國立高雄大學圖資館 |

語系: 繁體中文

說明(常見問題)

圖資館首頁

登入

回首頁

切換: 標籤 | MARC模式 | ISBD

Automating social network models for...

Abernethy, Neil Franklin.

Automating social network models for tuberculosis contact investigation.

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Automating social network models for tuberculosis contact investigation.
作者:	Abernethy, Neil Franklin.
面頁冊數:	120 p.
附註:	Adviser: Peter M. Small.
附註:	Source: Dissertation Abstracts International, Volume: 66-08, Section: B, page: 4176.
Contained By:	Dissertation Abstracts International66-08B.
標題:	Health Sciences, Public Health.
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3187255
ISBN:	9780542294549

Automating social network models for tuberculosis contact investigation.
Abernethy, Neil Franklin.

Automating social network models for tuberculosis contact investigation. - 120 p.

Adviser: Peter M. Small.

Thesis (Ph.D.)--Stanford University, 2005.

Contact investigation to locate individuals at risk of acquiring tuberculosis can be an effective procedure, yet the resulting data can be unstandardized and difficult for disease investigators to synthesize when searching for transmission patterns. Social networks have been increasingly used to visualize these transmission patterns and to guide further contact investigation. Although off-the-shelf software exists to display networks, use of these tools requires technical knowledge, data manipulation, and database experience. In this dissertation, I hypothesize that software enabling interactive querying and visualization of networks will improve health workers' access to this data, and that contact investigation data are sufficiently standardized to utilize social network models.

ISBN: 9780542294549Subjects--Topical Terms:

212500
Health Sciences, Public Health.

Automating social network models for tuberculosis contact investigation.
LDR:03422nmm _2200301 _450 001 170831
005 20061228142257.5
008 090528s2005 eng d
020 $a 9780542294549
035 $a 00242861
040 $a UnM $c UnM
100 0 $a Abernethy, Neil Franklin. $3 244862
245 1 0 $a Automating social network models for tuberculosis contact investigation.
300 $a 120 p.
500 $a Adviser: Peter M. Small.
500 $a Source: Dissertation Abstracts International, Volume: 66-08, Section: B, page: 4176.
502 $a Thesis (Ph.D.)--Stanford University, 2005.
520 # $a Contact investigation to locate individuals at risk of acquiring tuberculosis can be an effective procedure, yet the resulting data can be unstandardized and difficult for disease investigators to synthesize when searching for transmission patterns. Social networks have been increasingly used to visualize these transmission patterns and to guide further contact investigation. Although off-the-shelf software exists to display networks, use of these tools requires technical knowledge, data manipulation, and database experience. In this dissertation, I hypothesize that software enabling interactive querying and visualization of networks will improve health workers' access to this data, and that contact investigation data are sufficiently standardized to utilize social network models.
520 # $a Formal evaluation of the software supports the primary hypothesis: users saved time performing common queries with Outbreak Investigator, and their overall satisfaction was improved over existing software. The standardization of contact investigation data is addressed with a nationwide survey of contact investigation forms, which are statistically analyzed to identify a core set of fields appropriate for social network models. The results points to a path of standardization and greater data consistency. This dissertation contributes to public health informatics by integrating social network modeling tools with under-utilized public health data, enabling widespread evaluation of a promising technique to improve outbreak management in tuberculosis and other diseases.
520 # $a The utility of social network models for contact investigation is first shown though case studies in San Francisco in which manually-generated networks are used to describe the social structure underlying two distinct outbreaks in homeless shelter and daycare settings. The nature of household contacts is also explored, showing a possible relationship between housing density and incidence of tuberculosis. Having provisionally demonstrated the utility of these models, I developed the Outbreak Investigator, a user-friendly tool that uses graph layout algorithms to automatically produce interactive social network graphics based on natural language queries. The tool is designed to simplify adaptation to alternate database architectures, and the user interface uses pre-defined queries developed in consultation with infectious disease controllers.
590 $a School code: 0212.
650 # 0 $a Health Sciences, Public Health. $3 212500
650 # 0 $a Statistics. $3 182057
650 # 0 $a Computer Science. $3 212513
690 $a 0463
690 $a 0573
690 $a 0984
710 0 # $a Stanford University. $3 212607
773 0 # $g 66-08B. $t Dissertation Abstracts International
790 $a 0212
790 1 0 $a Small, Peter M., $e advisor
791 $a Ph.D.
792 $a 2005
856 4 0 $u http://libsw.nuk.edu.tw:81/login?url=http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3187255 $z http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3187255