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Challenge 3: COVID-19 in Connecticut

Learning Targets:

  • Analyze and interpret epidemiological data.
  • Create graphical representations of data.


The State of Connecticut Department of Public Health keeps detailed records of the COVID-19 outbreak in the state. All data and statistics are publicly accessible and the numbers are broken down by location (town or county), age, race and ethnicity. You now have the opportunity to find the raw data and to represent the data graphically so that it is easily compared. This data is useful for biostatisticians and for other public health officials who advise the government about the local outbreak.

Access and Graph Data

Use the CT Dept of Public Health webpage and navigate to the page including all of the raw data. Once within a particular data set, you can opt to “Configure Visualization” in the top right corner of the data set. This will bring you to a platform where you can select a graph type and the variables you would like to illustrate on the graph. Use this platform to construct graphs as described below. You can: use the graph visualization feature on the CT portal website, download the raw numbers to a spreadsheet, or you can make the graphs by hand.

When constructing a graph, remember to:

  • Include axes or data labels with units (for example, time can be expressed in days or months and cases can be expressed as people or/100k population)
  • Give your graph a title that tells the reader what the graph is representing

Construct 2 pie charts:

    • Illustrating the confirmed cases in CT broken down by race and ethnicity.
    • Showing the number of deaths in CT broken down by race and ethnicity.

Construct 2 bar charts:

    • Showing the number of deaths in CT broken down by race and ethnicity.
    • Indicating the total number of deaths broken down by age group.

Reflect & Discuss



  • Does the Connecticut case data indicate that there are major differences in the number of cases when looking at different races and ethnicities?

Which race and ethnicity has the most cases in CT, which has the least? Is this consistent with the demographics of the State of Connecticut or are certain races or ethnicities affected disproportionately?

  • What does your graph indicate about age and the likelihood of contracting or dying from COVID-19?

How does age affect the recovery from COVID-19?

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