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Epidemic Modeling using Machine Learning: COVID-19.

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Epidemic Modeling using Machine Learning: COVID-19./
作者:	Smith, Sarah J.
出版者:	Ann Arbor : ProQuest Dissertations & Theses, : 2021,
面頁冊數:	57 p.
附註:	Source: Masters Abstracts International, Volume: 83-03.
Contained By:	Masters Abstracts International83-03.
標題:	Computer science. -
電子資源:	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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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
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690 $a 0800
690 $a 0766
690 $a 0938
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773 0 $t Masters Abstracts International $g 83-03.
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