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Target Grades: 7-12
Time Required: 30-60 minutes
Time Required: 30-60 minutes
Subject Areas: Mathematics, Engineering and Technology
Activity Type: Data Analysis and Visualization
Standards: NGSS HS-LS2-2, CCSS.ELA-Literacy.RST.9-10.7, CCSS.ELA-Literacy.9-10.3
Standards: NGSS HS-LS2-2, CCSS.ELA-Literacy.RST.9-10.7, CCSS.ELA-Literacy.9-10.3
Mapping is a powerful tool for understanding the spread of diseases that are transmitted by invertebrates such as mosquitoes and ticks, called vectors. Epidemiologists, the scientists who study disease transmission and distribution, will often use colors or shapes to represent the number of disease cases on maps. By using maps to visualize how the number and distribution of infections change over time, epidemiologists can look for patterns in new infections that might provide clues about the role of vectors, weather, or human behavior in the spread of disease. For an example, take a look at the following heat map of Lyme disease from 2003 to 2011, and then click the "play" button:
As you look at this map you might notice that, from 2003-2011, most cases of Lyme disease in the U.S. occurred in the Northeast and upper-Midwest. Largely through studying maps like this, researchers were able to identify the common deer tick, Ixodes scapularis, as a possible vector in Lyme disease transmission.
If you’re interested in making your own epidemiological map, there is plenty of data to work with. When physicians identify human cases of vectored disease, they report them to the Centers for Disease Control (CDC). The CDC organizes that data by location and date and makes the information publically available.
For this activity, we’ve given you some tools to create your own map of a vectored disease:
- Ready-to-map CDC data and information on vectored diseases
- Links to two free online mapping tools with instructions
- A DIY map for mapping data by hand, plus instructions
- Some analysis questions to guide your observations
- SciFri interviews with experts on epidemiology and vectored disease
Vector: ticks in the genus Ixodes
Data: Reported cases of Lyme disease by state or locality, 2003-2012
Excel version: Lyme cases by state, 2003-2012.xlsx
CSV version: Lyme cases by state, 2003.csv
Excel version: Lyme cases by state, 2003-2012.xlsx
CSV version: Lyme cases by state, 2003.csv
West Nile Virus (WNV)
Vector: mosquitoes (see the full list of U.S. WNV mosquito vectors here)
Data: West Nile virus disease cases reported to CDC by state, 1999-2013
Excel version: WNV cases reported by state, 1999-2013.x
Excel version: WNV cases reported by state, 1999-2013.x
CSV version: WNV cases reported by state, 1999-2013.c
Eastern Equine Encephalitis Virus (EEEV)
CDC EEEV information
CDC EEEV information
Vector: mosquitoes in the genera Aedes, Coquillettidia, and Culex
Data: Confirmed and Probable Eastern Equine Encephalitis Cases, Human, U.S. 1964-2010
Excel version: EEEV cases from 2000–2010.xlsx
Excel version: EEEV cases from 2000–2010.xlsx
CSV version: EEEV cases from 2000–2010.csv
La Crosse Encephalitis Virus (LACV)
CDC LACV information
Vector: the Ochlerotatus triseriatus mosquito
CDC LACV information
Vector: the Ochlerotatus triseriatus mosquito
Excel version: LACV cases from 2000–2010.xlsx
CSV version: LACV cases from 2000–2010.csv
Pick another disease of interest
- Find data: Search the CDC site for a disease you’ve heard of, then look for a link to “statistics” for data about that disease.
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Download or copy those data into an Excel file, making sure that the top row contains only dates, and the left column includes either U.S. states or U.S. counties. So long as your data resemble the snapshot below, you can save your Excel file and start mapping.
- If you need to convert the file into a .csv file, start by removing all of the spaces from your state and county names. Next click “Save As,” and choose “Comma-Separated values (.csv)” from the drop-down menu; then click “ok.”
Open Heat Map –www.openheatmap.com
- Go to www.openheatmap.com and click “Create your map.”
- Select “Excel or CSV file,” then click “upload.”
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Select the dataset you chose, and click upload.*
*Note: You can use this with any data that are organized by state or county information in the left hand column. - Click “View your map.”
- Use the play button to visualize your data. You can also adjust the color settings, title, author, dates, and key to better represent your data. You will also probably have to adjust the data range to accurately reflect your data.
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Click “Save & view” in order to share or embed your map.
*eSpatial – www.espatial.com
*This map requires that you sign up for a free trial account.
*This map requires that you sign up for a free trial account.
- Upload the .csv file version of the data you would like to use, then click “complete.” You can ignore any error warnings—the data will still map correctly. Then click, "Return to Map."
- You can adjust the map to display different marker styles and colors based on your dataset by selecting “style & color” from the box in the upper left corner, and then choosing “style” and “color by value” from the box that pops up.
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You can choose which year’s data you would like to map by selecting the year from the drop-down menu in the “color by value” menu.
- Print out the U.S. Map and Key.
- Choose a single year (column) of data from one of the datasets above. This will be the data that you map.
- Choose the key whose highest value is closest to the highest value of any state or county in your data set. For example, if the highest number of cases of Lyme disease in any state for the year you are mapping is 300, choose the key whose range goes to 350, not the key that goes to 2000.
- Color each box in your key a different color. Label your key with the disease you are mapping and the year the data was collected.
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Using your key as a guide, color in each state based on the number of disease cases it had according to your data set. For example, if Texas had 25 cases of Lyme disease in 2003, color in Texas using the color from the key whose range includes that number. You can see an example of how to color in states in the image below:
Based on data of confirmed La Crosse encephalitis cases from 2000:
Minnesota 8 Colorado 1 North Carolina 6 Connecticut 1 Ohio 18 Georgia 2 Tennessee 19 Illinois 3 West Virginia 40 Indiana 2 Wisconsin 6 Iowa 4 all other states 0 Kentucky 2
- Based on your map, are there regions of the United States that are disease hotspots? Are there areas of the United States that seem to always have few or no cases each year?
- Compare the number of cases in states between years. Does the disease seem to be confined to certain parts of the U.S. even between years?
- How do the number of cases seem to be changing from year to year? Is there a pattern that suggests the disease is becoming more common, or that it is becoming less common over time?
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How might adding data from additional years change your conclusion from question 2 or 3 above?
- Are the regions with fewer cases similar in some way? Think about population size, altitude, proximity to the ocean, and latitude. How could these factors affect the spread of a disease transmitted by vectors such as mosquitoes or ticks?
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Compare your disease heat map to the map below of climate zones in the continental U.S. Are there any similarities? How could regional climate affect the distribution of a vectored disease? (Click on the map for an enlarged version.)
Standards
NGSS HS-LS2-2: Use mathematical representations to support and revise explanations based on evidence about factors affecting biodiversity and populations in ecosystems of different scales.
NGSS Science and Engineering Practices: Practice 4: Analyzing and interpreting data
CCSS.ELA-Literacy.RST.9-10.7: Translate quantitative or technical information expressed in words in a text into visual form (e.g., a table or chart) and translate information expressed visually or mathematically (e.g., in an equation) into words.
CCSS.ELA-Literacy.9-10.3: Follow precisely a complex multistep procedure when carrying out experiments, taking measurements, or performing technical tasks, attending to special cases or exceptions defined in the text.
Discussion