Comparing early outbreak detection algorithms based on their optimized parameter values

Wang, Xiaoli; Zeng, Daniel; Seale, Holly; Su, Li; He, Cheng; Luan, Rongsheng; He, Xiaofen; Pang, Xinghuo; Dou, Xiangfeng; Wang, Quanyi

doi:10.1016/j.jbi.2009.08.003

Cited by 25 publications

(24 citation statements)

References 26 publications

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“…Moreover, as it does not require any additional time to notify a disease once they are reading the guideline, it is hoped that, if even only for specific disease areas, some useful data will be collected. Nevertheless, as novel technologies start to generate a new type of epidemiological data, new approaches to understanding and automating how best to interpret these data, including algorithm generation, will be required [22,23]. …”

Section: Utility and Discussionmentioning

confidence: 99%

Development of ClickClinica: a novel smartphone application to generate real-time global disease surveillance and clinical practice data

Michael

Geleta

2013

BMC Med Inform Decis Mak

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BackgroundIdentification and tracking of important communicable diseases is pivotal to our understanding of the geographical distribution of disease, the emergence and spread of novel and resistant infections, and are of particular importance for public health policy planning. Moreover, understanding of current clinical practice norms is essential to audit clinical care, identify areas of concern, and develop interventions to improve care quality.However, there are several barriers to obtaining these research data. For example current disease surveillance mechanisms make it difficult for the busy doctor to know which diseases to notify, to whom and how, and are also time consuming. Consequently, many cases go un-notified. In addition assessments of current clinical practice are typically limited to small retrospective audits in individual hospitals.Therefore, we developed a free smartphone application to try to increase the identification of major infectious diseases and other acute medical presentations and improve our understanding of clinical practice.DescriptionWithin the first month there were over 1000 downloads and over 600 specific disease notifications, coming from a broad range of specialities, grades and from all across the globe, including some resource poor settings.Notifications have already provided important information, such as new cases of TB meningitis, resistant HIV and rabies, and important clinical information, such as where patient with myocardial infarctions are and are not receiving potentially life-saving therapy.The database generated can also answer new, dynamic and targeted questions. When a new guideline is released, for example for a new pandemic infection, we can track, in real-time, the global usage of the guideline and whether the recommendations are being followed. In addition this allows identification of where cases with key markers of severe disease are occurring. This is a potential resource for guideline-producing bodies, clinical governance and public health institutions and also for patient recruitment into ongoing studies.ConclusionsFurther parallel studies are needed to assess the clinical and epidemiological utility of novel disease surveillance applications, such as this, with direct comparisons made to data collected through routine surveillance routes.Nevertheless, current disease surveillance mechanisms do not always comprehensively and accurately reflect disease distribution for many conditions. Smartphone applications, such as ClickClinica, are a novel approach with the potential to generate real-time disease surveillance data that may augment current methods.

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Section: Utility and Discussionmentioning

confidence: 99%

Development of ClickClinica: a novel smartphone application to generate real-time global disease surveillance and clinical practice data

Michael

Geleta

2013

BMC Med Inform Decis Mak

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“…To enable the calculation of detection timeliness of all outbreaks, if an outbreak was undetected, TTD was assigned the total duration of the outbreak, so as to enable calculation of the median timeliness across all outbreaks. Therefore, TTD is an integrated index that reflects both the timeliness and sensitivity of an algorithm [7]. The optimal threshold for an algorithm was the one with the shortest TTD, or with the highest specificity when (a) the TTD was either same or (b) had a difference of less than half a day and the difference between the specificity was >5.0% [7].…”

Section: Methodsmentioning

confidence: 99%

“…Therefore, TTD is an integrated index that reflects both the timeliness and sensitivity of an algorithm [7]. The optimal threshold for an algorithm was the one with the shortest TTD, or with the highest specificity when (a) the TTD was either same or (b) had a difference of less than half a day and the difference between the specificity was >5.0% [7]. We used the Student t test to examine whether the number of signals was significantly different by setting the optimal threshold in each region according to the level of disease incidence and using the same optimal thresholds to the whole region.…”

