Assignment 1: Ebola in West Africa
Sarena Oberoi
Febuary 13
Problem Statement
The Ebola virus is an extremely dangerous disease that targets the organs, the immune system, and causes excessive bleeding. Since the Ebola virus targets so many specific parts of the body (including white blood cells), the disease is extremely fatal with no guaranteed treatment. Although scientists are researching various treatment plans and cures, the fatality rate is currently very high. Many countries around the world including parts of West Africa such as Sierra Leone, Guinea, and Liberia, have seen a significant number of deaths due to the threatening virus. During the time of the epidemic, there was a dramatic decline in the economies of the countries affected. Trading and tourism were also greatly affected which directly resulted in a large regression of the economy. Furthermore, peoples freedoms were being restricted, as citizens were unable to go about their everyday lives including going to their jobs. It is important to address the epidemic in order to see economic success and make the citizens feel safe in their home countries once again. In America, medical technologies are extremely advanced and there are a great deal of sophisticated infection control policies. Unfortunately, the quality of life and medical technologies are not as refined in Western Africa which makes the disease more transmissible. Those with a stronger immune system typically have a better chance of survival, but this is unlikely in select countries in Africa. It is possible to utilize data science methods in order to track the spread of the disease. By using different techniques and measures such as call detail records, geospatial data, and modeling the data using diverse models such as the ‘Poisson Model,’ it is possible to not only know where the disease is spreading, but why it is spreading. By keeping a close eye and surveillance measures on countries that are still at risk for the Ebola virus, we can contain the virus all together and keep it from becoming an issue once again (World Health Organization, 2020).
Annotations
1) Ebola Virus Disease Distribution Map: Cases of Ebola Virus Disease in Africa Since 1976. (2019, June 19). Center for Disease Control and Prevention. Retrieved from http://www.cdc.gov/vhf/ebola/history/distribution-map.html
In 2014, the Ebola virus was extremely prevalent in distinct countries in West Africa. The Ebola virus is a disease that is easily spread and is even more common in highly dense areas. Ebola is transmitted through direct contact with a number of body fluids including the blood and vomit of an infected person. Some of the main symptoms of the disease include excessive vomiting and bleeding, diarrhea, and rashes. If Ebola is diagnosed early on, the chance of survival is much higher. However, since the early symptoms of the virus are so similar to the symptoms of other common diseases, it is very difficult to diagnose Ebola in the early stages. There have been studies done regarding an extremely effective vaccine used for the virus, but the vaccine was not introduced to many African countries until after the large outbreak. Although there is currently no single treatment for curing the Ebola virus, there are additional methods used to prevent the disease from being fatal. Some of these procedures include blood transfusions and drug therapies. Following the massive outbreak of Ebola cases in Western Africa, the government decided to take large preventive measures in order to prevent such a large tragedy from occurring again. Research has shown that one of the main transmission methods of Ebola is through direct contact with the meat of animals including bats and apes. It is extremely important that individuals dealing with these animals ensure that they are not directly touching the infected meat. Furthermore, it is important that individuals refrain from contact with those individuals that are infected by the virus. By ensuring that individuals are thoroughly washing their hands, especially after coming in contact with someone infected, it will be easier to prevent the transmission of the disease.
2) Peak, C. M., Wesolowski, A., Erbach-Schoenberg, E. Z., Tatem, A. J., Wetter, E., Lu, X., Bengtsson, L. (2018). Population mobility reductions associated with travel restrictions during the Ebola epidemic in Sierra Leone: use of mobile phone data. International Journal of Epidemiology, 47(5), 1562–1570. doi: 10.1093/ije/dyy095
In the article “Population Mobility Reductions Associated with Travel Restrictions during the Ebola Epidemic in Sierra Leone: Use of Mobile Phone Data,” the authors expand on ‘big data’ approaches including the use of CDR’s and cell phone usage to track how the citizens of Sierra Leone travel during the Ebola outbreak. In March of 2015, a travel restriction was placed on Sierra Leone, in which citizens were asked to stay at home, in an attempt to eradicate the Ebola virus. Since many citizens in Sierra Leone have access to mobile devices, researchers were able to utilize ‘call detail records’ (CDR’s) in order to track the movement of the citizens of Sierra Leone, and in turn, track the transmission of the disease and its movement patterns. In his book “Development as Freedom,” Amartya Sen expands on his definition of human development which he defines as increasing the freedoms and choices of humans. Sen touches on some of the main types of freedoms which include social opportunities, protective security, political freedoms, economic facilities, and transparency guarantees. The article can be tied back to Sen’s definitions of social opportunities and protective security. In Sen’s book, he explains that social opportunities are integral in increasing the well-being of an individual, as well as their opportunity to live happily, by allowing them to receive an education and have access to health care. With the Ebola virus being so prevalent, individuals were deprived of this right, which also directly resulted in a decrease in well-being and the opportunity to live a happy life. Sen also touches on protective security which he describes as “a social safety net” in which the citizens are protected from disease, starvation, and death. In this article, the government protects the citizens from death by attempting to contain and eradicate the Ebola virus. The government in Sierra Leone along with other countries has taken a number of preventative measures (such as travel bans) in order to contain the spread of the disease. Along with this, Sen elucidates the idea of increasing each individuals quality of life. Those living with the Ebola virus have limited opportunities and capabilities and are restricted from living the best life they can live. When Sen discusses the quality of life, he also touches on life expectancy in regards to development and freedom, as a longer life expectancy is crucial in our understanding of development.
