Tracking SARS-CoV-2 Spread from Wastewater

This project follows the Scientific Method. Review the steps before you begin.

In this experiment, you will look at how COVID-19 viral DNA levels in wastewater change over time, across the nation and within a select state. To do this, you’ll first pick a state of interest and use the coding instructions below to help you visualize COVID-19 viral DNA levels from wastewater data in multiple counties across the state. You’ll then be able to visually see how the COVID-19 pandemic changed over that time, and use quantitative measurements to reinforce the visualization. 

Setting Up the Google Colab Environment

1. You will need a Google account. If you do not have one, make one when prompted. 
2. Download the covid_wastewater.ipynb file from Science Buddies. This is the code you will need to process your data.
3. Download the wastewater data from the CDC website. This is data on COVID-19 wastewater testing results and the counties where they were collected. To do this, click Export and ensure the Export format is CSV. In your downloads, rename this file to wastewater.csv.
4. Download the uscounties.csv file. This data contains the names of US counties and their country codes. We will use this file to plot data on the US map.
5. Within your Google Drive, click on ‘MyDrive,’ then create a new folder and rename it covid_wastewater. In this new folder, upload the wastewater.csv, uscounties.csv, and covid_wastewater.ipynb files. 
6. Double-click on the covid_wastewater.ipynb file. This should automatically open in Google Colab. 
   1. Read the Troubleshooting Tips and How to Use This Notebook sections. Follow the instructions you find in that section.
   2. Run the block under Importing Libraries to ensure you have access to all the functions we will use for this project. 
      1. Tip: To run each code block, you can either press the play button to the left of each code block or click on the code block and then hold Shift + Enter on your keyboard. 

Loading the Data into a Pandas DataFrame and Preprocessing

1. (Code Block 1A) Run this code block to create a DataFrame, which is essentially a data table, to load and manipulate the data in the notebook. The data from your wastewater.csv file will populate a table below the code block. Run this code block. 
2. (Code Block 1B) This code block displays the number of data points available for each state. When choosing the state you want to focus on, consider picking one with more data. Run this code block.
3. (Code Block 1C) This code block shows the years of available data for each state. Note that some states do not have data for consecutive years. When selecting a state, it is best to choose one with more than three years of data and, ideally, a continuous sequence of years. For example, some states may have data for 2022, 2023, and 2025, but not 2024. Run this code block.
4. (Code Block 1D) This code block sorts states by the number of years of available data and then by the total number of data rows. States near the top of the list are typically better choices because they tend to have more complete data and more consistent reporting; however, this is not a strict requirement. Aim to select a state with at least three years of data and more than 5,000 rows.

Preprocessing

1. (Code Block 2A) First, we can filter the data by the state of interest. Under the #TODO comment, you can define the state you are interested in by the state abbreviation. For example, if you are interested in Pennsylvania, you can type in “pa”. Make sure both letters are lowercase. You can do a simple Google search to find your state abbreviation if you don't already know it.
   
   
   

   
   Figure 1. Example of the code block that filters the DataFrame by a given state. You can change the state by updating the state variable under the #TODO comment.
   
   
   
2. (Code Blocks 2B–2F) These code blocks get the data ready so we can display it on a map more easily. You do not need to understand the code in these code blocks. Run all of these code blocks. 

Displaying COVID-19 Levels on a Map

Here, you will use the DataFrame you just generated to create a map display to visually analyze trends. 

1. (Code Block 3A) This code block defines a function that displays COVID-19 PCR target concentrations from wastewater levels on a map. Run this code block.
2. (Code Block 3B) This code block defines a function that will display the average PCR target concentration nationwide and for the state given a year. Run this code block.
3. (Code Block 3C-3D) Under the #TODO comment, you can change the year to display maps for different years. Run this code block to view a map of PCR target concentrations for the state you selected earlier in Code Block 2A. You can also click on the circles on the map to view data for individual counties. Code Blocks 3C and 3D contain the same code, but they are included twice, so you can generate maps for two different years and compare how PCR concentrations may have changed over time.
   
