東華大學圖書館 |

Language: English

Help

回圖書館首頁

手機版館藏查詢

Back

Switch To: Labeled | MARC Mode | ISBD

Markov Chain Epidemic Models and Par...

Ige, Oluwatobiloba.

Linked to FindBook

Google Book

Amazon

博客來

Markov Chain Epidemic Models and Parameter Estimation.

Record Type:	Electronic resources : Monograph/item
Title/Author:	Markov Chain Epidemic Models and Parameter Estimation./
Author:	Ige, Oluwatobiloba.
Published:	Ann Arbor : ProQuest Dissertations & Theses, : 2020,
Description:	82 p.
Notes:	Source: Masters Abstracts International, Volume: 81-11.
Contained By:	Masters Abstracts International81-11.
Subject:	Statistics. -
Online resource:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=27956122
ISBN:	9798643170952

Markov Chain Epidemic Models and Parameter Estimation.
Ige, Oluwatobiloba.

Markov Chain Epidemic Models and Parameter Estimation. - Ann Arbor : ProQuest Dissertations & Theses, 2020 - 82 p.

Source: Masters Abstracts International, Volume: 81-11.

Thesis (M.A.)--Marshall University, 2020.

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

Over the years, various parts of the world have experienced disease outbreaks. Mathematical models are used to describe these outbreaks. We study the transmission of disease in simple cases of disease outbreaks by using compartmental models with Markov chains. First, we explore the formulation of compartmental SIS (Susceptible-Infectious-Susceptible) and SIR (Susceptible-Infectious-Recovered) disease models. These models are the basic building blocks of other compartmental disease models. Second, we build SIS and SIR disease models using both discrete and continuous time Markov chains. In discrete time models, transmission occurs at fixed time steps, and in continuous time models, transmission may occur at any time. Third, we simulate examples of SIS and SIR disease models in discrete time and in continuous time to see how the number of infected individuals changes over time. Fourth, we estimate the transmission and recovery rates from simulated data using the method of maximum likelihood. The parameter estimates in discrete time are obtained using computer algorithms and those in continuous time are obtained using both computer algorithms and theoretical formulas. Finally, we compute the bias and mean squared error of the estimators.

ISBN: 9798643170952Subjects--Topical Terms:

517247
Statistics.
Subjects--Index Terms:

Disease models

Markov Chain Epidemic Models and Parameter Estimation.
LDR:02336nmm a2200373 4500 001 2270250
005 20200921070805.5
008 220629s2020 ||||||||||||||||| ||eng d
020 $a 9798643170952
035 $a (MiAaPQ)AAI27956122
035 $a AAI27956122
040 $a MiAaPQ $c MiAaPQ
100 1 $a Ige, Oluwatobiloba. $3 3547622
245 1 0 $a Markov Chain Epidemic Models and Parameter Estimation.
260 1 $a Ann Arbor : $b ProQuest Dissertations & Theses, $c 2020
300 $a 82 p.
500 $a Source: Masters Abstracts International, Volume: 81-11.
500 $a Advisor: Mummert, Anna.
502 $a Thesis (M.A.)--Marshall University, 2020.
506 $a This item must not be sold to any third party vendors.
520 $a Over the years, various parts of the world have experienced disease outbreaks. Mathematical models are used to describe these outbreaks. We study the transmission of disease in simple cases of disease outbreaks by using compartmental models with Markov chains. First, we explore the formulation of compartmental SIS (Susceptible-Infectious-Susceptible) and SIR (Susceptible-Infectious-Recovered) disease models. These models are the basic building blocks of other compartmental disease models. Second, we build SIS and SIR disease models using both discrete and continuous time Markov chains. In discrete time models, transmission occurs at fixed time steps, and in continuous time models, transmission may occur at any time. Third, we simulate examples of SIS and SIR disease models in discrete time and in continuous time to see how the number of infected individuals changes over time. Fourth, we estimate the transmission and recovery rates from simulated data using the method of maximum likelihood. The parameter estimates in discrete time are obtained using computer algorithms and those in continuous time are obtained using both computer algorithms and theoretical formulas. Finally, we compute the bias and mean squared error of the estimators.
590 $a School code: 0817.
650 4 $a Statistics. $3 517247
650 4 $a Biostatistics. $3 1002712
653 $a Disease models
653 $a Epidemic
653 $a Markov chain
653 $a Maximum likelihood
653 $a SIR
653 $a SIS
690 $a 0463
690 $a 0308
710 2 $a Marshall University. $b Mathematics. $3 3286951
773 0 $t Masters Abstracts International $g 81-11.
790 $a 0817
791 $a M.A.
792 $a 2020
793 $a English
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=27956122