The board of education in Hometown wants to address the annual flu outbreaks in the local schools. They are considering policies like closing schools early or providing free flu vaccinations. To help decide, the board collected flu recovery data from the school nurse at Maplewood School. However, the raw data has inconsistencies and missing information that make it hard to identify patterns. The board needs to adjust the data, fill in gaps, and analyze it properly to understand the flu's severity and timing in order to determine the best policy response.

1. The Flu
(Graphing Data and Seeing Patterns)
Every year, the elementary and high school schools in Hometown have been hit by a
severe outbreak of influenza. The board of education is interested in taking action to
lessen the annual impacts of the flu. Several policies have been proposed:
•Closing the school before the flu virus reaches a peak
•Providing free vaccinations to teachers residing in the school district
•Provide free vaccinations to children residing in the school district
•Or doing nothing
Any of these options could prove costly both in real dollars and in terms of teacher or
student time missed from school. In addition, the vaccinations are known to have some
adverse side effects for some portion of the population. It has proven medically that this
particular flu strain last for 5 days.
Before making a decision on how to deal with their annual flu crises, the Board decided
to gather some data concerning the severity and timing of the problem. Unfortunately, the
board discovered that no systematic records of the severity of the flu have been kept on a
system-wide basis. The only available records are those of Mrs. Stewart, the school
nurse at the Maplewood School. Early in September, she began to notice that many
students were staying home from school. When they returned, they said that they had the
flu. Being a conscientious nurse, Mrs. Stewart had kept a careful record of recoveries
shown in the table below.
Date Number of Recoveries Date Number of Recoveries
Sept 4 0 Sept. 25 18
5 0 26 4
6 1 27 3
7 3 28 3
8 6 29 2
11 55 Oct. 2 5
12 37 3 1
13 39 4 1
14 38 5 1
15 38 6 1
18 74 9 3
19 17 10 0
20 14 11 0
21 11 12 0
22 9 13 0

2. While it looks as if a large number of students recovered from the flu between September
11 and September 18, it is hard to see a clear pattern in these figures. Previously,
converting data from a table into a graph made it clearer. However, in this case, the graph
will show peaks. Why do these peaks occur? Why was the number of students returning
to school on September 18 so high?
The number of students recorded on that day, as having recovered from flu is 74, which is
significantly higher than the number recorded just before or after September 18. Only 17
students are marked as having recovered on September 19. How many are listed as
recovered for the day before the peak?
The table above does not show any information for either September 16 and 17. This
seems odd, but it does not explain the peak at September 18. There could be other
missing information in table above.
Guide Questions:
1. How should data have been collected?
2. Why is the data, when graphed appears distorted? Give explanations and cases
where this can happen.
3. How should the data appear when the missing information is supplied? Perform
an adjustment on the data of recoveries.
4. When did the students contract the flu?
5. What was the total number of students sick with the flu? Compute the total
number of sick persons for each day of the flu epidemic. Plot the total number of
persons sick for each day.
6. When did the maximum number of sick persons occur? What happened on the
same day that the maximum number of persons was sick? Explain why.
7. How many students got sick with the flu each day?
8. How should the board address the problem? Give recommendations and their
justifications.
9. State the problem of the system (following SD approach)