Download Ebola`s Impact on Individuals, Cultures, Society, and the Economy

Ebola’s Impact on Individuals, Cultures, Society,
and the Economy
Neha Baliga, Caroline Ghio
Summer Ventures in Math and Science 2015
Visual and Image Processing
Rahman Tashakkori, Mitchell Parry, David Kale, Sina Tashakkori
Appalachian State University
Abstract –The purpose of this research is to collect and analyze data related to Ebola
using visualization tools and to use the data to create a predictive model that may help
minimize or eliminate the impact of this disease. Microsoft Excel, Tableau Public,
MapChart, and NetLogo were used for analysis and modeling. Models were created and
the prevalence of specific symptoms, the morbidity and mortality rate for different
countries and age groups, and the impact Ebola exerts on the economy were illustrated.
This research provided visualization of the data and presentation of the facts in an
understandable way, which may allow people to respond to the threat of Ebola in a
manner that maximizes resources to save lives.
1.0 Introduction
Ebola Hemorrhagic Fever is a deadly disease that causes panic and pandemonium
wherever it breaks out. To better understand this dangerous disease, some scientists are
using visualization and simulation, such as those tools created by the World Health
Organization, the Network Dynamics and Simulation Science Laboratory at Virginia
Tech, and even the Washington Post [1][2][3]. To this point, visualization and simulation
have been primarily general in nature and show the overall spread and mortality of Ebola.
However, much more work can be done to develop these visualization and simulation
tools to more effectively illustrate and simulate the impact of variables related to Ebola.
There are factors related to the spread of Ebola, along with ramifications of the
disease that warrant further exploration through visualization and simulation. While
many models illustrate mortality, they often simply show the total number of individuals
diagnosed with the virus and predict a mortality rate. However, mortality varies widely
depending on variables such as age, gender, the availability of medical care, attitudes
towards doctors, nurses, and healthcare systems, and cultural beliefs regarding burial
practices. Current simulation and modeling tools are often used to predict the rate at
which others are infected, however they may not include important considerations such
as the period during which individuals are infectious or the means by which infections
may spread based on social or cultural beliefs. Ebola has an infectious period of sixty-one
days, therefore, there is a significant amount of time during which Ebola victims can pass
the illness to others, especially in nations where medical care is limited, or attitudes
towards healthcare or beliefs about burial practices take precedence over modern
healthcare practices [4]. Males are infectious for an even greater length of time as
evidence has been found that they continue to have the Ebola virus in their seminal fluid
for up to eighty-two days after they have recovered from the disease; therefore, any
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sexual encounters can infect other people [5]. While complex, the inclusion of these
factors would increase the usefulness of visualization and simulation tools.
Further avenues for visualization and simulation include consideration of factors that
relate to diagnosis, such as the incubation period and presentation of symptoms. During
the lengthy incubation period of two to twenty-one days, infected individuals are
typically unaware of their illness [6]. This is important since a majority of people will not
receive medical help or be counted among the ill until symptoms manifest themselves.
Further complicating diagnosis is that the complex array of symptoms varies in
presentation among individuals and makes diagnosis difficult. The first symptoms of
Ebola are fever, weakness, muscle pain, headache, nausea, and sore throat; these
symptoms mimic those of other diseases. As the disease progresses, there may be
vomiting, diarrhea, bleeding of tissue that produces mucus, ecchymosis, a discoloration
of the skin resulting from bleeding underneath, and septic shock, which is a potentially
lethal drop in blood pressure due to the presence of bacteria in the blood, impaired kidney
and liver function, internal and external bleeding, and abnormal laboratory values, such
as low white blood cell and platelet counts and elevated liver enzymes [7]. The period
between infection and presentation of the symptoms, especially the most life threatening
signs of Ebola, seems to be an area for exploration through use of visualization and
simulation so that diagnosis can be made more quickly and treatment implemented.
