Why It’s Important to "Flatten the Curve" Group/Individual
Lab
Due Date: Periods 3 & 4= Friday April 17th at 3:00pm; Periods 7= Monday April at
3:00pm
NOTE: This lab will demonstrate the need to flatten the curve in the COVID-19
outbreak and why it was important to take extreme measures to reduce the
spread of the virus. Early on this epidemic, the Governor of Ohio, Mike DeWine,
took steps to help flatten the curve. These include steps like: adding more
hospitals beds, closing schools, closing non-essential businesses, limiting
gatherings, etc. This lab shows just one way of how our local healthcare system
would have overwhelmed, had these steps not been taken. Thankfully, with the
measures taken by Gov. DeWine, the numbers you are seeing in your lab will
likely not be as high as you have calculated. Therefore, taking measures to flatten
the curve is crucial in the spread of a virus. We will discuss more about this next
week.
Directions: Follow steps 1-8 below to complete the lab. Make sure to watch the
corresponding YouTube video in Step 2, which gives an example of how to
complete the lab. Honors students must complete the additional section as well.
Step 1. You will work in groups based on the first letter of your last name. IF YOU
CHOOSE YOU MAY WORK ALONE, based on first letter of your last name as well.
E-mail/text your group members or work alone to begin the lab.
Last name begins with A – E, you work in the Tiffin Group
Last name begins with F – K, you work in the Maumee Group
Last name begins with L – P, you work in the Bowling Green Group
Last name begins with Q – T, you work in the Sandusky Group
Last name begins with U – Z, you work in the Findlay Group
Step 2. Watch the short YouTube below, which demonstrates how to complete
the lab. An example of how to complete each step is shown in the video. IT IS
VERY IMPORTANT YOU WATCH THIS SHORT VIDEO BEFORE THE LAB!
Step 3. Start to complete the graph. You will start with the City Population
Column and the Total City Population. You will use data from the U.S. Census to
complete this, the data is included in the links below. Depending on the city group
you are in, you will write in the population for the age groups in the data table.
You will need to add up the people within each age group to complete the table.
****Please watch the YouTube video in step 2 to see how to add up the people
within each age group.
Here are the links for each groups' city data:
Tiffin:
https://data.census.gov/cedsci/table?hidePreview=true&tid=ACSDP5Y2018.DP05&layer=VT_2018_160_
00_PY_D1&cid=DP05_0001E&vintage=2018&g=1600000US3976778
Maumee:
https://data.census.gov/cedsci/table?hidePreview=true&tid=ACSDP5Y2018.DP05&layer=VT_2018_160_
00_PY_D1&cid=DP05_0001E&vintage=2018&g=1600000US3948342
Bowling Green:
https://data.census.gov/cedsci/table?hidePreview=true&tid=ACSDP5Y2018.DP05&layer=VT_2018_160_
00_PY_D1&cid=DP05_0001E&vintage=2018&g=1600000US3907972
Sandusky:
https://data.census.gov/cedsci/table?hidePreview=true&tid=ACSDP5Y2018.DP05&layer=VT_2018_160_
00_PY_D1&cid=DP05_0001E&vintage=2018&g=1600000US3970380
Findlay:
https://data.census.gov/cedsci/table?hidePreview=true&tid=ACSDP5Y2018.DP05&layer=VT_2018_160_
00_PY_D1&cid=DP05_0001E&vintage=2018&g=1600000US3927048
Step 4. Complete the Infection Column and add up the Total Infections for each
age group in your city. The “Infections” will be (City Population x % Prevalence).
Step 5. Complete the Expected Death Column and add up the Total Expected for
each age group in your city. The “Expected Deaths” will be (Infections x Fatality
Rate %).
Step 6. Complete the Total Hospitalized. “Total Hospitalized” will be (Total
Infections x 20%). Current studies have shown that of the people that are
infected with COVID-19, 20% will be hospitalized
Step 7. Write in the Hospital Beds in the City. Your city will correspond to you
last name as follows:
A – E / Tiffin: Hospital Beds = 45 beds
F – K / Maumee: Hospital Beds = 166 beds
L – P / Bowling Green: Hospital Beds = 103 beds
Q – T / Sandusky: Hospital Beds = 183
U – Z / Findlay: Hospital Beds = 141 beds
Age
Under 10 – 19
20 – 34
35 – 44
45 – 54
55 – 59
60 – 64
65 – 74
75 – 84
85 years +
Projected Impact of COVID-19 (70% Prevalence)
Fatality
Rate
City Population
Infections
0.20%
0.20%
0.25%
0.80%
1.30%
2.00%
3.00%
8.50%
14.80%
Expected Deaths
Total
Total Infections
Total Hospitalized
(20% of all infected
will be
hospitalized)
Beds in County
214
* REMEMBER THAT THE DATA YOU FOUND IS ONLY A MODEL AND THANKFULLY
MEASURES TAKEN BY GOV. DEWINE WILL REDUCE THE TOTAL HOSPITALIZED AT
ONE TIME*
Step 8. With your group or individually, answer the following questions:
1. Current models of the spread of COVID-19 in Ohio show that by flattening the
curve, Ohio is reducing the number of people hospitalized at one time. The data
table you completed demonstrated the overall people that may hospitalized if
COVID-19 was 40% prevalent.
a. If everyone in your city was infected with COVID-19 at the same the time
and 20% of those infected were hospitalized, would your city have enough
hospital beds?
b. Considering your answer to a, list and explain at least 3 "flattening the
curve" measures that you think are most helpful IN YOUR CITY to reduce the
number of people hospitalized at the same time. (Remember to consider the ages
of people in your city, where your city is located, what businesses are in your city
like an amusement park or a college, etc.)
HONORS ONLY SECTION
Directions: Using the same city as you used above, work in your group or
individually to complete the data table on the next page and answer the
questions below. This data table demonstrates the impact of the flu (seasonal
influenza) on the population of your city.
Projected Impact of Regular Seasonal Influenza (20% Prevalence)
Fatality
Age
Rate
City Population
Infections
Expected Deaths
Under 5 - 19
0.01%
20 – 34
35 – 44
45 – 54
55 – 59
60 – 64
65 – 74
75 – 84
0.02%
0.02%
0.02%
0.02%
0.02%
0.06%
0.83%
85 years +
0.83%
Total
Total Infections
Total Hospitalized
(1% of all infected
will be
hospitalized)
Hospital Beds in City
1. How do the rates of hospitalizations and infections of the flu compare to those
you completed to COVID-19? Are they lower or higher?