SARS-CoV-19

Communicating data effectively with data visualization: Part 39 (Heatmaps of COVID-19 deaths)

INTRODUCTION

I wanted to incorporate a heatmap that illustrated the death rates (per 100,000 population) across time in the United States. But I also wanted to show when the coronavirus pandemic 2019 (COVID-19) vaccine was introduced and how it impacted death rates. I thought that a heatmap would do a nice job of illustrating this.

The data visualization by Tynan DeBold and Dov Friedman from the Wall Street Journal has a great visualization on the impact of vaccines for various disease from the measles to smallpox on death rates (See figure below). This heatmap shows the number of measles cases per 100,000 population between 1920 and 2000. Each row represents a state or territory of the United States (U.S.). In this tutorial, we’ll create a similar heatmap for COVID-19 deaths.

Source: Tynan DeBold and Dov Friedman, Wall Street Journal (link)

I set out to create my own heat map with COVID-related death rates using data from the Centers for Disease Prevention and Protection (CDC). The CDC provides a dashboard to visualize the trends in death rates by states and U.S. territories (Compare Trends in COVID-19 Cases and Deaths in the US). However, the data was not compiled in an easy manner. You can only visualize 6 territories at a time. I was able to download all the data and compile this into a single file for this tutorial, which you can download here. Use the file with the *.xlsm extension, which supports macros.

TUTORIAL

Step 1. Download the Excel file with the data. Use the data from the “data” tab. Inspect the data. The columns represent the weekly death rate (7-day average number of deaths per 100,000 population). The rows represents the states and U.S. territories.

Step 2. Use the VBA macro. In a previous article, I explained how to create a heatmap with different gradient levels. We will use a modified version of the macro for this exercise.

This is the VBA macro that we’ll use (link). Don’t be intimidated by this. I’ll go over how to use this code

I start by determining the number of gradient levels for the heatmap. The average death rate was 0.35 per 100,000 population, so I generated 20 levels of gradient (0 to 0.999, 1.0 to 1.999, 2.0 to 2.999, etc). I wanted a “blue” shade for this heatmap, so I had to figure out the RGB scheme for each level. I identified the RGB color scheme using a gradient generator by ColorDesigner. RGB code uses three values to represents the main color on the spectrum (red, green, blue).

Once you have the RGB codes for the gradient levels, you can edit the VBA macro.

In the Developer tab, click on “Visual Basic.” Make sure that the Developer tab is viewable on the Ribbon. If it is not, then you can activate this by going to the File > Options > Customize Ribbon and activate it by entering a check by the Ribbon box.

The Visual Basic interface is a separate window that pops up.

In the “Sub ChangeCellColor()” macro, we’re going to include 20 gradient levels. It’s important to make sure the Range() includes the data that we’re interested in modifying. Since the first cell is in A1 and the last cell is in CZ61, the range is Range("A1:CZ61").

Then we include the 20 gradient levels by changing the Case statement with the corresponding RGB codes. As you modify each Case statement, make sure to change the value ranges for each statement. For example, if you want to apply the RGB code for (18, 123, 141), the death rate range is 1.8 to 1.8999999. You can do this for all the gradient levels.

Here is an example:

    Case 1.8 To 1.8999999
         oCell.Interior.Color = RGB(18, 23, 141)
         oCell.Font.Bold = True
         oCell.Font.Color = RGB(18, 23, 141)
         oCell.Font.Name = "Times New Roman"
         oCell.HorizontalAlignment = xlCenter

Case 1.8 to 1.8999999 denotes the range of the values in each cell (7-day average deaths per 100,000 population).

oCell.Interior.Color = RGB(18, 23, 141) denotes the RGB color scheme for our gradient

oCell.Font.Bold = True denotes that the font is bolded

oCell.Font.Color = RGB(18, 23, 141) denotes that the font color matches the cell color

oCell.Font.Name = "Times New Roman" denotes that the font is Times New Roman

oCell.HorizontalAlignment = xlCenter denotes that the value is aligned in the center

After you’ve adjusted your code, you can execute the macro. To execute the macro, go to the Ribbon and select “Macros.” The Macro window will appear with three macros. Select the “ChangeCellColor” macro and click “Run.” This should execute the macro, and you will notice that the data will start to change color to the corresponding gradient values.

