Dataset Information

Applying cusum-based methods for the detection of outbreaks of Ross River virus disease in Western Australia.

ABSTRACT:

Background

The automated monitoring of routinely collected disease surveillance data has the potential to ensure that important changes in disease incidence are promptly recognised. However, few studies have established whether the signals produced by automated monitoring methods correspond with events considered by epidemiologists to be of public health importance. This study investigates the correspondence between retrospective epidemiological evaluation of notifications of Ross River virus (RRv) disease in Western Australia, and the signals produced by two cumulative sum (cusum)-based automated monitoring methods.

Methods

RRv disease case notification data between 1991 and 2004 were assessed retrospectively by two experienced epidemiologists, and the timing of identified outbreaks was compared with signals generated from two different types of cusum-based automated monitoring algorithms; the three Early Aberration Reporting System (EARS) cusum algorithms (C1, C2 and C3), and a negative binomial cusum.

Results

We found the negative binomial cusum to have a significantly greater area under the receiver operator characteristic curve when compared with the EARS algorithms, suggesting that the negative binomial cusum has a greater level of agreement with epidemiological opinion than the EARS algorithms with respect to the existence of outbreaks of RRv disease, particularly at low false alarm rates. However, the performance of individual EARS and negative binomial cusum algorithms were not significantly different when timeliness was also incorporated into the area under the curve analyses.

Conclusion

Our retrospective analysis of historical data suggests that, compared with the EARS algorithms, the negative binomial cusum provides greater sensitivity for the detection of outbreaks of RRv disease at low false alarm levels, and decreased timeliness early in the outbreak period. Prospective studies are required to investigate the potential usefulness of these algorithms in practice.

SUBMITTER: Watkins RE

PROVIDER: S-EPMC2542357 | biostudies-literature | 2008 Aug

REPOSITORIES: biostudies-literature

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Publications

Applying cusum-based methods for the detection of outbreaks of Ross River virus disease in Western Australia.

Watkins Rochelle E RE Eagleson Serryn S Veenendaal Bert B Wright Graeme G Plant Aileen J AJ

BMC medical informatics and decision making 20080813

<h4>Background</h4>The automated monitoring of routinely collected disease surveillance data has the potential to ensure that important changes in disease incidence are promptly recognised. However, few studies have established whether the signals produced by automated monitoring methods correspond with events considered by epidemiologists to be of public health importance. This study investigates the correspondence between retrospective epidemiological evaluation of notifications of Ross River ...[more]

PMID: 18700044

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Project description:Ross River virus (RRV), an alphavirus of the Togaviridae family, is the most medically significant mosquito-borne virus of Australia. Past RRV phylogenetic and evolutionary analyses have been based on partial genome analyses only. Three geographically distinct RRV lineages, the Eastern, the Western, and the supposedly extinct North-Eastern lineage, were classified previously. We sought to expand on past phylogenies through robust genome-scale phylogeny to better understand RRV genetic diversity and evolutionary dynamics. We analyzed 106 RRV complete coding sequences, which included 13 genomes available on NCBI and 94 novel sequences derived for this study, sampled throughout Western Australia (1977-2014) and during the substantial Pacific Islands RRV epidemic (1979-1980). Our final data set comprised isolates sampled over 59 years (1959-2018) from a range of locations. Four distinct genotypes were defined, with the newly described genotype 4 (G4) found to be the contemporary lineage circulating in Western Australia. The prior geographical classification of RRV lineages was not supported by our findings, with evidence of geographical and temporal cocirculation of distinct genetic groups. Bayesian Markov chain Monte Carlo (MCMC) analysis revealed that RRV lineages diverged from a common ancestor approximately 94 years ago, with distinct lineages emerging roughly every 10 years over the past 50 years in periodic bursts of genetic diversity. Our study has enabled a more robust analysis of RRV evolutionary history and resolved greater genetic diversity that had been previously defined by partial E2 gene analysis.IMPORTANCE Ross River virus (RRV) causes the most common mosquito-borne infection in Australia and causes a significant burden of suffering to infected individuals as well as being a large burden to the Australian economy. The genetic diversity of RRV and its evolutionary history have so far only been studied using partial E2 gene analysis with a limited number of isolates. Robust whole-genome analysis has not yet been conducted. This study generated 94 novel near-whole-genome sequences to investigate the evolutionary history of RRV to better understand its genetic diversity through comprehensive whole-genome phylogeny. A better understanding of RRV genetic diversity will enable better diagnostics, surveillance, and potential future vaccine design.

Project description:BACKGROUND: 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. METHODS: 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. RESULTS: 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. CONCLUSIONS: 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.

Dataset Information

Applying cusum-based methods for the detection of outbreaks of Ross River virus disease in Western Australia.

Background

Methods

Results

Conclusion

Publications

Applying cusum-based methods for the detection of outbreaks of Ross River virus disease in Western Australia.

Similar Datasets

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