Outbreak detection algorithms for seasonal disease data : a case study using Ross River virus disease
Pelecanos, Anita M., Ryan, Peter A., & Gatton, Michelle L. (2010) Outbreak detection algorithms for seasonal disease data : a case study using Ross River virus disease. BMC Medical Informtics Decision Making, 10(74).
Detection of outbreaks is an important part of disease surveillance. Although many algorithms have been designed for detecting outbreaks, few have been specifically assessed against diseases that have distinct seasonal incidence patterns, such as those caused by vector-borne pathogens.
We applied five previously reported outbreak detection algorithms to Ross River virus (RRV) disease data (1991-2007) for the four local government areas (LGAs) of Brisbane, Emerald, Redland and Townsville in Queensland, Australia. The methods used were the Early Aberration Reporting System (EARS) C1, C2 and C3 methods, negative binomial cusum (NBC), historical limits method (HLM), Poisson outbreak detection (POD) method and the purely temporal SaTScan analysis. Seasonally-adjusted variants of the NBC and SaTScan methods were developed. Some of the algorithms were applied using a range of parameter values, resulting in 17 variants of the five algorithms.
The 9,188 RRV disease notifications that occurred in the four selected regions over the study period showed marked seasonality, which adversely affected the performance of some of the outbreak detection algorithms. Most of the methods examined were able to detect the same major events. The exception was the seasonally-adjusted NBC methods that detected an excess of short signals. The NBC, POD and temporal SaTScan algorithms were the only methods that consistently had high true positive rates and low false positive and false negative rates across the four study areas. The timeliness of outbreak signals generated by each method was also compared but there was no consistency across outbreaks and LGAs.
This study has highlighted several issues associated with applying outbreak detection algorithms to seasonal disease data. In lieu of a true gold standard, a quantitative comparison is difficult and caution should be taken when interpreting the true positives, false positives, sensitivity and specificity.
Impact and interest:
Citation counts are sourced monthly from and citation databases.
Citations counts from theindexing service can be viewed at the linked Google Scholar™ search.
Full-text downloads displays the total number of times this work’s files (e.g., a PDF) have been downloaded from QUT ePrints as well as the number of downloads in the previous 365 days. The count includes downloads for all files if a work has more than one.
|Item Type:||Journal Article|
|Additional Information:||Pelecanos, Anita M
Ryan, Peter A
Gatton, Michelle L
Research Support, Non-U.S. Gov't
BMC medical informatics and decision making
BMC Med Inform Decis Mak. 2010 Nov 24;10:74.
|Keywords:||Algorithms, Alphavirus Infections/diagnosis/epidemiology, Australia/epidemiology, Disease Notification, Disease Outbreaks/prevention & control, Humans, Population Surveillance/methods, Ross River virus/isolation & purification, Seasons|
|Subjects:||Australian and New Zealand Standard Research Classification > MEDICAL AND HEALTH SCIENCES (110000) > PUBLIC HEALTH AND HEALTH SERVICES (111700) > Health Information Systems (incl. Surveillance) (111711)|
|Divisions:||Current > QUT Faculties and Divisions > Faculty of Health
Current > Schools > School of Public Health & Social Work
|Copyright Owner:||Copyright 2010 Pelecanos et al; licensee BioMed Central Ltd.|
|Copyright Statement:||This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.|
|Deposited On:||18 Aug 2014 01:24|
|Last Modified:||18 Aug 2014 22:50|
Repository Staff Only: item control page