To sort by the last column, select the “SortColumn” macro and click “Run.”

To create white borders around the cells, select on the “WhiteOutlineCells” macro and click “Run.”

Step 4. Final touches. You can select the columns and change width to 2.

Once you have the correct cell sizes, you can start to add labels to the file. I included a line to delineate when the first vaccine was introduced and a line for when the president announced that COVID-19 was a national emergency. I also added labels to the bottom part of the table to indicate dates along the timeline. The rows represented the states and U.S. territories.

CONCLUSIONS

The number of deaths was high early in the pandemic in a few select places in the U.S. As the vaccine is introduced, the number of deaths reached a zenith around December 2020 before falling to low levels in February 2021. Then, the death rate started to increase around the beginning of July 2021. Based on the heatmap, the vaccine may have resulted in a decrease in deaths. But the death rate increased approximately 6 months later in what appears to be the beginning of a seasonal pattern. It is unclear whether the introduction of new variants causes the increased death rate, but there is speculation that it may be a contributor. This heatmap does not generate any claims to what is actually happening; it only provides a visual of the patterns that are reported across each U.S. state and territory.

REFERENCES

I took inspiration from the data visualization by Tynan DeBold and Dov Friedman from the Wall Street Journal.

Date for this exercise came from the CDC (link).  

A previous article on how to create heatmaps is available here.

I used the Gradient Generator by ColorDesigner to find out the RGB values for my gradient levels.

Communicating data effectively with data visualizations: Part 26 (COVID-19 choropleth)

INTRODUCTION

Power BI is a useful tool that allows you to build dashboards and data visuals. One ability that makes Power BI unique is its mapping feature. Although there are pre-built map functions in Power BI, better and easier applications are available through their AppSource store. This tutorial will go over how to create a choropleth of the most recent COVID-19 pandemic using the incidence rate (dated June 21, 2020) for the United States (US) using Power BI and the Drilldown Choropleth application.

 

DATA

You will need to download two types of data: (1) COVID-19 incidence rate for each county in the United States (You can download the Excel file from this Dropbox link) and (2) Shape files for the United States. Fortunately, there are many sources of shape files for the United States, and I have made one available using the Albers USA projection, which has the state of Alaska and Hawaii in the lower left corner of the map. You can use the link to the map shape file here. Data for the shape files comes from the US Census. [I downloaded the ZIP file and converted the SHP files to TopoJSON using the following conversion tool (Mapshaper.org).] The COVID-19 data contains US county codes called the Federal Information Processing Standard Publication (FIPS), a 5-digit code that identifies counties. Incidence rate is defined as the number of confirmed cases per 100,000 persons.

Albers US projection

POWER BI TUTORIAL

For this tutorial, you will need to have Power BI installed on your desktop to re-create this choropleth.

 

Step 1. Get the data

When you start Power BI, you will see a start screen that allows you to select the data you need. Since the COVID-19 incidence rates for the US data are saved as an Excel file, you will need to select Excel as the data type and then connect to the data.

After clicking “Connect,” the Navigator will open. Select Sheet 1 containing the data. Notice that there are two variables (FIPS1 and FIPS). These are the data that will be used to pair the incidence rate to the US county code. The FIPS code has a “0” in front of some of the county code (e.g., 05001) for a total of 5 digits. However, notice that these are in numeric format so the preceding “0” is missing. To remedy this, we need transform the data. Make sure to select “Transform Data” from the Navigator screen.

After selection “Transform,” you will need to change the FIPS variable from numeric to text.

You will be asked to either “Replace” or “Add New Step;” make sure that you select “Replace.” This will replace the numeric data to text, which is what we want for the FIPS variable. Check the data after you do this; notice that the “0” is now preceding values that are less than 5 digits. (FIPS code is a 5-digit county code.) After the data has been transformed, select “Close & Apply” to finalize the data. This will load the data into Power BI, which you will use to build the choropleth.

