J Biomed Inform - Quantifying the determinants of outbreak detection performance through simulation and machine learning.

Tópicos

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Resumo

JECTIVE: To develop a probabilistic model for discovering and quantifying determinants of outbreak detection and to use the model to predict detection performance for new outbreaks.MATERIALS AND METHODS: We used an existing software platform to simulate waterborne disease outbreaks of varying duration and magnitude. The simulated data were overlaid on real data from visits to emergency department in Montreal for gastroenteritis. We analyzed the combined data using biosurveillance algorithms, varying their parameters over a wide range. We then applied structure and parameter learning algorithms to the resulting data set to build a Bayesian network model for predicting detection performance as a function of outbreak characteristics and surveillance system parameters. We evaluated the predictions of this model through 5-fold cross-validation.RESULTS: The model predicted performance metrics of commonly used outbreak detection methods with an accuracy greater than 0.80. The model also quantified the influence of different outbreak characteristics and parameters of biosurveillance algorithms on detection performance in practically relevant surveillance scenarios. In addition to identifying characteristics expected a priori to have a strong influence on detection performance, such as the alerting threshold and the peak size of the outbreak, the model suggested an important role for other algorithm features, such as adjustment for weekly patterns.CONCLUSION: We developed a model that accurately predicts how characteristics of disease outbreaks and detection methods will influence on detection. This model can be used to compare the performance of detection methods under different surveillance scenarios, to gain insight into which characteristics of outbreaks and biosurveillance algorithms drive detection performance, and to guide the configuration of surveillance systems.

Resumo Limpo

jectiv develop probabilist model discov quantifi determin outbreak detect use model predict detect perform new outbreaksmateri method use exist softwar platform simul waterborn diseas outbreak vari durat magnitud simul data overlaid real data visit emerg depart montreal gastroenter analyz combin data use biosurveil algorithm vari paramet wide rang appli structur paramet learn algorithm result data set build bayesian network model predict detect perform function outbreak characterist surveil system paramet evalu predict model fold crossvalidationresult model predict perform metric common use outbreak detect method accuraci greater model also quantifi influenc differ outbreak characterist paramet biosurveil algorithm detect perform practic relev surveil scenario addit identifi characterist expect priori strong influenc detect perform alert threshold peak size outbreak model suggest import role algorithm featur adjust week patternsconclus develop model accur predict characterist diseas outbreak detect method will influenc detect model can use compar perform detect method differ surveil scenario gain insight characterist outbreak biosurveil algorithm drive detect perform guid configur surveil system

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