Volgenau School of Engineering Graduate Research
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Browsing Volgenau School of Engineering Graduate Research by Subject "COVID-19"
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Item Analysis of Social Media Comments(2021-04-28) Mohamed, MazenFighting the COVID-19 is widespread acceptance of the covid-19 vaccines. Achieving the widespread uptake might be challenging and may be obstructed by the misinformation prevalent in social media. The social media platforms have become a common source of information and disinformation on vaccines. Vaccine’s hesitancy is more prevalent in social media, especially Twitter. Machine learning models can explain the social media comments about this topic.Item COVID-19 Vaccine Data Review and Reactions on Social Media(2021-04-28) Bhandari, Shova; Mohamed, Mazen; Maupin, JakeThe USA is one of the hardest hit areas by COVID-19. As of March 13th, 2021, 29.5M people were infected and 534K have lost their lives [1]. The USA government has authorized Pfizer and Moderna mRNA COVID-19 vaccines for the prevention of Coronavirus. However, these vaccines are allocated to certain groups and are not available to the public yet. Vaccine’s demand is skyrocketing as the United States of America is unable to contain the virus, and a new more contagious and deadlier Covid-19 variant is emerging. Given the impact of the COVID-19 pandemic, it is imperative to efficiently distribute vaccines to contain and eventually eradicate the virus. It is important to identify the hardest hit region/state that is still struggling to fight and protect their residence from viruses. The main question we are attempting to answer is how we can use COVID-19 data to identify focal points for COVID-19 breakout and formulate an efficient and speedy response. In this research we analyze COVID-19 vaccine trend.Item Data Analytics Research for COVID19 Pandemic(2021-04-28) Tran, ThaoThe importance of learning about where and how coronavirus has entered the United States will help further our understanding of the disease. According to CDC, the first coronavirus case in the US has been identified in Washington state, and that was due to air travel from Wuhan, China. The most common way Covid-19 can spread is by human interaction, through respiratory droplets such as talking, coughing, sneezing, and more. We apply machine learning models to answer this problem.Item Financial Impact of Lockdown on Big Business Versus Small Business based on Data Analytics(2021-04-28) Chekuri, Rohit Varma; Wilkinson, Brady; Xuan, Bach LeThough everyone was affected by the COVID-19 pandemic in their own ways, some suffered harder financial loss than others. Smaller-sized businesses that once thrived from crowds of people faced a sudden disappearance in those same people that kept the business financially alive. With little other resources to keep afloat, many small businesses faced heavy losses. Big businesses also faced difficulty, some industries more than others. While airports were empty and airline companies like United and JetBlue faced heavy losses, companies like Amazon or Google seemingly thrived. The different financial impacts that big and small businesses endured will be analyzed in this paper. The material will be presented in several steps. First the topic will be introduced, followed by an analysis of related work. Next, the problem is defined, followed by outlining the scope and analysis techniques. Analysis follows the analysis techniques description, with work to do and conclusion sections following.Item Flight Data to Predict COVID-19 Cases by Machine Learning(2021-04-28) Alshabana, GhadahCoronavirus can be transmitted through the air in close proximity to infected persons. Commercial aircraft is a likely way to both transmit the virus among passengers and move the virus between locations. Our team utilized machine learning to determine if the number of flights into the Washington DC Metro Area had an effect on the number of cases and deaths reported in the city and surrounding area.Item Machine learning and NLP Models to Predict COVID-19 Cases in US(2021-04-28) Alshabana, Ghadah; Chitimalla, Ashritha; Tran, Thao; Thompson, MichaelAir travel is an important factor to spread of the coronavirus from more infected regions to those with limited or no prior infections. The importance of learning about where and how coronavirus has entered the United States will help further our understanding of the disease. Air travelers can come from countries or areas with a high rate of infection and may very well be at risk of being exposed to the virus. Therefore, as they reach the United States, the virus could easily spread. In our analysis, we intend to use Machine learning and NLP models based on CDC data to determine if the number of flights into or out of the Washington DC metro area may have impacted the number of coronavirus deaths reported in those counties.Item Machine Learning Application in Health(2022-06-10) Alshabana, Ghadah; Tran, Thao; Chitimalla, Ashritha; Thompson, MichaelCoronavirus can be transmitted through the air by close proximity to infected persons. Commercial aircraft are a likely way to both transmit the virus among passengers and move the virus between locations. The importance of learning about where and how coronavirus has entered the United States will help further our understanding of the disease. Air travelers can come from countries or areas with a high rate of infection and may very well be at risk of being exposed to the virus. Therefore, as they reach the United States, the virus could easily spread. On our analysis, we utilized machine learning to determine if the number of flights into the Washington DC Metro Area had an effect on the number of cases and deaths reported in the city and surrounding area.Item Machine Learning Model to Detect Emergency in the Global Pandemic(2021-01-11) Raju, RiniIt