 

Step 2. Download and Install “Drilldown Choropleth” app

Power BI allows you to download apps from the AppSource store. Click on the “…” and select “Get more visuals.” This will open the AppSource store where you can type the name of the app you want.

Step 3. Enter data into the Drilldown Choropleth app

After you install the Drilldown Choropleth, an icon will appear in your Visualization panel. Click on this icon (Drilldown Choropleth); this will open up options in the Visualization panel. There are two important fields (“Location” and “Values”). Click and drag the FIPS variable into the “Location” field and the Incidence_Rate variable into the “Values” field.

Next, click on the Paint roll icon to open the Drilldown Choropleth options. Expand the “Shape” option to change the “Projection” to “albersUSA” and then enter the link to the TopoJSON shape file for the United States counties. The TopoJSON link is here: https://raw.githubusercontent.com/mbounthavong/Maps/master/c_03mr20.json

 

 

After entering the projection type and the TopoJSON link, you can make changes to the color of the choropleth. Since this is a choropleth, you will need to enter values for the Minimum, Center, and Maximum colors. This will generate a gradient where the darker colors reflect high incidence rates and the lighter colors reflect low incidence rates of COVID-19.

Finally, you can change the size of the visual and save it. Since there are a lot of data being processed, updating the choropleth will take a few minutes. With a little patience, you will generate a suitable choropleth of the COVID-19 incidence across US counties for June 21, 2020.

CONCLUSIONS

Power BI allows us to create visually spectacular choropleths. Additionally, it allows us to create choropleths that are interactive. You can hover over each shape and see the incidence rate per county. However, there are limitations. Inserting a legend to describe what the colors represent is not easily possible (I was unable to solve this problem). There are roundabout ways to get the legend inserted into this choropleth, but it will require additional software (as far as I know). There is potential for the Drilldown Choropleth app to improve by adding an easy way to include functionalities for legends and labels. Despite this limitation, Power BI allows us to create these choropleths without having to resort to more complex programming in R or Python.

Files used in this tutorial are available here.

REFERENCES

Mapshaper.org (link: https://mapshaper.org/)

US Census Cartographic files (link: https://www.census.gov/geographies/mapping-files/time-series/geo/carto-boundary-file.html)

COVID-19 data come from the Johns Hopkins GitHub site (link: https://github.com/CSSEGISandData/COVID-19/blob/master/csse_covid_19_data/csse_covid_19_daily_reports/06-21-2020.csv)

 

Communicating data effectively with data visualizations: Part 25 (Moving averages)

INTRODUCTION

As the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) continues around the world, data visualization experts have provided stunning visuals of the pandemic’s terrifying impact on public health. Several of these include a moving average metric that smooths the curves of the pandemic’s mortality data. Common moving averages presented by several of these visualizations include the 5-day average daily deaths and the 7-day average daily deaths. Information Is Beautiful presents mortality on a 7-day moving average while  International SOS presents the average daily deaths as 5-day moving averages.

Figure 1. 7-day moving average daily deaths by Information Is Beautiful (Last updated on 19 May 2020).

MOTIVATING EXAMPLE

In this article, we will replicate the 5-day and 7-day (moving) average daily deaths due to SARS-CoV-2 using data from the European Centre for Disease Control and Prevention (ECDC)1 or from the Our World in Data’s GitHub site.2 Due to the changing nature of SARS-CoV-2 data, this exercise will have used data that would ultimately be updated in the future. Please visit the ECDC or the Our World in Data site to download the most recent SARS-CoV-2 data.

We will download data from the Our World in Data’s GitHub site and then use Excel to recreate this plot for the United States (US).

Step 1. Download data from the OWID Covid-19 GitHub site

You can download the raw data for all the countries from the OWID’s site here. However, you can also download the cleaned data for this article (parsed for the United States) from my Dropbox folder here (I cleaned the data and prepared them for use in Excel).

The data has the following format:

Figure 2.png

We are interested in the new_deaths column, which is the number of new confirmed deaths by day. The moving average will estimate the average daily deaths across either 5-day or 7-day time periods.

Step 2. Add columns for the 5-day and 7-day moving average.

