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Statistical aspects of quarantine in...

The Johns Hopkins University.

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Statistical aspects of quarantine interventions and incubation periods in epidemics.

Record Type:	Language materials, printed : Monograph/item
Title/Author:	Statistical aspects of quarantine interventions and incubation periods in epidemics./
Author:	You, Xiaojun.
Description:	109 p.
Notes:	Adviser: Ron Brookmeyer.
Contained By:	Dissertation Abstracts International68-11B.
Subject:	Biology, Biostatistics. -
Online resource:	http://pqdd.sinica.edu.tw/twdaoeng/servlet/advanced?query=3288561
ISBN:	9780549314080

Statistical aspects of quarantine interventions and incubation periods in epidemics.
You, Xiaojun.

Statistical aspects of quarantine interventions and incubation periods in epidemics. - 109 p.

Adviser: Ron Brookmeyer.

Thesis (Ph.D.)--The Johns Hopkins University, 2008.

Quarantine of contacts of known SARS patients proved effective at controlling further transmissions during the 2003 outbreak. However, little work has been done on determining the optimal quarantine length. Our initial work analyzed the impact of using the maximum reported incubation period from an outbreak to guide quarantine policy. To do this, we quantified the probability of an infectious case transmitting disease after such quarantine. Methods were developed using order statistics theory with validation via a simulation study. Later we derived incubation-period-based methods to calculate the desired quarantine length, using the maximum likelihood estimation method built on truncated density function. The effectiveness of these latter methods was evaluated using simulation studies and applied to an actual in-flight SARS outbreak data. Because the incubation period is an important quantity in developing effective epidemic control programs, we proposed an E-M algorithm based method to estimate incubation period for epidemics where confirmed cases could be infected by more than one infectious source; this method was also applied to the in-flight SARS outbreak data. When a chain of infection happens, another possible problem arises that we only have information about the times of clinical illness onset but not the times of initial infection. We developed statistical approaches for using epidemic chains of symptomatic cases to estimate the distribution of incubation periods. We used the geometric distribution to approximate the date of infection, and derived estimators of the distribution parameters. We also examined imputation methods. We performed simulation to compare the performance of the various estimators.

ISBN: 9780549314080Subjects--Topical Terms:

1018416
Biology, Biostatistics.

Statistical aspects of quarantine interventions and incubation periods in epidemics.
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020 $a 9780549314080
035 $a (UMI)AAI3288561
035 $a AAI3288561
040 $a UMI $c UMI
100 1 $a You, Xiaojun. $3 1023414
245 1 0 $a Statistical aspects of quarantine interventions and incubation periods in epidemics.
300 $a 109 p.
500 $a Adviser: Ron Brookmeyer.
500 $a Source: Dissertation Abstracts International, Volume: 68-11, Section: B, page: 7052.
502 $a Thesis (Ph.D.)--The Johns Hopkins University, 2008.
520 $a Quarantine of contacts of known SARS patients proved effective at controlling further transmissions during the 2003 outbreak. However, little work has been done on determining the optimal quarantine length. Our initial work analyzed the impact of using the maximum reported incubation period from an outbreak to guide quarantine policy. To do this, we quantified the probability of an infectious case transmitting disease after such quarantine. Methods were developed using order statistics theory with validation via a simulation study. Later we derived incubation-period-based methods to calculate the desired quarantine length, using the maximum likelihood estimation method built on truncated density function. The effectiveness of these latter methods was evaluated using simulation studies and applied to an actual in-flight SARS outbreak data. Because the incubation period is an important quantity in developing effective epidemic control programs, we proposed an E-M algorithm based method to estimate incubation period for epidemics where confirmed cases could be infected by more than one infectious source; this method was also applied to the in-flight SARS outbreak data. When a chain of infection happens, another possible problem arises that we only have information about the times of clinical illness onset but not the times of initial infection. We developed statistical approaches for using epidemic chains of symptomatic cases to estimate the distribution of incubation periods. We used the geometric distribution to approximate the date of infection, and derived estimators of the distribution parameters. We also examined imputation methods. We performed simulation to compare the performance of the various estimators.
590 $a School code: 0098.
650 4 $a Biology, Biostatistics. $3 1018416
650 4 $a Health Sciences, Epidemiology. $3 1019544
690 $a 0308
690 $a 0766
710 2 $a The Johns Hopkins University. $3 1017431
773 0 $t Dissertation Abstracts International $g 68-11B.
790 $a 0098
790 1 0 $a Brookmeyer, Ron, $e advisor
791 $a Ph.D.
792 $a 2008
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoeng/servlet/advanced?query=3288561