminated food item or water source
during a specific occasion.

9. B. Continuous Source Epidemic Curve
•Description: A continuous source curve appears as a
plateau rather than a sharp peak. Cases continue at a
steady rate over a period of time and then gradually
taper off.
•Example: A contaminated water supply that people
drink from regularly. As long as people are exposed to
the contaminated water, new cases will continue to
emerge.
•Interpretation: This pattern suggests ongoing
exposure to the pathogen, which is often linked to an
environmental source like contaminated water or
food. Cases appear consistently over a period because
people are continually exposed.

10. C. Propagated (Person-to-Person) Epidemic Curve
•Description: A propagated curve has multiple peaks, with
each peak often larger than the last. This pattern occurs
when the infection spreads from person to person, creating
secondary, tertiary, and further waves of infection.
•Example: Consider an outbreak of influenza. As people
interact, those infected can pass the virus to others. This
leads to new cases that cause multiple peaks as the infection
moves through the population.
•Interpretation: Multiple peaks in the curve indicate person-
to-person transmission. The successive waves of cases
suggest that each infected person is transmitting the disease
to new individuals, causing the outbreak to expand over
time.

11. Curve Type Shape &
Pattern
Interpretation &
Example
Point Source Single, sharp
peak
One-time exposure
event, like a
contaminated meal
Continuous Plateaued curveOngoing exposure, like a
contaminated water
supply
Propagated Multiple peaks
with increasing
cases
Person-to-person
spread, as in flu
outbreaks

12. Part 2: Data Collection and
Preparation

13. Case Scenario
•We’re investigating a gastroenteritis outbreak in a community,
where cases have been tracked over five days.
•This type of outbreak, often caused by contaminated food or
water, results in a high number of cases developing symptoms
around the same time, making it ideal for plotting an epidemic
curve.
•We’ll use a small dataset showing the onset of symptoms and
number of cases to demonstrate how to create an epidemic
curve.

14. 2. Data Example for Collection
This dataset represents the date on which people began showing
symptoms (symptom onset) and the number of cases reported on each
date. We’ll use this data to create an epidemic curve that will help us
understand the outbreak pattern.
•April 1: 2 cases
•April 2: 5 cases
•April 3: 8 cases
•April 4: 3 cases
•April 5: 2 cases

15. Excel setup
Date
Number of
Cases
April 1 2
April 2 5
April 3 8
April 4 3
April 5 2
Step-by-Step Instructions:
1.Open Excel:
Open a new Excel worksheet to start entering the data.
2.Create Column Headers:
1.In cell A1, type "Date" to label the date of symptom
onset.
2.In cell B1, type "Number of Cases" to label the
number of cases reported on each date.
3.Enter the Data:
Below the headers, input each date and its
corresponding number of cases, so your data table
should look like this:
4.Double-Check Data Entry:
Ensure all dates and case numbers are correct. This
accuracy is essential for creating a reliable epidemic
curve.

16. Part 3: Creating the Epidemic Curve in Excel
This section provides a step-by-step guide to creating an
epidemic curve in Excel using the data we prepared in the
previous part.
Objective: By the end of this section, participants will have
created a histogram that visually represents the outbreak
pattern over time, allowing them to interpret the data more
effectively.

17. Data Input in Excel:
Date Number of Cases
April 1 2
April 2 5
April 3 8
April 4 3
April 5 2
Before creating the chart, let’s ensure the data is correctly entered:
•Column A (Date): Enter the dates of symptom onset.
•Column B (Cases): Enter the number of cases for each date.
The table in Excel should look like this:

18. 2. Creating a Histogram (Column Chart)
Follow these steps to transform the data into an epidemic curve:
1.Select the Data Range:
oHighlight the data in Column A (Dates) and Column B (Cases), including the headers.
2.Insert the Chart:
oGo to the Insert tab at the top of Excel.
oIn the Charts group, select Column Chart. Choose a Clustered Column or 2-D Column option,
which will create a basic bar chart that we can adjust to resemble an epidemic curve.
3.Format the Chart:
oSet Dates as the X-Axis Labels: If Excel doesn’t automatically use the dates as x-axis labels,
right-click on the x-axis, select Select Data, and choose the dates for the horizontal axis.
oNumber of Cases on the Y-Axis: Ensure the y-axis represents the number of cases accurately.
oChart Title: Click on the chart title and rename it to “Epidemic Curve of Gastroenteritis
Outbreak” to clearly identify the chart.

19. 4. Presenting the Curve
•With the histogram
complete, you now
have a clear visual
representation of the
outbreak.
•The epidemic curve
should show the
number of cases
peaking on April 3 and
tapering down
afterward.
•This pattern is typical of
a point source
outbreak, where cases
increase rapidly and
then decline as the
exposure source is
limited or removed.
0
1
2
3
4
5
6
7
8
Epi Curve of gastroenteritis outbreak
1-Apr2-Apr3-Apr4-Apr5-Apr
Date
Number of cases

20. Part 4: Interpreting the Epidemic
Curve

21. •Question 1: Curve Shape Analysis
•Question: “What does the shape of the curve indicate about
the source of the outbreak?”
•Expected Answer: The curve has a single peak around April
3, which is typical of a point source outbreak. This shape
suggests that the cases were likely due to a single exposure
event, such as a contaminated meal or water source that
exposed multiple people at the same time.
2. Discussion Questions and Expected Answers

22. •Question 2: Exposure Timing
•Question: “Based on the peak date, when do you think
exposure most likely happened, provided that IP 1-3 days?”
•Expected Answer: Since the peak occurs on April 3, and
gastroenteritis typically has a short incubation period (1-3
days), exposure likely happened between in April 2 (from
peak of the curve back count for the average IP)

24. •Question 3: Evaluating Control Measures
•Question: “If control measures were introduced on April 3,
what does the decrease in cases on April 4 and April 5
suggest?”
•Expected Answer: The decline in cases after April 3 suggests
that the control measures might be effective in reducing
new infections. Fewer cases in the following days could
indicate that the source of exposure has been removed or
that people are taking precautions.

25. Summary of Key Takeaways
•Single Peak: A point source outbreak, likely due to a single
exposure event.
•Likely Exposure Date/time: Estimated as April 2, based on
the average incubation period and peak on April 3.
•Impact of Control Measures: The decrease in cases suggests
that hygiene measures introduced on April 3 are likely
helping to contain the outbreak.