When you load the data into Excel, you will need to create two columns; one for the 5-day average daily death and another for the 7-day average daily deaths. I used MAD_5 for 5-day moving average daily deaths and MAD_7 for 7-day moving average daily deaths.

Figure 3.png

Step 3. Activate the Analysis ToolPak

Excel has a tool that will perform simple analysis; however, you may need to activate this if it is not on the ribbon.

Once the Options selection has been selected, you can navigate to the Add-in option and open the window to select the Analysis ToolPak. You only need the Analysis ToolPak for this exercise, but it is a good idea to explore the other tools available (e.g., Solver).

Step 4. Estimate the moving average

Once the Analysis ToolPak is activated, return to your main worksheet with the new data columns. Start by selecting the cell where you want the first moving average value to be calculated. Then select the Data Analysis add-in. This will open a window with different Analysis Tools. Select the Moving Average tool.

Once you click “Ok,” you will be asked for several elements to calculate the moving average. You will need to select the Input Range, the number of days needed for calculating the moving average, and the Output Range. After selecting the appropriate information, you can click “Ok” to generate the moving average. Do these steps for the 5-Day and 7-Day moving average daily deaths.

Step 5. Generate the line graph

Now that the average daily deaths are calculated for 5-day and 7-day moving averages, you can generate a line graph that resembles Figure 1.  

Insert a line graph onto the Excel worksheet. Right click on any empty area of the line graph; this will open up the edit options.

You can make edits to the figure like entering the data to create the line graph. The Series values is the set of numbers that you calculated for the 5-day average daily deaths.

Once you have selected the data for the Series values, you can edit the x-axis label. Currently, the x-axis defaults to a sequence of 1, 2, 3, …, n. You want this to reflect the dates which are on the B column. Click on Edit and select the dates on the B column to properly relabel the x-axis.

This will generate the first line graph with the 5-day average daily deaths visualization.

Step 6. Finalize the figure

After adding the 5-day average daily deaths, go ahead and repeat these steps for the 7-day average daily deaths. Using Excel’s format feature, you can make changes to the figure’s size, height, and colors. To replicate Figure 1, you will need to change the y-axis to a logarithmic scale.

After changing the scale on the y-axis and adding the 7-day average daily deaths, you should get a figure similar to the one below.

You can also present this without the logarithmic scale.

CONCLUSIONS

The moving average daily deaths smooths the line plots and provide a trend that is easy to understand and interpret. The major difference between these moving average plots and the mortality curves (see previous blog) is the ability to see the decline in daily deaths. The average daily deaths curve allows us to see the decline in the amount of deaths on a moving average. Mortality curves don’t do that since they are based on the total amount of deaths. Despite the differences in plots, both the moving average daily deaths and mortality curves provide critical visual information about the current SARS-CoV-2 trends.

You can download the data and complete exercise here.

REFERENCES

  1. European Centre for Disease Control and Prevention. Download today’s data on the geographic distribution of COVID-19 cases worldwide. European Centre for Disease Prevention and Control. Published April 18, 2020. Accessed May 19, 2020. https://www.ecdc.europa.eu/en/publications-data/download-todays-data-geographic-distribution-covid-19-cases-worldwide

  2. Our World in Data. GitHub: Owid/Covid-19-Data. Our World in Data; 2020. Accessed May 19, 2020. https://github.com/owid/covid-19-data

Communicating data effectively with data visualizations: Part 24 (Mortality Curves)

INTRODUCTION

The continual threat of infection by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has ground the world to a metaphysical stop. Economies appear to be under threat of a long recession, political debates have delayed needed relief to citizens, lack of N95 masks for healthcare workers place them at greater risk for doing their jobs, and mortality has increased world-wide. We are undoubtedly experiencing an seminal period in the 21st Century and data analysts have rushed to develop stunning visuals and dashboards such as the ones developed by Johns Hopkins University, “Our World in Data”,1 and the Centers for Disease Control and Prevention to feature the impact the SARS-CoV-2 is having on our world.

 

MOTIVATING EXAMPLE

In this article, we will replicate the total deaths due to SARS-CoV-2 using data from the European Centre for Disease Control and Prevention (ECDC)2 or from the Our World in Data’s GitHub site.3 Due to the changing nature of SARS-CoV-2 data, this exercise will have used data that would ultimately be updated in the future. Please visit the ECDC site to download the most recent SARS-CoV-2 data.

