Outbreak detection algorithms for seasonal disease data: a case study using ross river virus disease

Anita M Pelecanos,Peter A Ryan,Michelle L Gatton

doi:10.1186/1472-6947-10-74

Anita M Pelecanos, Peter A Ryan + Show 1 more

Open Access

https://doi.org/10.1186/1472-6947-10-74

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Abstract

BackgroundDetection 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.MethodsWe 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.ResultsThe 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.ConclusionsThis 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.

Highlights

Detection of outbreaks is an important part of disease surveillance
The notification data received for each de-identified patient included the onset week of illness, age (0-29, 30-59 and ≥60 years), gender and local government area (LGA) of residence
Notification data for patients residing in the LGAs of Brisbane, Emerald, Redland and Townsville were selected for this study due to their contrasting population sizes and disease incidence rates

Summary

Introduction

Detection of outbreaks is an important part of disease surveillance. Disease surveillance and outbreak detection are fundamental to the provision of adequate and timely public health services. There are a multitude of outbreak detection algorithms that have been applied to a variety of disease studies at different spatial scales. Many detection algorithms have been reported, there are few studies comparing methods, especially using public health data. C3 algorithms have been assessed and compared using artificial simulations that mimic public health data [6,7,8] and semi-synthetic disease data [9]. Watkins et al [10] compared the sensitivity and timeliness of the EARS C1, C2 and C3 methods and a negative binomial cusum outbreak detection method to detect aberrations in Ross River virus (RRV) disease in Western Australia

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