The dimension of human development that the authors emphasize is a long and healthy life. A long and healthy life is based on the life expectancy of each individual. After the Ebola virus became prevalent, life expectancy decreased dramatically. The sustainable development goal focused on was healthy lives and well-being which was emphasized by attempting to increase life expectancy by eradicating the Ebola outbreak all together. The researchers looked into global positioning systems and call detail records (along with cell towers) to track the movement of citizens. These CDR’s had information regarding the exact location the call was made. This makes the movement and location of citizens more accessible. The radiation model (grounded on phone data) was a method used in order to track and model the movements of individuals from various locations. This model works by calculating the configurations of movement based on past patterns. The researchers main goal was to investigate migration patterns of the citizens in Sierra Leone in order to see how many citizens migrate following a travel ban, in order to eradicate the Ebola virus. In sum, the researchers are investigating how migration patterns change (using cell phone data) after a travel ban has been put on the country.
3) Zinszer, K., Morrison, K., Verma, A., & Brownstein, J. S. (2017). Spatial Determinants of Ebola Virus Disease Risk for the West African Epidemic. PLoS Currents. doi: 10.1371/currents.outbreaks.b494f2c6a396c72ec24cb4142765bb95
Following the initial outbreak of the Ebola virus, researchers looked into specific outbreaks in Western African including Guinea, Liberia, and Sierra Leone. They wanted to understand if there was a relationship between the Ebola virus and different environmental and socio-demographic variables. Researchers looked into a variety of covariates including average rainfall, average elevation, households without toilets, households with drinking water, and households with radios, and compared these findings to the number of total confirmed Ebola outbreaks. The number of Ebola cases for each covariate was found using demographic and health surveys, as well as satellite sensor-derived data. One of the most surprising findings showed that ownership of a radio was a strong predictor of whether or not the individual/household would get the Ebola virus. This is because information regarding the spread of the virus and prevention was disseminated using radio campaigns. Therefore, those without a radio were unable to receive this vital information. In order to obtain these calculations, the total number of Ebola cases for each covariate, as well as the total population for each district was recorded and placed into a Poisson model. The data was further described using a spatial autoregressive modelling approach which was used to compare the results (number of observed Ebola cases) seen from the spatial and non-spatial disparity for each covariate. One of the most surprising findings showed that ownership of a radio was a strong predictor of whether or not the individual would get the Ebola virus. This is because information regarding the spread of the virus and information about prevention was spread using radio campaigns. Another surprising discovery showed that those living in less dense areas regarding population were more prone to the Ebola virus because the hospitals and medical care is not as advanced in these less populated areas.
Amartya Sen speaks a great deal regarding how development is based on the freedoms individual people have. This article can relate to Sen’s detailed description of social opportunities because the government worked very hard to educate the public and keep citizens updated about the latest news regarding the virus. All of these efforts were made so that more citizens didn’t have to be impacted by the dangerous disease. Furthermore, the citizens were also provided the freedom of transparency. The government was very clear with its people about how dangerous the virus was and what precautions needed to be taken. Giving the citizens the opportunity to live a long and healthy life was the primary goal of the government. I believe that the authors are investigating the distribution and patterns of wealth within different countries. Those without a radio may not have the money to afford one which could directly relate to not being able to afford healthcare or the necessary treatments. In sum, the authors are investigating how various covariates effect the prevalence of Ebola, as well as how individuals can look at data to see how likely an Ebola outbreak is based on living conditions, in turn decreasing the prevalence of the disease.
