Visualization of diseases

VIZEASES :
RE-EMERGENCE OF WHOOPING COUGH
VENKATESH DUVVURI (VED14) , BHARATHKUMAR INBASEKARAN (BHI2), SETAREH SARACHI (SES188), SARAHVINSKI (SEV52)

MOTIVATION
• Recently, multiple disease that were once under control are beginning to re-emerge.
• Whooping cough (Pertussis) is a prevalent re-emerging disease, with more than oneWhooping cough epidemic
declared in California in the past 5 years.

PROJECT TYCHO DATA
ProjectTycho is a collaborative project at the University of Pittsburgh.
It consists of a large dataset containing all weekly surveillance reports
of nationally notifiable diseases for all US cities and states published
since 1888.The data set consists of 87,950,807 reported individual
cases, each localized in space and time.

DATA EXPLORATION
• The Whooping Cough data is
missing information from
1956-1973. According to a
member of Project Tycho, this
data is missing because the
number of cases were so low.
• To compensate for this lack of
data, we either omitted these
years or combined the data by
decade, depending on the
application.

RELATED WORK
We referenced two other projects that are exploring the resurgence ofWhooping Cough :
In the paper "The pertussis enigma: reconciling epidemiology,
immunology and evolution," scientists at the Rohani lab of the
University of Georgia explored if the Pertussis resurgence is
universal, the geographical variation in Pertussis trends, and the
reasons for these.
In an NEJM paper "Contagious Diseases in the United States
from 1888 to the Present," scientists explored why some
contagious diseases are now on the rise despite the
availability of vaccines, and if intentional under-vaccination
and organized anti-vaccination movements amplify the
problem.

OURVISUALIZATION SYSTEM
• Our website implements a simple series of buttons/tabs at the top of the display for the
user to move through our system.
• Explanation of each graph and some additional information about the re-emergence of
Whooping Cough is included with each visualization.
• Guidance is included to point out important and interesting information and filters.
• All visualizations were made using d3.js.

THE STORY
WHY explore
Whooping Cough data?
WHEN have important
Whooping Cough events
occurred?
WHERE have the highest
number of Whooping
Cough cases occurred?
This is a relevant and current
issue.The problem of re-
emerging diseases, especially
Whooping Cough, is introduced
on the system's first page.As the
user moves through each
visualization, explanation of each
visualization, interesting
information, and guidance will be
provided.
All of the visualizations are
time-based.This shows
important events, such as
release of the original
vaccine, near-eradication,
implementation of a
second vaccine, and re-
emergence.
We use multiple
visualizations to show the
Whooping Cough
occurrences by state, such
as Choropleth, timeline,
and heat map.

PART 1:TIME SERIES
TheWhooping Cough resurgence trend
between decades1930's to 2010's (based
on the reported counts).
As it has been shown, the highest
reported counts of the disease is in 1940,
before the vaccine was implemented.The
lowest reported count is in1970's.
The graph shows that the disease is re-
emerging in the 2010's.
By hovering over each decade, the user
can explore the counts for each decade.

PART 1I: EXPLORATORY TIMELINE
Whooping Cough Cases byYear
for All States
Whooping Cough Cases byYear
for Selected States

PART 1I: EXPLORATORY TIMELINE - FILTERING
The user can explore data by
year span, instead of decades,
by using the exploratory
timeline and choosing the
time period.

PART III: CHOROPLETH
United States Whooping Cough Cases by Decade
1940's 1970's 2010's

PART IV: HEATMAP MATRIX
This heatmap shows the
number of cases per state
by decade. Color indicates
the number of cases.
The heatmap re-arranges
by sorting on either a
decade or state when
clicked.

PARTV: CIRCULAR HEAT MAP
• Along with location and time trends,
we wanted to explore trends by
month.
• These visualizations can be used to
understand when an outbreak is most
likely to hit.
• They also reinforce our goal of
showing the re-emerging diseases.
• Hovering over a section will give the
value of that section, as seen to the
right.

PARTV: CIRCULAR HEAT MAP
Before/DuringVaccine years
More recent years. Higher number of
cases can be seen for the
2010s. Numbers are also higher for the
1990s, the time when the new vaccine
came out.

PARTVI: PARALLEL LINES
The Parallel Lines continues to
show more monthly trends, this
time using month and state.
The graph is interactive.
Choosing a selection on each
parallel line will only show the
data that passes through those
lines.

PARTVII:WORD CLOUD
On December 5th, we pulled the top 3
news articles on Google with the search
key "whooping cough."
Using this data, we made a word cloud to
explore some current topics related to
whooping cough.

CASE STUDY
• 5 users, 2 of which were familiar with information visualization, One was familiar with Public Health Data
• Asked users to use the website, read the story, go to visualizations and interact with visualizations
Challenges Response
Understanding Story The story was well understood from the home page.
Time Series Pros :The time series was well understood.The user interaction was clear.
ExploratoryTimeline Pros :The timeline was well understood.The missing data was clearly perceived.
Cons:The user interaction was confusing, without instruction.
Choropleth Pros :The users interact easily with the map.They were able to track states disease count
through out the decades.
Heat map Matrix Pros :Very well perceived.The users were able to find the highest and lowest counts.
They found it more informative than Choropleth, regarding the states.
Circular Heat map Pros :The user understood the visualization well.
Word Cloud Pros :The user found it interesting.
Cons :The user did not find it informative.

CHALLENGES & LIMITATIONS
• Finding an efficient and effective way of using the data, because of the missing data from 1956 and 1973.
• Because the number of cases were so high in the 1940s, our data was greatly skewed.This made it difficult to
do some visualizations that included data for all years.
• The Project Tycho data did not include many attributes.Thus, we had to think of some new features to
explore, such as monthly changes.
• Not having many attributes, we had great limitations in number of visualizations we did.
• Initially, we attempted to make a word cloud usingTwitter data, but Tweets with the key phrase "whooping
cough" were sparse.
• We attempted to do a comparison between several diseases; However, the data was not consistent among
them.

IMPROVEMENT & FUTURE WORK
• Use statistical model to infer the missing data.
• Real-time text analysis of social media content, usingWord Cloud, and other text
visualization tools.
• Implement additional user interactions and explore interactive visual systems.

REFERENCES
• Domenech de Cellès, Matthieu, Felicia M. G. Magpantay, Aaron A. King, and Pejman Rohani. "The Pertussis
Enigma: Reconciling Epidemiology, Immunology and Evolution." Proceedings of the Royal Society B: Biological
Sciences 283.1822 (2016): 20152309. Web.
• Associated Press. (2014, December 12). Whooping Cough Epidemic: California's Worst in 70 Years. The Mercury
News. Retrieved December 2, 2016, from http://www.mercurynews.com/2014/12/12/whooping-cough-epidemic-
californias-worst-in-70-years/
• Mayo Clinic Staff (2016). Whooping cough. Retrieved December 04, 2016, from http://www.mayoclinic.org/diseases-
conditions/whooping-cough/basics/definition/con-20023295
• Emerging and re-emerging infectious diseases. (1999). Rockville, MD: National Institutes of Health, Office of Science
Education.Revised September 2012
• van Panhuis WG, Grefenstette J, Jung SY, Chok NS, Cross A, Eng H, et al. Contagious diseases in the United States
from 1888 to the present.N.Engl.J.Med. 2013 Nov 28;369(22):2152-2158.
THANKYOU!

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