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Title:A study of the dynamics and genetics of COVID-19 through machine learning
Author(s):Basu, Sayantani
Advisor(s):Campbell, Roy H
Department / Program:Computer Science
Discipline:Computer Science
Degree Granting Institution:University of Illinois at Urbana-Champaign
Long Short-Term Memory (LSTM)
Mitigation Measures
Social Distancing
Multi-Layer Perceptron (MLP)
Abstract:COrona VIrus Disease (COVID-19), a disease caused due to the severe acute respiratory syndrome coronavirus 2 (SARS-CoV2) resulted in over 12 million infections and over 560,000 deaths in a worldwide pandemic. Countries all over the world carried out mitigation measures to curb the pandemic in forms of lockdowns, disinfection measures, and social distancing. We aim to study the dynamics of this disease by using a machine learning based approach using Long Short-Term Memory (LSTM) in order to evaluate the degree to which the virus spread. Our model is trained on accumulated COVID-19 cases and deaths. Parameters in our model can be adjusted to obtain predictions as required. Results have been obtained at both the country and county levels for the United States of America and some globally affected areas. We show predictions up to three different points of time – May 11, June 10, and June 30. We have also carried out a quantitative evaluation of mitigation measures in eight different counties in the United States depending on the rate of difference between a short and long window parameter based on the proposed LSTM model. The proposed LSTM model provides useful insights and can be a useful aid for various places planning strategies for mitigation and reopening. We aim to study the genetics of COVID-19 using a Multi-Layer Perceptron (MLP) model to recognize and classify COVID-19 genetic sequences at various taxonomic levels. Our model is an alignment-free method based on machine learning and natural language processing techniques that achieves reasonable performance in terms of cross-validation accuracy and time compared to the baseline model. The results from this work could potentially contribute to society during the current global concern.
Issue Date:2020-07-22
Rights Information:Copyright 2020 Sayantani Basu
Date Available in IDEALS:2020-10-07
Date Deposited:2020-08

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