Epidemiology and Disease Data
- Junessa Masaya
- Apr 17
- 5 min read
HSC Biology | Free Study Notes
In this lesson
what incidence means
what prevalence means
how to interpret maps and graphs in infectious disease
how to identify trends, patterns and anomalies
how to evaluate disease studies and data sources
What is epidemiology?
Epidemiology is the study of disease patterns in populations.
It looks at questions such as:
how common a disease is
where it occurs
how it spreads
which groups are most affected
how control strategies change disease patterns over time
In Module 7, epidemiology helps students understand infectious disease at the population level, not just in one patient.
Incidence
Incidence is the number of new cases of a disease in a population over a given time period.
What this means
Incidence tells you:
how quickly new cases are appearing
how strongly transmission is currently occurring
Why incidence matters
A high incidence suggests:
many new infections are occurring
disease transmission may be active
control measures may not be working well enough
Prevalence
Prevalence is the total number or proportion of people in a population who have a disease at a particular time.
What this means
Prevalence tells you:
how widespread the disease is
how much disease is currently present in the population
Why prevalence matters
Prevalence can remain high even if incidence falls, especially if:
the disease lasts a long time
recovery is slow
many people are already living with the disease
Key difference from incidence
Incidence = new cases
Prevalence = all existing cases at a point in time or over a period
The Module 7 syllabus names both incidence and prevalence together, so students need to keep them clearly separate.
Incidence and prevalence compared
Term | What it measures | Key question |
Incidence | New cases over time | How many new infections are occurring? |
Prevalence | Total existing cases | How widespread is the disease right now? |
Interpreting maps
Disease maps are used to show how disease varies between regions.
What maps can show
Maps may show:
higher and lower incidence in different countries or regions
links between disease and climate
links between disease and vector distribution
links between disease and economic conditions
HSC-style example
The Module 7 sample materials use a map of malaria incidence and explain that higher incidence is linked to:
tropical and subtropical climate
the mosquito vector
the economic status of developing nations and their ability to provide eradication programs, housing and health care
How to interpret a disease map
When looking at a map, ask:
where is incidence highest?
where is it lowest?
what environmental or social factors may explain this pattern?
does the pattern match vector distribution, climate or healthcare access?
Interpreting graphs
Graphs are used to show changes in disease over time or relationships between variables.
What graphs can show
Disease graphs may show:
rising or falling incidence
changes after vaccination programs
changes in prevalence
relationships between immunisation and disease cases
HSC-style example
The Module 7 sample material shows that as measles vaccine coverage increased from 17% in 1980 to 85% in 2015, measles cases per million fell from 944.6 to 28.5. This is explained by vaccine effectiveness and herd immunity.
Important graph-reading point
The same material warns that time points may not be equally spaced, so students need to read the axis carefully before describing trends.
Trends
A trend is the general direction of change in the data.
Examples of trends
incidence increases over time
incidence decreases after a public health program
prevalence is highest in certain regions
Good exam style
Instead of saying:
“The graph goes down”
write:
“As vaccine coverage increased, the number of measles cases per million decreased.”
Patterns
A pattern is a repeated relationship or noticeable arrangement in the data.
Examples of patterns
tropical countries show higher malaria incidence
areas with vectors show more cases
higher immunisation levels are linked to fewer cases
Why patterns matter
Patterns help students explain disease using:
transmission
climate
immunity
public health measures
Anomalies
An anomaly is a result or data point that does not fit the general trend.
Why anomalies matter
Anomalies may be caused by:
unusual local conditions
data collection issues
small sample size
reporting differences
random variation
Important point
Do not ignore an anomaly. Mention it, then explain that it does not match the overall pattern.
Drawing conclusions from disease data
A conclusion should answer the question using evidence from the data.
A strong conclusion should
refer directly to the graph or map
identify the trend or pattern
explain the biological reason where possible
avoid going beyond what the data show
Example
A good conclusion might be:
“Higher malaria incidence in tropical regions is linked to conditions that support the mosquito vector, and variation between countries may also reflect differences in health care and eradication programs.”
Evaluating studies
Module 7 also expects students to do more than just describe data. They need to judge how useful and trustworthy the study is.
Things to evaluate
When evaluating a disease study or data source, consider:
sample size, was it large enough?
time period, was it long enough to show a real pattern?
validity, did the study actually measure the disease pattern properly?
reliability, were the data collected consistently?
bias, could reporting differences affect results?
confounding factors, were there other reasons for the trend?
Example of confounding factors
If incidence differs between countries, this might reflect:
climate
vector presence
health care access
reporting quality
vaccination coverage
Not just one factor alone.
Working with maps and graphs in exams
A good exam approach
Read the title carefully.
Check the axis labels, units and key.
Identify the main trend or regional pattern.
Use actual data where possible.
Explain the pattern using biology.
Avoid copying unrelated facts that are not shown by the data.
The Module 7 sample marking guidelines specifically reward students who account for patterns by explaining them, not just describing what is visible.
Worked example
Exam-style question
Explain how epidemiological data can show that vaccination reduces infectious disease spread.
Worked answer
Epidemiological data can show that vaccination reduces infectious disease spread by comparing vaccine coverage with disease incidence over time. For example, as measles vaccine coverage increased, the number of measles cases per million fell. This suggests vaccination reduced transmission, and higher coverage also helped protect the population through herd immunity.
Why this works
This answer:
uses incidence correctly
identifies the trend
links the pattern to vaccination and herd immunity
stays based on the data
Common mistakes
Mixing up incidence and prevalence.
Describing a map or graph without explaining the biological reason for the pattern.
Ignoring axis labels or units.
Assuming one factor explains everything without considering other variables.
Writing general disease facts that do not refer to the data shown.
Confusing a trend with a single data point.
Quick quiz
What is incidence?
What is prevalence?
Why are disease maps useful in epidemiology?
What is an anomaly in a graph?
What should you check when evaluating a disease study?
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