Ebola is a deadly disease that spreads rapidly in some nations. The average Ebola
case fatality rate is approximately 50%, but the most recent outbreak in western Africa
had a fatality rate of 70% [8]. While those around the world have good intentions, it has
been difficult to streamline efforts and resources to combat Ebola. The success of the
diagnosis and treatment of Ebola is intertwined with demographics, economic conditions,
level of education, and social and cultural factors that lend themselves to study with
visualization and simulation tools. The goal of this study was to use tools, such as
Microsoft Excel, Tableau Public, MapChart, and NetLogo, to identify patterns in the data
and make a predictive model that can be used to focus efforts to minimize or eliminate
the impact of this dreadful disease.
2.0 Methods
The morbidity and mortality rates of Ebola are challenging to quantify because of the
difficulty in diagnosing the illness in early stages. With a multitude of cases that likely go
unreported in underdeveloped nations, the scientific community has largely reached a
consensus and released data about the disease so that patterns can be identified and
illustrated using visualization tools. In this research, graphs and other visualization tools
were used to present data related to symptoms, morbidity, and mortality by country, over
time, and by age group, effect of Ebola on the Gross Domestic Product, and other factors.
2.1 Graphical Visualization Methods
The data for this research was gathered from sources such as the World Health
Organization (WHO) and The Centers for Disease Control and Prevention (CDC) and
was saved into Excel. Using Excel, graphs were created that demonstrate the
susceptibility of different people to Ebola, the mortality rates in different countries, and
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the economic changes following the Ebola outbreak in western Africa. Additionally, data
was imported into Tableau Public to create types of visualizations that were unavailable
in Excel, such as bubble charts that more fully illustrate the deadliness and infectiousness
of Ebola in different countries. Finally, MapChart was used to create the map
infographics that show the travel restrictions and the countries where Ebola was
prevalent.
2.2 NetLogo Modeling Methods
In addition to creating graphs and charts, a model was created using NetLogo because
it allows for the modeling of complex situations. In making the NetLogo model, the
statistics to be expressed were gathered: the mortality rate, incubation period, infectious
period, period after recovery when people can still infect others, recovery rate, and the
mortality rate at specific ages. The original conditions were established in the Setup
procedure and the methods called by the Setup procedure and the actions that would
occur upon pressing “Go” were dictated in the procedure, Go (as shown in Figure 1). The
Setup procedure and the attached procedures create the black background, insert the
number of people as dictated by the user input, and infect ten of these people, causing
them to be red (Figure 2). Then the Go procedure was written and the procedures called
from Go were written (Figure 3). These procedures cause movement, dictate who gets
infected at what rate, infect people, dictate the rate of recovery, cause people to recover,
and cause people to die based on input. Additionally, healthcare professionals were added
in order to illustrate the increased recovery rate associated with healthcare professionals
and the methods they employ, namely “quarantine,” which is demonstrated through a
lack of movement and an inability to infect others. Buttons and sliders were also created
in order to allow the user to control the population, the infectiousness of the disease, the
chance of recovery, the duration of the disease, and the chance that two people will have
sex (possibly causing a male to infect another person with Ebola).
Figure 1 - Flow chart on NetLogo model created
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Figure 3 - NetLogo
Simulation Actions
Figure 2 - NetLogo Simulation Setup
3.0 Results
Figure 4 shows the percentage of Ebola patients that report experiencing different
symptoms. It is clear that there is variance in the symptoms experienced by patients.
Figure 4 - Symptoms most commonly experienced by Ebola patients [9]
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Figure 5 shows the countries that had one or more person infected with Ebola. Figure
6 demonstrates the countries that imposed a travel ban or restriction on countries where
Ebola was prominent.
Figure 5 - Ebola infected countries [10]
Figure 6 - Countries that implemented travel restrictions [11]
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Figure 7 illustrates the number of cases and deaths from Ebola between 1976 and
2015. Figure 8 shows the survival rate of Ebola in the countries that have treated more
than one patient with Ebola.