   
   

   
   Figure 2. Example of what the map should look like. Bigger, darker circles indicate higher concentrations of the PCR target.
   
   
   
   1. If you see an error, it is most likely because there is no data for the year you entered. For example, if your selected state only has data from 2022 to 2026 and you enter 2021, the code will return an error.
4. (Code Block 3E-3F) Under the #TODO comment, update the year and state abbreviation to print the yearly average COVID-19 PCR target concentration. The output will show both the selected state’s concentration and the national concentration. Code Blocks 3E and 3F contain the same code and are provided twice so you can easily compare concentration levels between two different years.
   1. If you encounter an error, it is most likely because no data is available for the year you entered. Also, check that the state abbreviation is correct.
5. In a notebook, document, or spreadsheet, create a table like the one below. Using Code Blocks 3E-3F, fill out this table. This will help you compare your state's data to national data. 
   1. How does the PCR concentration of your given state compare to the nation? Is it higher or lower?
   2. Can you think of any reasons why certain states/communities may be more impacted by COVID-19 than others?

Displaying County Data

Here, you will use the DataFrame you generated to determine the PCR concentration in select counties over time.

1. (Code Block 4A) This code block displays the counties in the state you selected, the years for which each county has data, and the county_fips code. FIPS stands for Federal Information Processing Standards and uniquely identifies each county. Run this code block.
2. (Code Block 4B) This code block defines a helper function that will print out the average PCR concentration for each year, given the county_fips code. Run this code block. 
3. (Code Block 4C) This code block will use the helper function defined in Code Block 4B. Under the #TODO comment, enter the county FIPS code for the county you want to see the yearly average PCR concentration for.
4. Create another table like the one below. Select 5 counties you are interested in. Remember that you can see the list of counties for your selected state under Code Block 4A, and the yearly average PCR concentration for any county you’re interested in under Code Block 4C. Fill out this table.
   1. How does the PCR concentration differ from county to county?
   2. Does the county with the most PCR concentration differ from year to year?
      
      Swipe left to see more

      Table 2. County overview of COVID-19 PCR concentrations over time. 

      Year	County 1	County 2	County 3	County 4	County 5	Average
      2022
      2023
      2024
      2025
      2026

5. (Optional) (Code Block 4D) This code block displays a histogram of PCR target concentration over time. Under the #TODO comment, enter the county FIPS code for the county you want to examine.
   1. After viewing the histogram, look for a trend. Does the PCR target concentration appear to be increasing, decreasing, or staying about the same over time?

Review the data and develop conclusions based on the information.

1. How did the spread of SARS-CoV-2 change in wastewater in the state you chose year-over-year? 
2. What year(s) did the concentration of SARS-CoV-2 in wastewater drastically change in the state?
3. When was the peak of Sar-CoV-2 spreading in this date range? This can be considered the height of the pandemic. When was the lowest SARS-CoV-2 spread? This can be considered the endemic phase of the virus. 
   1. Take it a step further and cross-reference this information with when infection rates were highest or at the height of the pandemic. This information can be found online, as it was publicly announced by the CDC. 
   2. Investigate further when the SARS-CoV-2 virus was announced as endemic by the CDC. Does this align with the wastewater data, and what’s expected in an endemic virus (e.g. cold, flu)?
4. How did the concentration of SARS-CoV-2 change in wastewater in each county you chose year-over-year? When you compare two counties, which had higher levels over time, or did they stagger as if the virus was being spread from one area to the next?
5. What year did the concentration of SARS-CoV-2 in wastewater drastically change (increase or decrease) in the county or each county? 
   1. Take it a step further, and investigate if there is a large population difference between the counties you compared. Why would this matter based on what you know about how SARS-CoV-2 is predominantly transmitted (refer to the introduction)?
6. How did the local or regional wastewater data (in the county) compare to the state-level trends? Why do you think there are similarities or differences?