On-line Monitoring of Public Health Surveillance Data

Peter Diggle; Leo Knorr-Held; Barry Rowlingson; Ting Li Su; Peter Hawtin; Trevor N. Bryant

doi:10.1093/acprof:oso/9780195146493.003.0009

On-line Monitoring of Public Health Surveillance Data

Peter Diggle
, Leo Knorr-Held
, Barry Rowlingson
, Ting Li Su
, Peter Hawtin
, Trevor N. Bryant

Lancaster University
Ludwig Maximilian University of Munich
UK Health Security Agency
University of Southampton

Research output: Chapter in Book/Report/Conference proceeding › Chapter › peer-review

3 Citations (Scopus)

Abstract

The Ascertainment and Enhancement of Gastrointestinal Infection Surveillance and Statistics (AEGISS) 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. This chapter uses the AEGISS project to discuss the methodologic issues in developing a rapidresponse, spatial surveillance system. It considers 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.

Original language	English
Title of host publication	Monitoring the Health of Populations : Statistical Principles and Methods for Public Health Surveillance
ISBN (Electronic)	9780199864928
DOIs	https://doi.org/10.1093/acprof:oso/9780195146493.003.0009
Publication status	Published - 1 Sept 2009
Externally published	Yes

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 3 Good Health and Well-being

Keywords

AEGISS
Data collection
Public health monitoring
Surveillance system
United Kingdom

Access to Document

10.1093/acprof:oso/9780195146493.003.0009

Cite this

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title = "On-line Monitoring of Public Health Surveillance Data",

abstract = "The Ascertainment and Enhancement of Gastrointestinal Infection Surveillance and Statistics (AEGISS) 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. This chapter uses the AEGISS project to discuss the methodologic issues in developing a rapidresponse, spatial surveillance system. It considers 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.",

keywords = "AEGISS, Data collection, Public health monitoring, Surveillance system, United Kingdom",

author = "Peter Diggle and Leo Knorr-Held and Barry Rowlingson and Su, \{Ting Li\} and Peter Hawtin and Bryant, \{Trevor N.\}",

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doi = "10.1093/acprof:oso/9780195146493.003.0009",

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AU - Diggle, Peter

AU - Knorr-Held, Leo

AU - Rowlingson, Barry

AU - Su, Ting Li

AU - Hawtin, Peter

AU - Bryant, Trevor N.

PY - 2009/9/1

Y1 - 2009/9/1

N2 - The Ascertainment and Enhancement of Gastrointestinal Infection Surveillance and Statistics (AEGISS) 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. This chapter uses the AEGISS project to discuss the methodologic issues in developing a rapidresponse, spatial surveillance system. It considers 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.

AB - The Ascertainment and Enhancement of Gastrointestinal Infection Surveillance and Statistics (AEGISS) 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. This chapter uses the AEGISS project to discuss the methodologic issues in developing a rapidresponse, spatial surveillance system. It considers 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.

KW - AEGISS

KW - Data collection

KW - Public health monitoring

KW - Surveillance system

KW - United Kingdom

U2 - 10.1093/acprof:oso/9780195146493.003.0009

DO - 10.1093/acprof:oso/9780195146493.003.0009

M3 - Chapter

SN - 9780195146493

BT - Monitoring the Health of Populations : Statistical Principles and Methods for Public Health Surveillance

ER -

On-line Monitoring of Public Health Surveillance Data

Abstract

UN SDGs

Keywords

Access to Document

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