Figure 7 - Morbidity and Mortality by Country [12]
Figure 8- Survival rate for each country [12], [10]
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Figure 9 shows the number of deaths and cases of Ebola from 1976 until present day.
Figure 9- Morbidity and Mortality over time [13]
Figure 10 shows the mortality rate of Ebola for different age groups. A clear
difference between the mortality rate of age groups can be seen.
Figure 10- The mortality rates of Ebola for each age-group [14]
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Figure 11 illustrates the loss in Gross Domestic Product in Liberia, Guinea, and Sierra
Leone, the three countries most effected by the recent Ebola outbreak.
Figure 11- Gross Domestic Product (GDP) loss in three core countries compared [15]
Figure 12 shows the initial position and values for the NetLogo model, in which there
are 10 infected people, 15 doctors, and 1475 healthy people. Figure 13 shows the
NetLogo model while running, when the sickness is spreading, as shown through the
spread of the color red. Figure 14 shows the NetLogo model at completion, with
individuals seen as healthy, recovered, or deceased.
Figure 12- Beginning of the Ebola infection simulation
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Figure 13- Halfway through the Ebola infectious simulation
Figure 14- End of the Ebola infectious simulation
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4.0 Conclusion
Data visualization tools, specifically Microsoft Excel, Tableau Public, and MapChart,
were utilized for analysis of Ebola data. The study revealed many trends related to Ebola,
including symptoms, morbidity and mortality as they relate to geography, age,
government regulation regarding travel, and the economic impact of this disease. Figure 4
shows the symptoms most commonly experienced by Ebola patients, while
acknowledging the inconsistencies in presentation of symptoms among patients. Since
many symptoms of Ebola are also signs of other diseases, identification of an Ebola
victim is often very difficult. Data such as that shown in Figure 4 can aid those trying to
make diagnoses by highlighting symptoms such as fever, headaches weakness,
and diarrhea which are experienced by greater than 50% of Ebola victims. Used in
conjunction with other information, this data may allow for earlier treatment of
individuals and protection of those in proximity to Ebola patients.
Figure 5 depicts the countries where one or more infected person has been housed. It
is clear that the majority of the outbreaks occurred in Africa. However, as Figure 5
shows, the disease did not ravage its way across the entirety of Africa. It missed Chad,
Niger, Burkina Faso, Ghana, Togo, and Benin. The extent of infection is presumed to be
because the place of Ebola's origin, a town in Guinea, was a trading post. The fact that it
was a trading post meant that many people from other African countries traveled to and
from this location, carrying the disease back to their home countries. Other people visited
these newly infected nations, either for business or pleasure, and brought Ebola back to
their home nations also. This is what occurred in most of the effected locations in Africa,
in addition to Spain, Great Britain, and the United States. During the outbreak, experts
attempted to predict the travel of the disease using maps similar to this one. But, the
unpredictability of travel and infection led to mass spreading and the inability of
scientists or countries to protect citizens.
The manner by which countries attempted to protect their citizens was frequently with
the implementation of travel restrictions. Figure 6 illustrates the countries that imposed
travel bans or restrictions on Ebola-affected countries. As shown by a comparison of
Figures 5 and 6, travel restrictions were relatively effective. Of all the countries that
imposed bans or restrictions, only three had any incidents of Ebola infection: the United
States, Gabon, and South Africa. Bans and restrictions in other countries were completely
effective in preventing Ebola from crossing their borders. Illustration of this data
warrants evaluation of bans and restrictions as effective means of limiting the spread of
Ebola.
Figure 7 shows the number of Ebola cases and deaths for each country since its
identification in 1976. Clearly, the disease has been centered in Africa, as the only nonAfrican countries that appear are Spain, the United Kingdom, and the United States.
