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Epidemic modelling: SIRS models.

Dolgoarshinnykh, Regina G.

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Epidemic modelling: SIRS models.

Record Type:	Electronic resources : Monograph/item
Title/Author:	Epidemic modelling: SIRS models./
Author:	Dolgoarshinnykh, Regina G.
Description:	132 p.
Notes:	Source: Dissertation Abstracts International, Volume: 64-07, Section: B, page: 3352.
Contained By:	Dissertation Abstracts International64-07B.
Subject:	Statistics. -
Online resource:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3097098

Epidemic modelling: SIRS models.
Dolgoarshinnykh, Regina G.

Epidemic modelling: SIRS models. - 132 p.

Source: Dissertation Abstracts International, Volume: 64-07, Section: B, page: 3352.

Thesis (Ph.D.)--The University of Chicago, 2003.

A class of models, the SIRS models, has been proposed to study infection spread in a finite population. The letters S, I, R refer to the three possible states an individual in the population may assume in turn: susceptible, infected and recovered.Subjects--Topical Terms:

517247
Statistics.

Epidemic modelling: SIRS models.
LDR:01928nmm 2200289 4500 001 1858971
005 20041020095008.5
008 130614s2003 eng d
035 $a (UnM)AAI3097098
035 $a AAI3097098
040 $a UnM $c UnM
100 1 $a Dolgoarshinnykh, Regina G. $3 1946638
245 1 0 $a Epidemic modelling: SIRS models.
300 $a 132 p.
500 $a Source: Dissertation Abstracts International, Volume: 64-07, Section: B, page: 3352.
500 $a Adviser: Steven P. Lalley.
502 $a Thesis (Ph.D.)--The University of Chicago, 2003.
520 $a A class of models, the SIRS models, has been proposed to study infection spread in a finite population. The letters S, I, R refer to the three possible states an individual in the population may assume in turn: susceptible, infected and recovered.
520 $a This dissertation investigates the infection spread under the SIRS models. For certain values of parameters the infection becomes endemic and the proportions of the individuals in each state stay near a certain stable level. We prove the law of large numbers for the population proportions and describe the limiting system of ordinary differential equations. We also investigate the fluctuations around the stable level of the population proportions and describe a diffusion large population approximation to the system.
520 $a Of interest to epidemiologists is the time until infection disappears. Since the population is finite, in the SIRS models this happens in finite time. To estimate this time a diffusion approximation is often assumed. We discuss dangers of such approximations and solve the ensuing large deviations problem.
590 $a School code: 0330.
650 4 $a Statistics. $3 517247
650 4 $a Biology, Biostatistics. $3 1018416
690 $a 0463
690 $a 0308
710 2 0 $a The University of Chicago. $3 1017389
773 0 $t Dissertation Abstracts International $g 64-07B.
790 1 0 $a Lalley, Steven P., $e advisor
790 $a 0330
791 $a Ph.D.
792 $a 2003
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3097098