The figure we will replicate is one posted on the “Our World in Data” website and looks like the following:

Source: Our World in Data. Total confirmed COVID-19 deaths: How rapidly are they increasing? URL: https://ourworldindata.org/grapher/covid-confirmed-deaths-since-5th-death [last accessed: 17 April 2020].

We will download data from the ECDC and then use Excel to recreate this plot for several countries (we won’t create plots for all the countries, but you can feel free to do so by taking advantage of the available data).

Step 1. Download data from the ECDC.

You can download the raw data from the ECDC’s site here. Alternatively, you can also download the cleaned data for this article here (I cleaned the data and prepared them for use in Excel).

The data has the following format:

Figure 2.png

Each column represents a country and the rows represents the total number of deaths for each day after the 5th confirmed death.

 

Step 2. Select all of the data and insert a line chart.

Once all the data have been downloaded, select all of them. Insert a line chart and Excel will automatically generate a figure for you. This figure will need to be edited further, but Excel does a good job of plotting the total number of deaths along the X-axis (time).

Right-click in the chart region and click on the Select Data option. You want to de-select the “Days since the 5th total confirmed death” because this is not the value of interest. Rather, this represents the values for the X-axis (time).

Once you de-select the “Days since the 5th total confirmed death,” your line chart should look like the following figure.

Step 3. Changing the scale on the Y-axis.

In the figure from the Our World in Data, the values for total deaths are plotted using a log-scale. When Excel generates the line chart, it automatically uses the continuous scale on the Y-axis. To change this, you need to right-click on the Y-axis and then select Format Axis...

Once you make the changes to the Y-axis scale, the line chart should now look similar to the one from Our World in Data.

Step 4. Adding the axes labels and formatting the lines.

Once the line chart’s Y-axis has been transformed into a log-scale, you can make changes to the axes labels and the line formatting. Select the Design tab to made changes to the Y-axis label. Select the Add Chart Element to open the drop-down menu, then select the Axis Title followed by the Primary Vertical option. This will allow you to make changes to the Y-axis label.

Change the Y-axis label to read, “Log number of COVID-19 deaths.” Do the same thing for the X-axis label, but change it to read “Days since 5th death occurred.” Your figure should look like the following.

You can make the lines thinner by right-clicking on one of them (e.g., China), opening up the options. Select Format Data Series… and then adjusting the Width to be 1.5 points. This will make the line easier to see without having to take up space with the thicker lines.

Repeat this process for all the lines in the chart. Once you have completed that, the line chart should look like the following.

Step 5. Now all that’s left is changing some of the aesthetics.

The final line chart replicates the figure from Our World in Data and provides the references line for a doubling in the number of deaths for each country. The reference line (“Doubling every 5 days”) was creating using a base of 10 on a log-scale to replicate a doubling of that value every 5 days. The reference line was also placed on a secondary axis to create a continuous line (since creating a line on the same scale as the other countries would have yielded gaps because we’re doubling deaths every 5 days). In the Design tab, you can add the secondary horizontal axis to match that of the primary horizontal axis (time). The secondary vertical and horizontal axes had their font color changed to white to hide them from view and to clean the final figure.

Gridlines were added along with the label for the reference line, which indicates to point where the total confirmed SARS-CoV-2 deaths are doubling every five days.

Here is the final chart after some formatting changes were made.

Conclusions

Although we plotted total deaths from SARS-CoV-19 from existing data, these were limited to five countries. More countries can be added using the available data, and it is encouraged that you try to plot all the other countries as an exercise. The reference line provides us with the doubling of deaths on a log-scale and carefully provides the readers with a threshold where certain doubling of deaths would be reported. China seems to have controlled their total number of deaths, but there is a spike at the end of day 87 that shows an increase in deaths. This may be due to reporting error or a change in the definitions of death. The US, including the other European countries, are trailing the rest of the world in containing the SARA-CoV-2 pandemic. Hopefully, this type of data visualization will help inform decision makers to develop policies that would mitigate the impact of SARS-CoV-19 on mortality any parts of the world.