Figure 7 also shows that Sierra Leone has had the most cases of Ebola with nearly 10,000
and Liberia has a significant number that totals almost 8,000 cases. However, Liberia has
more deaths from Ebola than Sierra Leone. Sierra Leone, Guinea, and Liberia are the
three nations most affected by Ebola, and Liberia has the highest mortality rate, or the
lowest recovery rate of the three. Liberia’s mortality rate of 60% is confirmed in Figure
8, which shows the survival rates for most of the countries affected by the most recent
outbreak. Sierra Leone has the highest recovery rate at 65%, while Liberia has the lowest
at 40%. Additionally, Figure 7 shows many other countries that had cases of Ebola,
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although not as significant in number. There is a clear disparity in survival rates among
these countries, with Figure 8 showing the United State’s survival rate of 80% during its
short bout with Ebola, while Congo had a survival rate of only 15%. So, while the three
core countries that were overwhelmed with Ebola cases had a fairly high survival rate at
an average of 50%, the low survival rate of many of the other countries causes the overall
mortality rate for Ebola to be as high as 70%.
Figure 9 outlines the morbidity and mortality in all countries with Ebola over time.
There are clear spikes during the major outbreaks in history: 1976, 1995, 2000, 2007, and
most recently, 2014. These graphs demonstrate the enormity and unprecedented nature of
the most recent Ebola outbreak. It is also interesting to note the very similar shape of
each graph. This shows that so far in human history, every large outbreak of Ebola has
been accompanied by a large number of deaths, indicating the inability of scientists and
doctors to significantly decrease the ratio between cases and deaths.
In addition to the trends that exist between countries and over time, there exists a
pattern of mortality based on age, as presented in Figure 10. The lowest mortality rate
exists for children between the ages of ten to fifteen. Babies and toddlers have a high
mortality rate, along with older adults. This is speculated to be due to decreased immune
system protection, developing or declining organ function, and overall susceptibility to
different diseases of all kinds. Figure 10 data show that there is predictability to how the
body will respond with the Ebola virus based on age, even though the virus presents itself
with different symptoms.
Beyond the physical devastations caused by Ebola on individuals and countries, are
economic effects that are enough to ravage a country. Figure 11 shows the effects of
Ebola on the Gross Domestic Product (GDP) in Liberia, Sierra Leone, and Guinea. The
gross domestic product is the total monetary value of all the finished goods and services
produced within a country's borders during one year. [14] Each country's GDP suffered
dramatically as a result of the Ebola virus. In 2013, before the Ebola crisis, the
Democratic Republic of Congo was the poorest country in the world, while Liberia was
the fourth poorest nation, and Sierra Leone was the twenty-fourth poorest country [15].
There had been hope that these countries were making economic strides, but the Ebola
crisis diminished those hopes. This is because the major sources of income for these
western-African countries have decreased. Cross-border trade, 20-75% of the GDP in
western Africa, decreased due to mobility constraints, and the yield from the planting
season was smaller because of the deaths and illness related to Ebola. This has caused
inflation to rise in Liberia from 7.7% to 13.1%. Finally, many investors have grown
weary of problems in western Africa, so mining companies have been forced to pull out
of the countries in this region. Mining typically accounts for 14% of Liberia’s economy
and 17% of Sierra Leone’s, so withdrawal of investors dampened the economy [16].
In addition to creating a variety of graphs to visualize data, a NetLogo model was
created to demonstrate the impact of the disease. Unlike many other models, this model
shows the manner in which people can be infected and includes background information
about who is most likely to be infected. Several runs of the model showed that the people
who were very young or very old had the highest mortality. The model highlights the
lower mortality rate among the ten to fifteen year old age group, and compels one to ask
if there is some identifiable advantage that certain groups hold that can provide clues to
combatting the disease and increasing the chance of recovery that can be applied to save
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lives in the other age groups. Additionally, one of the most distinctive properties of Ebola
was included: the ability of men to spread the disease even after they have recovered. For
eighty-two days after recovery, the Ebola disease remains in men’s seminal fluid,
allowing for the spreading to other people with whom they have sexual relations.
