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On-line monitoring of public health surveillance data.

Research output: Contribution in Book/Report/Proceedings - With ISBN/ISSN › Chapter

Published

Standard

On-line monitoring of public health surveillance data. / Diggle, Peter J.; Knorr-Held, Leo; Rowlingson, Barry et al.
Monitoring the health of populations : statistical principles and methods for public health surveillance. ed. / Ron Brookmeyer; Donna F. Stroup. Oxford: Oxford University Press, 2004. p. 233-266.

Research output: Contribution in Book/Report/Proceedings - With ISBN/ISSN › Chapter

Harvard

Diggle, PJ, Knorr-Held, L, Rowlingson, B, Su, T, Hawtin, P & Bryant, TN 2004, On-line monitoring of public health surveillance data. in R Brookmeyer & DF Stroup (eds), Monitoring the health of populations : statistical principles and methods for public health surveillance. Oxford University Press, Oxford, pp. 233-266.

APA

Diggle, P. J., Knorr-Held, L., Rowlingson, B., Su, T., Hawtin, P., & Bryant, T. N. (2004). On-line monitoring of public health surveillance data. In R. Brookmeyer, & D. F. Stroup (Eds.), Monitoring the health of populations : statistical principles and methods for public health surveillance (pp. 233-266). Oxford University Press.

Vancouver

Diggle PJ, Knorr-Held L, Rowlingson B, Su T, Hawtin P, Bryant TN. On-line monitoring of public health surveillance data. In Brookmeyer R, Stroup DF, editors, Monitoring the health of populations : statistical principles and methods for public health surveillance. Oxford: Oxford University Press. 2004. p. 233-266

Author

Diggle, Peter J. ; Knorr-Held, Leo ; Rowlingson, Barry et al. / On-line monitoring of public health surveillance data. Monitoring the health of populations : statistical principles and methods for public health surveillance. editor / Ron Brookmeyer ; Donna F. Stroup. Oxford : Oxford University Press, 2004. pp. 233-266

Bibtex

@inbook{e0a0b92840e44126beedf91647b0933e,
title = "On-line monitoring of public health surveillance data.",
abstract = "The Ascertainment and Enhancement of Gastrointestinal Infection Surveillance and Statistics (AGEISS) project aims to use spatial statistical methods to identify anomalies in the space-time distribution of nonspecific, gastrointestinal infections in the United Kingdom, using the Southampton area in southern England as a test case. Health-care providers are asked to report incident cases daily. Regionwide incident data are then sent electronically to Lancaster, where a statistical analysis of the space-time distribution of incident cases is updated. The results are then posted to a Web site with tabular, graphical and map-based summaries of the analysis. Here we use the AEGISS project to discuss the methodological issues in developing a rapid-response, spatial surveillance system. We consider simple, exploratory statistical methods together with more sophisticated methods, based on hierarchical space-time stochastic process models defined either at individual or small-area levels. The chapter is a report of work in progress. Currently, the Web-based AEGISS reporting system uses only simple summaries of the incident data, but its ultimate aim is to display the results of formal predictive inference in a hierarchical model of space-time variation in disease risk.",
keywords = "Public health surveillance. Public health surveillance - statistical methods",
author = "Diggle, {Peter J.} and Leo Knorr-Held and Barry Rowlingson and Ting-li Su and Peter Hawtin and Bryant, {Trevor N.}",
year = "2004",
language = "English",
isbn = "0195146492",
pages = "233--266",
editor = "Ron Brookmeyer and Stroup, {Donna F.}",
booktitle = "Monitoring the health of populations : statistical principles and methods for public health surveillance",
publisher = "Oxford University Press",

}

RIS

TY - CHAP

T1 - On-line monitoring of public health surveillance data.

AU - Diggle, Peter J.

AU - Knorr-Held, Leo

AU - Rowlingson, Barry

AU - Su, Ting-li

AU - Hawtin, Peter

AU - Bryant, Trevor N.

PY - 2004

Y1 - 2004

N2 - The Ascertainment and Enhancement of Gastrointestinal Infection Surveillance and Statistics (AGEISS) project aims to use spatial statistical methods to identify anomalies in the space-time distribution of nonspecific, gastrointestinal infections in the United Kingdom, using the Southampton area in southern England as a test case. Health-care providers are asked to report incident cases daily. Regionwide incident data are then sent electronically to Lancaster, where a statistical analysis of the space-time distribution of incident cases is updated. The results are then posted to a Web site with tabular, graphical and map-based summaries of the analysis. Here we use the AEGISS project to discuss the methodological issues in developing a rapid-response, spatial surveillance system. We consider simple, exploratory statistical methods together with more sophisticated methods, based on hierarchical space-time stochastic process models defined either at individual or small-area levels. The chapter is a report of work in progress. Currently, the Web-based AEGISS reporting system uses only simple summaries of the incident data, but its ultimate aim is to display the results of formal predictive inference in a hierarchical model of space-time variation in disease risk.

AB - The Ascertainment and Enhancement of Gastrointestinal Infection Surveillance and Statistics (AGEISS) project aims to use spatial statistical methods to identify anomalies in the space-time distribution of nonspecific, gastrointestinal infections in the United Kingdom, using the Southampton area in southern England as a test case. Health-care providers are asked to report incident cases daily. Regionwide incident data are then sent electronically to Lancaster, where a statistical analysis of the space-time distribution of incident cases is updated. The results are then posted to a Web site with tabular, graphical and map-based summaries of the analysis. Here we use the AEGISS project to discuss the methodological issues in developing a rapid-response, spatial surveillance system. We consider simple, exploratory statistical methods together with more sophisticated methods, based on hierarchical space-time stochastic process models defined either at individual or small-area levels. The chapter is a report of work in progress. Currently, the Web-based AEGISS reporting system uses only simple summaries of the incident data, but its ultimate aim is to display the results of formal predictive inference in a hierarchical model of space-time variation in disease risk.

KW - Public health surveillance. Public health surveillance - statistical methods

M3 - Chapter

SN - 0195146492

SP - 233

EP - 266

BT - Monitoring the health of populations : statistical principles and methods for public health surveillance

A2 - Brookmeyer, Ron

A2 - Stroup, Donna F.

PB - Oxford University Press

CY - Oxford

ER -