You can download the data and complete exercise here.

References

  1. Roser M, Ritchie H, Ortiz-Ospina E, Hasell J. Coronavirus Disease (COVID-19) – Statistics and Research. Our World Data. March 2020. https://ourworldindata.org/coronavirus. Accessed April 17, 2020.

  2. European Centre for Disease Control and Prevention. Download today’s data on the geographic distribution of COVID-19 cases worldwide. European Centre for Disease Prevention and Control. https://www.ecdc.europa.eu/en/publications-data/download-todays-data-geographic-distribution-covid-19-cases-worldwide. Published April 18, 2020. Accessed April 18, 2020.

  3. Our World in Data. GitHub: Owid/Covid-19-Data. Our World in Data; 2020. https://github.com/owid/covid-19-data. Accessed April 18, 2020.

Communicating data effectively with data visualizations: Part 23 (Epidemic Curves)

INTRODUCTION

In December 2019, a novel strain of coronavirus was detected in Wuhan, the capital city of the Hubei province in China. This coronavirus was designated as severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). This current iteration of the coronavirus has many similar characteristics to its earlier ancestor SARS-CoV-1,[1] which was first detected in 2003 and known simply as SARS. According to a recent study, clinical characteristics of patients in China who were infected with SARS-CoV-2 included fever (up to 88.7% who were hospitalized) and cough (67.8%).[2] The median age of patients infected was 47.0 years (IQR: 35.0-58.0) with a large distribution of 58.3% over the age of 50 years having severe symptoms. Additionally, the case fatality rate was reported to be 1.4%.

In January 2020, the World Health Organization (WHO) in regards to the SARS-CoV-2 outbreak declared a global health emergency.[3] Regardless, as SARS-CoV-2 spread across the globe into a pandemic, many countries started to report the attributable number of cases and deaths. According to the WHO, the global total of confirmed cases is at 191,127 and global deaths is at 7,807 (as reported on March 18, 2020).[4]

One of the most important tools in understanding the SARS-CoV-2 epidemic course is the epidemic curve. Epidemic curves allow epidemiologists to visualize the progression of an outbreak by surveilling the number of cases across time.[5] The epidemic curve informs epidemiologists about the pattern of the outbreak’s spread, magnitude, time to exposure, and outliers. Moreover, the epidemic curve is constantly updated as more data become available.

As the SARS-CoV-2 pandemic spreads to other countries, many data visualizations have been developed to help educate and inform people. Johns Hopkins University has developed a real-time dashboard with epidemic curves on the SARS-CoV-2 pandemic that is an excellent source of global cases and mortality. The Centers for Disease Control and Prevention (CDC) also has a series of data visualizations on the SARS-CoV-2 outbreak in the United States including an epidemic curve.

This article will review the features of an epidemic curve and provide a tutorial on creating one based on the available data from the CDC on the SARS-CoV-2 outbreak in the United States.

EPIDEMIC CURVE

When an outbreak happens, there is an urgency to determine when it first occurred. Epidemiologists carefully, collect data to determine who patient zero is and when the case was first identified. This gives them a starting point for when the outbreak occurred. Epidemic curves provide information on the outbreaks’ spread, magnitude, incubation period, outliers, and time trend. Key features of the epidemic curve include the number of cases on the Y-axis and the date of illness on the X-axis. Figure 1 illustrates the key features of the epidemic curve for a point-source outbreak.

Figure 1. Key features of the epidemic curve for a point-source outbreak.

