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Epidemic Modeling using Machine Learning: COVID-19.

Record Type:	Electronic resources : Monograph/item
Title/Author:	Epidemic Modeling using Machine Learning: COVID-19./
Author:	Smith, Sarah J.
Published:	Ann Arbor : ProQuest Dissertations & Theses, : 2021,
Description:	57 p.
Notes:	Source: Masters Abstracts International, Volume: 83-03.
Contained By:	Masters Abstracts International83-03.
Subject:	Computer science. -
Online resource:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=28493700
ISBN:	9798538100132

Epidemic Modeling using Machine Learning: COVID-19.
Smith, Sarah J.

Epidemic Modeling using Machine Learning: COVID-19. - Ann Arbor : ProQuest Dissertations & Theses, 2021 - 57 p.

Source: Masters Abstracts International, Volume: 83-03.

Thesis (M.S.)--University of Maryland, Baltimore County, 2021.

This item must not be sold to any third party vendors.

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 hypothesis, 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.

ISBN: 9798538100132Subjects--Topical Terms:

523869
Computer science.
Subjects--Index Terms:

Epidemic modeling

Epidemic Modeling using Machine Learning: COVID-19.
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020 $a 9798538100132
035 $a (MiAaPQ)AAI28493700
035 $a AAI28493700
040 $a MiAaPQ $c MiAaPQ
100 1 $a Smith, Sarah J. $3 3690580
245 1 0 $a Epidemic Modeling using Machine Learning: COVID-19.
260 1 $a Ann Arbor : $b ProQuest Dissertations & Theses, $c 2021
300 $a 57 p.
500 $a Source: Masters Abstracts International, Volume: 83-03.
500 $a Advisor: Dutt, Abhijit.
502 $a Thesis (M.S.)--University of Maryland, Baltimore County, 2021.
506 $a This item must not be sold to any third party vendors.
520 $a 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 hypothesis, 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.
590 $a School code: 0434.
650 4 $a Computer science. $3 523869
650 4 $a Epidemiology. $3 568544
650 4 $a Artificial intelligence. $3 516317
650 4 $a Demography. $3 614991
653 $a Epidemic modeling
653 $a Machine learning
653 $a COVID-19
653 $a Population density
653 $a Morbidity rate
653 $a Infection rate
653 $a Predictive modeling
690 $a 0984
690 $a 0800
690 $a 0766
690 $a 0938
710 2 $a University of Maryland, Baltimore County. $b Computer Science. $3 1018441
773 0 $t Masters Abstracts International $g 83-03.
790 $a 0434
791 $a M.S.
792 $a 2021
793 $a English
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=28493700