Possibility of transmission through sexual contacts for such an extended period of time
has important implications for health education. It is clear by viewing the NetLogo model
that while Ebola is not a disease that spreads very quickly, it has a very high mortality
rate. This leads to implications regarding the importance of containing the disease
through early diagnosis and isolation of patients, health education about the possibility of
infection through sexual contact with a seemingly healthy male, and it also lends support
for travel restrictions. Finally, a model (Figure 12) was created which showed how the
presence of healthcare professionals and the use of quarantine procedures lead to
improvement in care and chance of survival. Figure 13 shows the model once the disease
begins to spread and others begin to get sick. This is significant because of the pace at
which it spreads. It is not a particularly fast spreading disease, but in a short amount of
time, it is clear how many people have died. Also, as more people become sick, the
healthcare professionals present are able to heal more people, but it does not make a
significant difference since the sickness has already spread. Figure 14 is the point at
which there are no more infected people and everyone is either immune or healthy. It is
clear that while some people have avoided infection in the model, a number of people
have been infected and become immune. These people represent the minority of the
people that recovered from the disease.
There are many relationships between age, economy, country, traditions, and Ebola
that currently accelerate morbidity and mortality, but can also be used to limit the damage
done by the disease. Through visualization of scientific data collected on Ebola, others
can more easily see and interpret information and recognize threats pertinent to individual
nations, age groups, and cultures. Modeling tools, such as NetLogo, show the rate at
which Ebola spreads and kills and also can predict the impact of specific interventions. In
the future, it is possible that diagnoses will be made more quickly through an algorithm
that considers the frequency with which symptoms of Ebola present themselves, or that
different models of care will be used to provide treatment for the youngest and oldest
members of the population, or travel restrictions will be considered by nations not
implementing them, or cultural practices will slowly change to allow professional care of
those infected with the virus or preparation of Ebola’s victims for burial. An
understanding of the data facilitated by visualization and modeling tools may help
educate people and lead to informed decisions by leaders across the world, who can
influence medical care, policy and cultural practices that will decrease the mortality rate
and reduce the spread of Ebola.
References
[1] World Health Organization, http://www.who.int/csr/disease/ebola/situationreports/en/
[2] Network Dynamics & Simulation Science Laboratory,
http://www.vbi.vt.edu/ndssl/featured-projects/ebola
[3] The Washington Post, http://www.washingtonpost.com/wp-srv/special/health/howebola-spreads/
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[4] World Health Organization, http://www.who.int/mediacentre/factsheets/fs103/en/
[5] World Health Organization, http://www.who.int/reproductivehealth/topics/rtis/ebolavirus-semen/en/
[6] World Health Organization, http://www.who.int/mediacentre/factsheets/fs103/en/
[7] Biodigital Human, https://human.biodigital.com/index.html
[8] World Health Organization, http://www.who.int/mediacentre/factsheets/fs103/en/
[9] Daily Mail, http://www.dailymail.co.uk/health/article-2815614/The-Ebola-agedivide-Fatality-rates-45s-94-25s-best-chance-survival.html
[10] Time, http://time.com/3554416/ebola-history-map/
[11] Voice of America, http://www.voanews.com/content/mali-scrambles-to-tracecontacts-of-ebola-stricken-baby-girl/2500379.html
[12] International Business Times, http://www.ibtimes.com/ebola-survival-rates-whypatients-outcomes-vary-1708555
[13] Health Intelligence, http://healthintelligence.drupalgardens.com/content/chronologyebola-virus-disease-outbreaks-1976-2014
[14] thebmj, http://www.bmj.com/content/350/bmj.h1718
[15] Investopedia, http://www.investopedia.com/terms/g/gdp.asp
[16] Brookings, http://www.brookings.edu/blogs/africa-in-focus/posts/2014/10/01-ebolaoutbreak-west-africa-sy-copley
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