scholarly journals Comparison of statistical algorithms for daily syndromic surveillance aberration detection

2019 ◽  
Vol 35 (17) ◽  
pp. 3110-3118
Author(s):  
Angela Noufaily ◽  
Roger A Morbey ◽  
Felipe J Colón-González ◽  
Alex J Elliot ◽  
Gillian E Smith ◽  
...  
Keyword(s):  
Public Health ◽  
Syndromic Surveillance ◽  
Performance Metrics ◽  
High Specificity ◽  
Outbreak Detection ◽  
Supplementary Information ◽  
Surveillance Systems ◽  
Detection Algorithms ◽  
Aberration Detection ◽  
Wide Range

Abstract Motivation Public health authorities can provide more effective and timely interventions to protect populations during health events if they have effective multi-purpose surveillance systems. These systems rely on aberration detection algorithms to identify potential threats within large datasets. Ensuring the algorithms are sensitive, specific and timely is crucial for protecting public health. Here, we evaluate the performance of three detection algorithms extensively used for syndromic surveillance: the ‘rising activity, multilevel mixed effects, indicator emphasis’ (RAMMIE) method and the improved quasi-Poisson regression-based method known as ‘Farrington Flexible’ both currently used at Public Health England, and the ‘Early Aberration Reporting System’ (EARS) method used at the US Centre for Disease Control and Prevention. We model the wide range of data structures encountered within the daily syndromic surveillance systems used by PHE. We undertake extensive simulations to identify which algorithms work best across different types of syndromes and different outbreak sizes. We evaluate RAMMIE for the first time since its introduction. Performance metrics were computed and compared in the presence of a range of simulated outbreak types that were added to baseline data. Results We conclude that amongst the algorithm variants that have a high specificity (i.e. >90%), Farrington Flexible has the highest sensitivity and specificity, whereas RAMMIE has the highest probability of outbreak detection and is the most timely, typically detecting outbreaks 2–3 days earlier. Availability and implementation R codes developed for this project are available through https://github.com/FelipeJColon/AlgorithmComparison Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals The importance of age-specific data in routine syndromic surveillance

2017 ◽  
Vol 9 (1) ◽  
Author(s):  
Roger Morbey ◽  
Alex J. Elliot ◽  
Gillian E. Smith
Keyword(s):  
Public Health ◽  
Hospital Admissions ◽  
Age Distribution ◽  
Age Groups ◽  
Health Indicators ◽  
Older Age ◽  
Outbreak Detection ◽  
Detection Methods ◽  
Surveillance Systems ◽  
Wide Range

ObjectiveTo investigate whether aberration detection methods for syndromicsurveillance would be more useful if data were stratified by age band.IntroductionWhen monitoring public health incidents using syndromicsurveillance systems, Public Health England (PHE) uses the ageof the presenting patient as a key indicator to further assess theseverity, impact of the incident, and to provide intelligence on thelikely cause. However the age distribution of cases is usually notconsidered until after unusual activity has been identified in the all-ages population data. We assessed whether monitoring specific agegroups contemporaneously could improve the timeliness, specificityand sensitivity of public health surveillance.MethodsFirst, we examined a wide range of health indicators from the PHEsyndromic surveillance systems to identify for further study thosewith the greatest seasonal variation in the age distribution of cases.Secondly, we examined the identified indicators to ascertain whetherany age bands consistently lagged behind other age bands. Finally,we applied outbreak detection methods retrospectively to age specificdata, identifying periods of increased activity that were only detectedor detected earlier when age-specific surveillance was used.ResultsSeasonal increases in respiratory indicators occurred first inyounger age groups, with increases in children under 5 providingearly warning of subsequent increases occurring in older age groups.Also, we found age specific indicators improved the specificity ofsurveillance using indicators relating to respiratory and eye problems;identifying unusual activity that was less apparent in the all-agespopulation.ConclusionsRoutine surveillance of respiratory indicators in young childrenwould have provided early warning of increases in older age groups,where the burden on health care usage, e.g. hospital admissions, isgreatest. Furthermore this cross-correlation between ages occurredconsistently even though the age distribution of the burden ofrespiratory cases varied between seasons. Age specific surveillancecan improve sensitivity of outbreak detection although all-agesurveillance remains more powerful when case numbers are low.

scholarly journals Comparison of statistical algorithms for syndromic surveillance aberration detection

2018 ◽  
Vol 10 (1) ◽  
Author(s):  
R. Morbey ◽  
A. Noufaily ◽  
F. D. Colón-González ◽  
A. Elliot ◽  
S. Harcourt ◽  
...  
Keyword(s):  
Public Health ◽  
Sensitivity And Specificity ◽  
Syndromic Surveillance ◽  
Mixed Effects ◽  
Outbreak Detection ◽  
Aberration Detection ◽  
Day Of The Week ◽  
Multi Level ◽  
Public Holidays

