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# Descriptive Epidemiology

### Patterns and Trends

A graph showing probability of having an avian flu outbreak versus quarantine days. Epidemiologists present such graphs to politicians, so they can make an informed decision on quarantine policies.

Using descriptive epidemiology may reveal patterns or general trends in the preliminary data. Epidemiologists can look at these trends, and come up with hypotheses explaining why or how the disease occurred (which can be proved using analytical epidemiology), and ways to stop the epidemic.

What kinds of patterns would epidemiologists find in data? There can be something found in all three types of descriptive data (who, where, and when).

In a hypothetical flu outbreak, data that answers the question “who has the flu?” would show what type of people got the flu. If certain groups of people seem more likely to get the flu compared to other groups (according to the data), this may indicate something important. Epidemiologists often calculate the “attack rate” for different groups of people. The attack rate is a ratio between infected and non-infected people in a certain group. A higher attack rate shows that a higher percentage of people in this group were infected (attack rates between groups can also be compared). Suppose in our imaginary outbreak, the flu most frequently occurred in people ages 8-15. This shows that the outbreak has something to do with children.

Data that answers the question “where do the people with the flu live?” could be plotted on a map. In general, epidemiologists look for certain areas where there are an unusually high number of infected people, and plotting cases as dots on a map is the easiest way to see this kind of trend. Suppose in our imaginary epidemic, cases occurred more frequently in the east side of a city than in the west side. This could indicate that the source of the epidemic is located or has something to do with the east side of the city.

Data that answers the question “when did people catch the flu?” can be plotted on a graph called an epidemiological curve. This graph shows how many people were infected, and the time at which they became sick. These graphs have a characteristic shape- they start low, rise up over time (as the epidemic affects more people), and eventually fall (as the epidemic affects less people). Suppose an epidemiological curve was made for our imaginary flu epidemic. We could look at the very beginning of the epidemic curve, when the very first child got sick. This child could be the source of the whole outbreak, and we could find out more about him or her to see how the epidemic was caused.

We can put all these bits of information together to create a more logical story of how the epidemic occurred. Since the flu affected young people in mostly the east part of a city, a possible hypothesis explaining what happened could be that perhaps the child who was the first one to get sick brought the flu to school. It spread at the school where many residents of the east side of town attended.

Sources:
“Introduction to Basic Epidemiology and Principles of Statistics for Tropical Diseases Control”. World Health Organization. 2002.

Aragón, Tomás, Wayne Enanoria, and Arthur Reingold. Essential Field Epidemiology. Center for Infectious Disease Preparedness, UC Berkeley School of Public Health. 2006. .

"Steps of an Outbreak Investigation." Centers for Disease Control and Prevention. .

Okasha, Mona. "Commentary: Mona Okasha Takes You Through the Paper and Explains What It Means." Student BMJ (March 2003). .