Section: Methodsmentioning

confidence: 99%

“…In current public health practice, aberrancy-detection algorithms, including temporal (e.g., Exponentially Weighted Moving Average and cumulative sum), spatial (e.g., Spatial Scan Statistic), and spatio-temporal (e.g., Space-Time Scan Statistic) methods, can contribute important information to support outbreak detection and management [3]–[6]. Evaluations of surveillance systems have demonstrated that many factors affect the accuracy of aberration detection, including the mode of disease transmission, seasonal patterns in disease incidence, the detection algorithm used, and the alerting threshold of the algorithm [7], [8]. Disease incidence can vary greatly between regions under surveillance, but it is not known to what extent this variation in incidence affects the accuracy and timeliness of aberration detection if the same algorithm with a constant alerting threshold is applied to all regions.…”

Section: Introductionmentioning

confidence: 99%

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Improving the Performance of Outbreak Detection Algorithms by Classifying the Levels of Disease Incidence

et al. 2013

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We evaluated a novel strategy to improve the performance of outbreak detection algorithms, namely setting the alerting threshold separately in each region according to the disease incidence in that region. By using data on hand, foot and mouth disease in Shandong province, China, we evaluated the impact of disease incidence on the performance of outbreak detection algorithms (EARS-C1, C2 and C3). Compared to applying the same algorithm and threshold to the whole region, setting the optimal threshold in each region according to the level of disease incidence (i.e., high, middle, and low) enhanced sensitivity (C1: from 94.4% to 99.1%, C2: from 93.5% to 95.4%, C3: from 91.7% to 95.4%) and reduced the number of alert signals (the percentage of reduction is C1∶4.3%, C2∶11.9%, C3∶10.3%). Our findings illustrate a general method for improving the accuracy of detection algorithms that is potentially applicable broadly to other diseases and regions.

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“…After 6 months, once the collection procedures were stabilized, the C1-mild epidemics detection method used by the Early Aberration Reporting System ( 18 , 19 ) was performed. This method enables the detection of outbreaks on a dataset with limited historical data ( > 7 weeks).…”

Section: Implementing a Biosurveillance Systemmentioning

confidence: 99%

Implementation and Initial Analysis of a Laboratory-Based Weekly Biosurveillance System, Provence-Alpes-Côte d’Azur, France

Huart¹,

Bédubourg²,

Abat³

et al. 2017

Emerg. Infect. Dis.

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We describe the implementation of an automated infectious disease surveillance system that uses data collected from 210 microbiologic laboratories throughout the Provence-Alpes-Côte d’Azur region in France. Each week, these facilities report bacterial species that have been isolated from patients in their area. An alarm is triggered whenever the case count for a bacterial species infection exceeds 2 SDs of the historical mean for that species at the participating laboratory. At its inception in July 2013, the system monitored 611 bacterial species. During July 1, 2013–March 20, 2016, weekly analyses of incoming surveillance data generated 34 alarms signaling possible infectious disease outbreaks; after investigation, 14 (41%) of these alarms resulted in health alerts declared by the regional health authority. We are currently improving the system by developing an Internet-based surveillance platform and extending our surveillance to include more laboratories in the region.

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Comparing early outbreak detection algorithms based on their optimized parameter values

Abstract: The performance of these algorithms has a correlation to their parameter values, which may affect the performance evaluation.

Cited by 25 publications

References 26 publications

Development of ClickClinica: a novel smartphone application to generate real-time global disease surveillance and clinical practice data

Development of ClickClinica: a novel smartphone application to generate real-time global disease surveillance and clinical practice data

Improving the Performance of Outbreak Detection Algorithms by Classifying the Levels of Disease Incidence

Implementation and Initial Analysis of a Laboratory-Based Weekly Biosurveillance System, Provence-Alpes-Côte d’Azur, France

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