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Also, the ignorance of CBS reduces the performance of outbreak detection if data are truly correlated.
The primary aim of this study was to investigate the performance of outbreak detection algorithms applied to seasonal data.
Many determinants affect the performance of outbreak detection in automated surveillance, and knowing about how these factors influence the detection performance can help to improve automated surveillance system.
This study compared the performance of three outbreak detection algorithms stratified by epidemic features of infectious disease and examined the relationship between epidemic features and performance of outbreak detection algorithms.
Although many algorithms have been designed to improve the performance of outbreak detection, few published studies have examined how epidemic features of infectious disease impact on the detection performance of algorithms.
Recently, extensive researches have explored novel algorithms to improve the performance of outbreak detection [ 9- 13], but evidence on how epidemic features impact on detection performance is still limited.
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In this study, we compared the performance of three outbreak detection methods by adding simulated outbreaks to actual daily counts of notifiable infectious diseases in CIDARS and examined the relationship of the detection performance with disease incubation, baseline counts and outbreak magnitude.
Testing performance on outbreaks of different temporal distributions and sizes is important because of the uncertainty about the types of outbreaks likely to be encountered in practice.
The simulation was designed to provide data to estimate the expected average performance of an outbreak detection algorithm for Western Australia based on daily notifiable disease surveillance, given the specified outbreak scenario.
For evaluation, we insert artificial outbreaks into the synthetic surveillance data streams at different locations and times and measure the performance of an outbreak detection protocol based on scan statistics.
The results support our working hypothesis that judicious use of information contained in the EMR can improve early disease detection compared to the sole use of diagnostic codes, and raise the possibility that harnessing the EMR as a data source may improve the overall performance of automated outbreak detection systems.
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