is crucial to use advanced machine learning models to improve disaster and emergency response in critical events around the world. In this paper, we introduce a new model, which can highlight the essential help that people need in times of emergency. Based on the user comments, we choose the emergency response that can use the optimal resources to address the maximum needs. The new features in the model help to analyze each person's response from political, social, and health perspectives. This approach helps to recognize different types of users to improve emergency response in the time of the global pandemic. Also, collecting pandemic data from different online resources, makes this research more powerful in feature extraction to improve the model accuracy based on emergency data. This model can help health applications to improve disaster response time and services.Item Machine Learning Models and Transfer Models for Measuring Impact of the Pandemic on Communities(2021-04-28) Berdibekov, TimurThis paper studies the relationship between the 2019 Novel Coronavirus (COVID-19) pandemic, its public health and economic impact, and rates of economic inclusion and access to banking services throughout the pandemic in the United States. For select U.S. counties, this paper examined COVID-19 infection and mortality rates, unemployment rates and the number of bank closures, and the rate of economic inclusion to discover any notable relationships. Lastly, select features are evaluated for the predictive capability of the county and county-equivalent rates of unbanked households to better inform policy making given that the unbanked household rates are unknown for most counties.Item Machine learning models for Prediction of the need for future Covid-19 vaccine booster(2021-04) Marzook, Ahmad Al; Xu, Ge; Jagannath, Prajna ShettyAbout 4 million Virginia citizens are fully vaccinated against COVID-19. According to medical data from nations like Israel and the United Kingdom, all those people may require another shot in the near future, at least when keeping transmis- sible diseases at bay. Health officials are looking at whether a booster shot is needed to ensure the vaccine’s effectiveness. To prepare it for predictions, we will develop a new simple logistic regression model. Make prediction models using classification and probability. With the vaccine immune response lifetime taken into account, we will run our model to forecast the completely vaccinated number timeline and observe when it achieves the herd immunity percentage of the overall Virginia population. The percentage of people that need to be vaccinated to attain COVID- 19 herd immunity is still up for dispute. The goal of this study was to forecast when everyone would be fully immunized. COVID-19 vaccination loses nearly half of its defense antibodies every 108 days, according to Elie Dolgin. As a result, immunizations that initially provided 90% protection against mild episodes of the disease may only provide 70% protection after 6 or 7 months. The percentage of people that need to be vaccinated to attain COVID-19 herd immunity is still being disputed.Item Neurological Manifestation of COVID-19: An Updated Literature Review(2022-05) Eltayeb, Sohaib; Peixoto, NathaliaSARS-CoV-2, which causes the disease known as Coronavirus Disease 2019 (COVID-19), is a novel coronavirus that arose in Wuhan, China in 2019. Within a short time, it rapidly spread across the world and has been declared a global pandemic by the World Health Organization (WHO) due to its severe morbidity and mortality rate. This virus left many scientists and biomedical engineers perplexed due to the various uncertainties about its infection rate as new COVID-19 variants arise. However, attempts to contain the virus are ongoing all over the world. Some of the most common symptoms of COVID- 19 include fever, dry cough, and fatigue. However, some physicians in affected areas have discovered that some patients that were diagnosed with COVID-19 did not exhibit these expected respiratory symptoms at the time of diagnosis, but rather these patients displayed only neurological symptoms as their initial symptoms. For instance, the symptoms range from non-specific to more particular, such as headaches or dizziness which were one of the more common symptoms, to more complicated symptom onset such as convulsions, cerebrovascular and peripheral diseases.Item Sentiment Analysis Methods to Mitigate Negative Effect of the COVID-19 Pandemic(2021-01-11) Mohamud, Sofia AThe goal of this research is to determine crucial factors that played a role in the number of confirmed COVID-19 infections within a given location. We hypothesize that political bias plays a significant role in the rise of COVID-19 cases globally and nationally; specifically, in overriding scientific reasoning for the delay or lack of deploying national policies to address the pandemic. Methods: To determine the validity of our hypothe- sis, we performed a literature review that identified statistical information on 1) the origins of the virus, 2) the lethality of the virus, and 3) potential parties responsible for the creation and release of the virus. In addition to the literature review, our team performed a behavioral analysis using information extracted from social media platforms to identify and determine behavior patterns associated with specific words related to the virusItem Stop COVID-19 Cases by Using Data Analytics Approach(2021-04-28) Chitimalla, AshrithaThe most common way Covid-19 can spread is by human interaction, through respiratory droplets such as talking, coughing, sneezing, and more. Air travelers can come from countries or areas with a high rate of infection and may very well be at risk of being exposed to the virus. Therefore, as they reach the United States, the virus could easily spread. This project predicts COVID19 cases based on flight data.