832Detecting spatial clusters of human salmonellosis in Victoria. (2nd September 2021)
- Record Type:
- Journal Article
- Title:
- 832Detecting spatial clusters of human salmonellosis in Victoria. (2nd September 2021)
- Main Title:
- 832Detecting spatial clusters of human salmonellosis in Victoria
- Authors:
- Morgan, Hannah
Cutcher, Zoe
Firestone, Simon
Stevenson, Mark
Stylianopoulos, Anastasia
Arnold, Anna-Lena
Pfeiffer, Caitlin - Abstract:
- Abstract: Focus of Presentation: 'Cluster Tracker' is an automated tool for spatial cluster detection of notifiable disease data collected by the Department of Health (DH), Victoria. The tool combines R statistical software and a SaTScan cluster detection algorithm (prospective space-time permutation scan statistic) to detect notifiable disease case clusters in Victoria and is presently implemented for salmonellosis (categorised by type and/or MLVA). The objective of the tool is to conduct an initial screening of case data to improve the prioritisation of salmonellosis cases for epidemiological investigation. Findings: The Cluster Tracker tool parameters have been validated using historical data from 2017-2018, comparing DH outbreak and cluster investigations identified by usual surveillance activities with clusters detected by the Cluster Tracker tool. Parameter selection considered cluster detection agreement and disagreement, disease-specific epidemiological characteristics, and operational requirements. The Cluster Tracker tool was able to provide closely-aligned agreement with existing DH outbreak and cluster investigations using the validated parameters. Implications: This automated spatial cluster detection tool complements existing desktop surveillance of salmonellosis notifications to enhance public health decision making, and serves as an example of how spatial methods can improve real-time surveillance. Key messages: Advanced spatial statistical tools have a roleAbstract: Focus of Presentation: 'Cluster Tracker' is an automated tool for spatial cluster detection of notifiable disease data collected by the Department of Health (DH), Victoria. The tool combines R statistical software and a SaTScan cluster detection algorithm (prospective space-time permutation scan statistic) to detect notifiable disease case clusters in Victoria and is presently implemented for salmonellosis (categorised by type and/or MLVA). The objective of the tool is to conduct an initial screening of case data to improve the prioritisation of salmonellosis cases for epidemiological investigation. Findings: The Cluster Tracker tool parameters have been validated using historical data from 2017-2018, comparing DH outbreak and cluster investigations identified by usual surveillance activities with clusters detected by the Cluster Tracker tool. Parameter selection considered cluster detection agreement and disagreement, disease-specific epidemiological characteristics, and operational requirements. The Cluster Tracker tool was able to provide closely-aligned agreement with existing DH outbreak and cluster investigations using the validated parameters. Implications: This automated spatial cluster detection tool complements existing desktop surveillance of salmonellosis notifications to enhance public health decision making, and serves as an example of how spatial methods can improve real-time surveillance. Key messages: Advanced spatial statistical tools have a role alongside traditional methods to make better use of limited epidemiological capacity and improve the timeliness and prioritisation of surveillance activities for notifiable diseases. … (more)
- Is Part Of:
- International journal of epidemiology. Volume 50(2021)Supplement 1
- Journal:
- International journal of epidemiology
- Issue:
- Volume 50(2021)Supplement 1
- Issue Display:
- Volume 50, Issue 1 (2021)
- Year:
- 2021
- Volume:
- 50
- Issue:
- 1
- Issue Sort Value:
- 2021-0050-0001-0000
- Page Start:
- Page End:
- Publication Date:
- 2021-09-02
- Subjects:
- Epidemiology -- Periodicals
614.4 - Journal URLs:
- http://ije.oxfordjournals.org/ ↗
http://ukcatalogue.oup.com/ ↗ - DOI:
- 10.1093/ije/dyab168.457 ↗
- Languages:
- English
- ISSNs:
- 0300-5771
- Deposit Type:
- Legaldeposit
- View Content:
- Available online (eLD content is only available in our Reading Rooms) ↗
- Physical Locations:
- British Library DSC - 4542.244000
British Library DSC - BLDSS-3PM
British Library HMNTS - ELD Digital store - Ingest File:
- 19885.xml