ObjectiveTo investigate whether alternative statistical approaches can improve daily aberration detection using syndromic surveillance in England.IntroductionSyndromic surveillance involves monitoring big health datasets to provide early warning of threats to public health. Public health authorities use statistical detection algorithms to interrogate these datasets for aberrations that are indicative of emerging threats. The algorithm currently in use at Public Health England (PHE) for syndromic surveillance is the ‘rising activity, multi-level mixed effects, indicator emphasis’ (RAMMIE) method (Morbey et al, 2015), which fits a mixed model to counts of syndromes on a daily basis. This research checks whether the RAMMIE method works across a range of public health scenarios and how it compares to alternative methods.MethodsFor this purpose, we compare RAMMIE to the improved quasi-Poisson regression-based approach (Noufaily et al, 2013), currently implemented at PHE for weekly infectious disease laboratory surveillance, and to the Early Aberration Reporting System (EARS) method (Rossi et al, 1999), which is used for syndromic surveillance aberration detection in many other countries. We model syndromic datasets, capturing real data aspects such as long-term trends, seasonality, public holidays, and day-of-the-week effects, with or without added outbreaks. Then, we compute the sensitivity and specificity to compare how well each of the algorithms detects synthetic outbreaks to provide recommendations for the most suitable statistical methods to use during different public health scenarios.ResultsPreliminary results suggest all methods provide high sensitivity and specificity, with the (Noufaily et al, 2013) approach having the highest sensitivity and specificity. We showed that for syndromes with long-term increasing trends, RAMMIE required modificaiton to prevent excess false alarms. Also, our study suggests further work is needed to fully account for public holidays and day-of-the-week effects.ConclusionsOur study will provide recommendations for which algorithm is most effective for PHE's syndromic surveillance for a range of different syndromes. Furthermore our work to generate standardised synthetic syndromic datasets and a range of outbreaks can be used for future evaluations in England and elsewhere.ReferencesNoufaily, A., Enki, D. G., Farrington, C. P., Garthwaite, P., Andrews, N. and Charlett, A. (2013). An Improved Algorithm for Outbreak Detection in Multiple Surveillance Systems. Statistics in Medicine, 32(7), 1206-1222.Morbey, R. A., Elliot, A. J., Charlett, A., Verlander, A. Q, Andrews, N. and Smith, G. (2013). The application of a novel ‘rising activity, multi-level mixed effects, indicator emphasis’ (RAMMIE) method for syndromic surveillance in England, Bioinformatics, 31(22), 3660-3665.Rossi, G, Lampugnani, L, Marchi, M. (1999), An approximate CUSUM procedure for surveillance of health events. Statistics in Medicine, 18, 2111–2122

scholarly journals Cluster Detection Mechanisms for Syndromic Surveillance Systems: Systematic Review and Framework Development (Preprint)

2018 ◽  
Author(s):  
Prosper Kandabongee Yeng ◽  
Ashenafi Zebene Woldaregay ◽  
Terje Solvoll ◽  
Gunnar Hartvigsen
Keyword(s):  
Systematic Review ◽  
Disease Surveillance ◽  
Syndromic Surveillance ◽  
Space Time ◽  
Cluster Detection ◽  
Surveillance Systems ◽  
Syndromic Surveillance System ◽  
Detection Algorithms ◽  
Aberration Detection ◽  
Detection Mechanisms

BACKGROUND The time lag in detecting disease outbreaks remains a threat to global health security. The advancement of technology has made health-related data and other indicator activities easily accessible for syndromic surveillance of various datasets. At the heart of disease surveillance lies the clustering algorithm, which groups data with similar characteristics (spatial, temporal, or both) to uncover significant disease outbreak. Despite these developments, there is a lack of updated reviews of trends and modelling options in cluster detection algorithms. OBJECTIVE Our purpose was to systematically review practically implemented disease surveillance clustering algorithms relating to temporal, spatial, and spatiotemporal clustering mechanisms for their usage and performance efficacies, and to develop an efficient cluster detection mechanism framework. METHODS We conducted a systematic review exploring Google Scholar, ScienceDirect, PubMed, IEEE Xplore, ACM Digital Library, and Scopus. Between January and March 2018, we conducted the literature search for articles published to date in English in peer-reviewed journals. The main eligibility criteria were studies that (1) examined a practically implemented syndromic surveillance system with cluster detection mechanisms, including over-the-counter medication, school and work absenteeism, and disease surveillance relating to the presymptomatic stage; and (2) focused on surveillance of infectious diseases. We identified relevant articles using the title, keywords, and abstracts as a preliminary filter with the inclusion criteria, and then conducted a full-text review of the relevant articles. We then developed a framework for cluster detection mechanisms for various syndromic surveillance systems based on the review. RESULTS The search identified a total of 5936 articles. Removal of duplicates resulted in 5839 articles. After an initial review of the titles, we excluded 4165 articles, with 1674 remaining. Reading of abstracts and keywords eliminated 1549 further records. An in-depth assessment of the remaining 125 articles resulted in a total of 27 articles for inclusion in the review. The result indicated that various clustering and aberration detection algorithms have been empirically implemented or assessed with real data and tested. Based on the findings of the review, we subsequently developed a framework to include data processing, clustering and aberration detection, visualization, and alerts and alarms. CONCLUSIONS The review identified various algorithms that have been practically implemented and tested. These results might foster the development of effective and efficient cluster detection mechanisms in empirical syndromic surveillance systems relating to a broad spectrum of space, time, or space-time.