4) Jain, A. (2017). Predictive Models for Ebola Using Machine Learning Algorithm (Doctoral dissertation). Retrieved from http://fau.digital.flvc.org/islandora/object/fau%3A38026/datastream/OBJ/view/Predictive_Models_for_Ebola_using_Machine_Learning_Algorithms.pdf
With the Ebola virus spreading so rapidly, it is crucial to understand important information regarding the future of the virus such as where the virus is going and how fast it is moving. Researchers have done extensive research in order to create models that track these integral characteristics. These models are far more specific than other models, in that the study focuses on individuals to see how smaller groups (families) will be affected if they come in contact with an infected individual. This specific study utilized a machine learning algorithm to achieve this goal. The foundation of this model was built on three distinct layers: obtaining data, machine learning algorithm, and looking at results, respectively. In order to obtain the data, various sources were looked into. The Humanitarian Data Exchange made up a large portion of the data collected (number of individuals with Ebola), but other sources such as data from doctors that conducted medical research on countries with Ebola were used as well. Random forest, among other models, was used as the machine learning aspect of the model. The random forest model is a model that has the ability to take very large sets of data (variables) and give a prediction about which of these variables is the most important for classification. A large range of trees were used in order to predict the likelihood of an individual getting Ebola: 100 trees to 1500 trees. The data shows that the accuracy of using a random forest model was slightly lower than other models such as a logistic regression model. However, even with the random forest model it is easier to see how likely it is for an individual to be infected with the Ebola virus.
This article can be tied into Amartya Sen and his view of individual freedoms and their integral role in development. Those infected with the Ebola virus are unable to support themselves and soon become ostracized from society. This inhibits these individuals’ freedoms and rights to live happily. The main goal is for individuals to live a long and healthy life where they are at a state of well-being. Therefore, in order to be more aware and prepared, the authors were attempting to find a sound method through machine learning that allowed them to predict the likelihood of an individual contracting Ebola.
5) Fiorillo, G., Bocchini, P., & Buceta, J. (2018). A Predictive Spatial Distribution Framework for Filovirus-Infected Bats. Scientific Reports, 8(1). doi: 10.1038/s41598-018-26074-4
The Ebola epidemic in 2014 was one with the highest fatality rate, with more than 28,000 reported cases. With the disease being so prevalent around the world, researchers took it upon themselves to see which animal carriers were spreading the disease the most rapidly. After extensive research, it was found that Filovirus-infected bats, along with differences in the environment, were interconnected to the massive outbreak of the virus. The researchers wanted to see the correlation between the environment and the number of infected bats observed. Therefore, a geographical map was created that displayed the distribution of four different kinds of infected bats. Bat colonies were observed using surveillance studies, and a SIR model (predicts number of infected individuals) which was used to infer which infectious states the bats were in. In order to display to relationship between environmental characteristics and the presence of bats, a few regression models were used: MLN (multi-linear model), a GLC (generalized multi-linear model), and GPC (polynomial model). These models displayed a graph to show the relationship between the two variables (environmental characteristics and presence of bats). The data regarding the environmental characteristics was obtained using the ‘Google Earth Engine database collection.’ A few of the environmental characteristics being tested were precipitation, daily air temperature, and land cover index. A very important result observed from conducting the study showed that a greater number of infected bats were found where land vegetation was the highest. All of the data regarding the land and terrain was obtained using satellite imagery but was soon converted into various indexes (depending on which variable used).
Amartya Sen emphasizes the importance of increasing individuals well-being, freedoms, and health in order to see economic success. With the pervasiveness of the Ebola virus, it was difficult for citizens to feel safe and expand their freedoms, which led to a large decline in the economy. However, with the research being done on the bats and the environment, obtaining information regarding the most common transmission cause will allow individuals to be more aware of where they might be more likely to contract the disease. This knowledge will prevent the number of infected individuals from increasing which will soon lead to an increase in not only life expectancy, but also the well-being and happiness of the people. The scientists are attempting to allow more individuals to live a long and healthy life, without the virus. In sum, the authors are investigating how the environment effects the number of Ebola infected bats found in a variety of locations.