EPIDEMIC MODELING USING MACHINE LEARNING: COVID-19
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Computer Science and Electrical Engineering
Program
Computer Science
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This item may be protected under Title 17 of the U.S. Copyright Law. It is made available by UMBC for non-commercial research and education. For permission to publish or reproduce, please see http://aok.lib.umbc.edu/specoll/repro.php or contact Special Collections at speccoll(at)umbc.edu
This item may be protected under Title 17 of the U.S. Copyright Law. It is made available by UMBC for non-commercial research and education. For permission to publish or reproduce, please see http://aok.lib.umbc.edu/specoll/repro.php or contact Special Collections at speccoll(at)umbc.edu
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Abstract
This research seeks to determine if population density effects the morbidity or infection rate of COVID-19 in a given region. It is driven by the research question: What underlying conditions make a country or region more vulnerable to COVID-19? I hypothesize that, population density significantly impacts the rate of COVID-19 cases and deaths for a given region. As such, population density is a key feature for training a predictive model. To test this hypotheses, I trained a series of predictive models. Each model is trained on 28 days of data and forecasts 14 days. Models are trained with and without population density. I evaluated all models using Mean Absolute Error, Root Square Mean Error, and R2. These metrics provide a means to evaluate and compare the performance of the models. The results indicate that a correlation exists between population density and the rate of COVID-19 cases and deaths for a given region. Additionally, it demonstrates the utility of using population density as a feature.