scholarly journals Cluster Detection Mechanisms for Syndromic Surveillance Systems: Systematic Review and Framework Development

10.2196/11512 ◽  
2020 ◽  
Vol 6 (2) ◽  
pp. e11512 ◽  
Author(s):  
Prosper Kandabongee Yeng ◽  
Ashenafi Zebene Woldaregay ◽  
Terje Solvoll ◽  
Gunnar Hartvigsen
Keyword(s):  
Systematic Review ◽  
Disease Surveillance ◽  
Syndromic Surveillance ◽  
Space Time ◽  
Cluster Detection ◽  
Surveillance Systems ◽  
Syndromic Surveillance System ◽  
Detection Algorithms ◽  
Aberration Detection ◽  
Detection Mechanisms

Background The time lag in detecting disease outbreaks remains a threat to global health security. The advancement of technology has made health-related data and other indicator activities easily accessible for syndromic surveillance of various datasets. At the heart of disease surveillance lies the clustering algorithm, which groups data with similar characteristics (spatial, temporal, or both) to uncover significant disease outbreak. Despite these developments, there is a lack of updated reviews of trends and modelling options in cluster detection algorithms. Objective Our purpose was to systematically review practically implemented disease surveillance clustering algorithms relating to temporal, spatial, and spatiotemporal clustering mechanisms for their usage and performance efficacies, and to develop an efficient cluster detection mechanism framework. Methods We conducted a systematic review exploring Google Scholar, ScienceDirect, PubMed, IEEE Xplore, ACM Digital Library, and Scopus. Between January and March 2018, we conducted the literature search for articles published to date in English in peer-reviewed journals. The main eligibility criteria were studies that (1) examined a practically implemented syndromic surveillance system with cluster detection mechanisms, including over-the-counter medication, school and work absenteeism, and disease surveillance relating to the presymptomatic stage; and (2) focused on surveillance of infectious diseases. We identified relevant articles using the title, keywords, and abstracts as a preliminary filter with the inclusion criteria, and then conducted a full-text review of the relevant articles. We then developed a framework for cluster detection mechanisms for various syndromic surveillance systems based on the review. Results The search identified a total of 5936 articles. Removal of duplicates resulted in 5839 articles. After an initial review of the titles, we excluded 4165 articles, with 1674 remaining. Reading of abstracts and keywords eliminated 1549 further records. An in-depth assessment of the remaining 125 articles resulted in a total of 27 articles for inclusion in the review. The result indicated that various clustering and aberration detection algorithms have been empirically implemented or assessed with real data and tested. Based on the findings of the review, we subsequently developed a framework to include data processing, clustering and aberration detection, visualization, and alerts and alarms. Conclusions The review identified various algorithms that have been practically implemented and tested. These results might foster the development of effective and efficient cluster detection mechanisms in empirical syndromic surveillance systems relating to a broad spectrum of space, time, or space-time.

scholarly journals Key elements of infectious disease syndromic surveillance systems: A scoping review

2017 ◽  
Vol 9 (1) ◽  
Author(s):  
Stephanie Hughes ◽  
Alex Elliot ◽  
Scott McEwen ◽  
Amy Greer ◽  
Ian Young ◽  
...  
Keyword(s):  
Public Health ◽  
Infectious Disease ◽  
Data Quality ◽  
Scoping Review ◽  
Syndromic Surveillance ◽  
Public Health Practice ◽  
Data Sources ◽  
Surveillance Systems ◽  
Health Practice ◽  
Wide Range