Epidemic curves can tell us information about the outbreak’s pattern of spread. Figure 1 illustrates the pattern of spread for a point-source outbreak. In a point-source outbreak, a single source of contamination affects a group of people at a single event (e.g., rotten potato salad at a dinner party). Other patterns include continuous-source and propagated-source outbreaks. Continuous-source outbreak occurs when the group of people are exposed to a source of contamination for a period of time (e.g., lead poisoning in children). Propagated-source outbreak occurs when the contamination is spread from person-to-person (e.g., flu). The SARS-CoV-2 is an example of a mixed-source outbreak where the early outbreak was due to a common-source (e.g., possibly from zoonotic transmission from animal to human) followed by a propagated-source outbreak where the virus is spread from person-to-person via air droplets or physical contact.[6] Based on a recent study, the mean incubation period for SARS-CoV-2 is 5.1 days (95% CI: 4.5 to 5.8 days).[6]

 

Motivating example

The SARS-CoV-2 outbreak in the United States was first reported in January 14, 2020. Data on the number of cases can be downloaded from the CDC’s SARS-CoV-2 surveillance site (Note: CDC data are updated daily; hence, the data for this exercise will not reflect these changes). We will use these data to create an epidemic curve of the SARS-CoV-2 outbreak in the United States using Excel. You can download the data files used in this exercise here.

The data are arranged in a wide format where the date (time) is represented by columns and the number of cases is represented by rows. This makes it much easier to generate the epidemic curve in Excel.

Step 1. Highlight the data and Insert a bar chart.

Select the data and insert a clustered column chart. The default version will provide a simple epidemic curve. However, we want to remove the spaces between the bars. To do that, we will need to format the

Step 2. Changing the size of the bars.

To change the size of the bars, we need to right-click one of them to bring up the editing menu. Then we select the Format Data Series… to bring up the options. We set the Gap Width of the bars to 0% so that their sides are in contact with each other. But to distinguish them, we can change the outline’s color to White. Increasing the width of the border’s color will increase gaps between each bar.

After a few more changes (e.g., color and labels), the final epidemic curve will represent the CDC’s data on SARS-CoV-2 on March 18, 2020 (Figure 2). Since data are constantly changing and require validation during an outbreak, this epidemic curve will eventually change. It is recommended that you constantly update this exercise’s data in order to have the most recent, accurate, and valid data from the CDC on SARS-CoV-2. You can also compare your findings to those of the CDC at their website.

Figure 2. Cases of SARS-CoV-2 in the United States.

Conclusions

Epidemic curves are helpful in understanding a disease outbreak in a community. They provide us with a visual representation of the outbreak’s magnitude, pattern, and time period, which will allow us to implement public health policy to stem, reduce, and eventually eradicate the contagion from our population. Although this is a short introduction on epidemic curves, it will, hopefully, be enough for you to review and interpret other epidemic curves in the news or literature.

Files related to this exercise are available here.

References

  1. van Doremalen N, Bushmaker T, Morris DH, et al. Aerosol and Surface Stability of SARS-CoV-2 as Compared with SARS-CoV-1. N Engl J Med. 2020;0(0):null. doi:10.1056/NEJMc2004973

  2. Guan W, Ni Z, Hu Y, et al. Clinical Characteristics of Coronavirus Disease 2019 in China. N Engl J Med. 2020;0(0):null. doi:10.1056/NEJMoa2002032

  3. World Health Organization. Statement on the second meeting of the International Health Regulations (2005) Emergency Committee regarding the outbreak of novel coronavirus (2019-nCoV). https://www.who.int/news-room/detail/30-01-2020-statement-on-the-second-meeting-of-the-international-health-regulations-(2005)-emergency-committee-regarding-the-outbreak-of-novel-coronavirus-(2019-ncov). Accessed March 19, 2020.

  4. World Health Organization. Coronavirus Disease 2019 (COVID-19) Situation Report –58.; 2020. https://www.who.int/docs/default-source/coronaviruse/situation-reports/20200318-sitrep-58-covid-19.pdf?sfvrsn=20876712_2. Accessed March 19, 2020.

  5. Centers for Disease Control and Prevention. Interpretation of Epidemic (Epi) Curves during Ongoing Outbreak Investigations | Foodborne Outbreaks | Food Safety | CDC. https://www.cdc.gov/foodsafety/outbreaks/investigating-outbreaks/epi-curves.html. Published November 16, 2018. Accessed March 19, 2020.

  6. Lauer SA, Grantz KH, Bi Q, et al. The Incubation Period of Coronavirus Disease 2019 (COVID-19) From Publicly Reported Confirmed Cases: Estimation and Application. Ann Intern Med. March 2020. doi:10.7326/M20-0504