Approaches to the evaluation of outbreak detection methods

Watkins, Rochelle; Eagleson, Serryn; Hall, Robert; Dailey, Lynne; Plant, Aileen J.

doi:10.1186/1471-2458-6-263

Cited by 23 publications

(21 citation statements)

References 21 publications

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“…This approach has been documented in a number of previous studies, which have compared the performance of early outbreak detection algorithms using simulated outbreaks [11][12][13][14][15][16][17][18]. The simulation enables the performance assessment and provides much-need comparative findings about outbreak detection algorithms.…”

Section: Introductionmentioning

confidence: 99%

Comparing early outbreak detection algorithms based on their optimized parameter values

Wang

Zeng

Seale

et al. 2010

Journal of Biomedical Informatics

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Section: Introductionmentioning

confidence: 99%

Comparing early outbreak detection algorithms based on their optimized parameter values

Wang

Zeng

Seale

et al. 2010

Journal of Biomedical Informatics

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“…By reviewing the recently published literature for reports that evaluated methods for the detection of infectious disease outbreaks in public health surveillance data, Watkins et al . categorized evaluation methods into four main methods: the descriptive, derived, epidemiological and simulation approaches, and concluded that no single approach could fulfill all evaluation requirements [41]. So, the appropriate model should be selected through comprehensive consideration and conscientious analysis according to the actual circumstance, and be promptly adjusted in relation to specific conditions in the community, changes in the pattern of life and the natural history of disease.…”

Section: Discussionmentioning

confidence: 99%

The Application of the Grey Disaster Model to Forecast Epidemic Peaks of Typhoid and Paratyphoid Fever in China

Shen

Chen

et al. 2013

PLoS ONE

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ObjectiveThe objectives of this study were to forecast epidemic peaks of typhoid and paratyphoid fever in China using the grey disaster model, to evaluate its feasibility of predicting the epidemic tendency of notifiable diseases.MethodsAccording to epidemiological features, the GM(1,1) model and DGM model were used to build the grey disaster model based on the incidence data of typhoid and paratyphoid fever collected from the China Health Statistical Yearbook. Model fitting accuracy test was used to evaluate the performance of these two models. Then, the next catastrophe date was predicted by the better model.ResultsThe simulation results showed that DGM model was better than GM(1,1) model in our data set. Using the DGM model, we predicted the next epidemic peak time will occur between 2023 to 2025.ConclusionThe grey disaster model can predict the typhoid and paratyphoid fever epidemic time precisely, which may provide valuable information for disease prevention and control.

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“…For this, we need datasets with clearly identified and delimited (in time) health events. While such data may be more easily available or derived for large well-defined seasonal outbreaks, the use of a small number of variables to define the occurrence of an outbreak may constitute a limited indicator and result in smaller outbreaks being missed [99]. Alternatively, expert judgement may be used retrospectively to determine the occurrence of events of public health importance, often using resource-intensive epidemiological investigation techniques.…”

Section: Discussionmentioning

confidence: 99%

Methodological challenges to multivariate syndromic surveillance: a case study using Swiss animal health data

2016

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BackgroundIn an era of ubiquitous electronic collection of animal health data, multivariate surveillance systems (which concurrently monitor several data streams) should have a greater probability of detecting disease events than univariate systems. However, despite their limitations, univariate aberration detection algorithms are used in most active syndromic surveillance (SyS) systems because of their ease of application and interpretation. On the other hand, a stochastic modelling-based approach to multivariate surveillance offers more flexibility, allowing for the retention of historical outbreaks, for overdispersion and for non-stationarity. While such methods are not new, they are yet to be applied to animal health surveillance data. We applied an example of such stochastic model, Held and colleagues’ two-component model, to two multivariate animal health datasets from Switzerland.ResultsIn our first application, multivariate time series of the number of laboratories test requests were derived from Swiss animal diagnostic laboratories. We compare the performance of the two-component model to parallel monitoring using an improved Farrington algorithm and found both methods yield a satisfactorily low false alarm rate. However, the calibration test of the two-component model on the one-step ahead predictions proved satisfactory, making such an approach suitable for outbreak prediction. In our second application, the two-component model was applied to the multivariate time series of the number of cattle abortions and the number of test requests for bovine viral diarrhea (a disease that often results in abortions). We found that there is a two days lagged effect from the number of abortions to the number of test requests. We further compared the joint modelling and univariate modelling of the number of laboratory test requests time series. The joint modelling approach showed evidence of superiority in terms of forecasting abilities.ConclusionsStochastic modelling approaches offer the potential to address more realistic surveillance scenarios through, for example, the inclusion of times series specific parameters, or of covariates known to have an impact on syndrome counts. Nevertheless, many methodological challenges to multivariate surveillance of animal SyS data still remain. Deciding on the amount of corroboration among data streams that is required to escalate into an alert is not a trivial task given the sparse data on the events under consideration (e.g. disease outbreaks).

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Approaches to the evaluation of outbreak detection methods

Cited by 23 publications

References 21 publications

Comparing early outbreak detection algorithms based on their optimized parameter values

Comparing early outbreak detection algorithms based on their optimized parameter values

The Application of the Grey Disaster Model to Forecast Epidemic Peaks of Typhoid and Paratyphoid Fever in China

Methodological challenges to multivariate syndromic surveillance: a case study using Swiss animal health data

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