IntroductionSyndromic surveillance is an alternative type of public healthsurveillance which utilises pre-diagnostic data sources to detectoutbreaks earlier than conventional (laboratory) surveillance andmonitor the progression of illnesses in populations. These systems areoften noted for their ability to detect a wider range of cases in under-reported illnesses, utilise existing data sources, and alert public healthauthorities of emerging crises. In addition, they are highly versatileand can be applied to a wide range of illnesses (communicable andnon-communicable) and environmental conditions. As a result, theirimplementation in public health practice is expanding rapidly. Thisscoping review aimed to identify all existing literature detailing thenecessary components in the defining, creating, implementing, andevaluating stages of human infectious disease syndromic surveillancesystems.MethodsA full scoping review protocol was developeda priori. Theresearch question posed for the review was “What are the essentialelements of a fully functional syndromic surveillance system forhuman infectious disease?” Five bibliographic databases (Pubmed,Scopus, CINAHL, Web of Science, ProQuest) and eleven websites(Google, Public Health Ontario, Public Health England, Public HealthAgency of Canada, Centers for Disease Control and Prevention,European Centre for Disease Prevention and Control, InternationalSociety for Disease Surveillance, Syndromic Surveillance Systems inEurope, Eurosurveillance, Kingston Frontenac, Lennox & AddingtonPublic Health (x2)) were searched for peer-reviewed, government,academic, conference, and book literature. A total of 1237 uniquecitations were identified from this search and uploaded into thescoping review softwareCovidence. The titles and abstracts werescreened for relevance to the subject material, resulting in 142documents for full-text screening. Following this step, 55 documentsremained for data extraction and inclusion in the scoping review. Twoindependent reviewers conducted each step.ResultsThe scoping review identified many essential elements in thedefining, creating, implementing, and evaluating of syndromicsurveillance systems. These included the defining of “syndromicsurveillance”, classification of syndromes, data quality andcompleteness, statistical methods, privacy and confidentialityissues, costs, operational challenges, management composition,collaboration with other public health agencies, and evaluationcriteria. Several benefits and limitations of the systems were alsoidentified, when comparing them to other public health surveillancemethods. Benefits included the timeliness of analyses and reporting,potential cost savings, complementing traditional surveillancemethods, high sensitivity, versatility, ability to perform short- andlong-term surveillance, non-specificity of the systems, ability to fillin gaps of under-reported illnesses, and the collaborations whichare fostered through its platform; limitations included the potentialresources and costs required, inability to replace traditional healthcareand surveillance methods, the false alerts which may occur, non-specificity of the systems, poor data quality and completeness, timelags in analyses, limited effectiveness at detecting smaller-scaleoutbreaks, and privacy issues with accessing data.ConclusionsOver the past decade, syndromic surveillance systems have becomean integral part of public health practice internationally. Their abilityto monitor a wide variety of illnesses and conditions, detect illnessesearlier than traditional surveillance methods, and be created usingexisting data sources make them a valuable public health tool.The results from this scoping review demonstrate the benefits andlimitations and overall role of the systems in public health practice.In addition, this study also shows that a complete set of key elementsare required in order to properly define, create, implement, andevaluate these systems to ensure their effectiveness and performance.

scholarly journals Assessing 3 Outbreak Detection Algorithms in an Electronic Syndromic Surveillance System in a Resource-Limited Setting

2020 ◽  
Vol 26 (9) ◽  
pp. 2196-2200
Author(s):  
Emily Alsentzer ◽  
Sarah-Blythe Ballard ◽  
Joan Neyra ◽  
Delphis M. Vera ◽  
Victor B. Osorio ◽  
...  
Keyword(s):  
Surveillance System ◽  
Syndromic Surveillance ◽  
Resource Limited Setting ◽  
Outbreak Detection ◽  
Syndromic Surveillance System ◽  
Detection Algorithms ◽  
Resource Limited ◽  
Limited Setting

Public health impacts of culture independent diagnostic testing in Australia

2017 ◽  
Vol 38 (4) ◽  
pp. 162
Author(s):  
Fiona J May
Keyword(s):  
Public Health ◽  
Health Management ◽  
Diagnostic Testing ◽  
Primary Source ◽  
Outbreak Detection ◽  
Surveillance Systems ◽  
Advantages And Disadvantages ◽  
Testing Protocol ◽  
Culture Independent ◽  
The Impact

Culture independent diagnostic tests (CIDT) for detection of pathogens in clinical specimens have become widely adopted in Australian pathology laboratories. Pathology laboratories are the primary source of notification of pathogens to state and territory surveillance systems. Monitoring and analysis of surveillance data is integral to guiding public health actions to reduce the incidence of disease and respond to outbreaks. As with any change in testing protocol, the advantages and disadvantages of the change from culture based testing to culture independent testing need to be weighed up and the impact on surveillance and outbreak detection assessed. This article discusses the effect of this change in testing on surveillance and public health management of pathogens in Australia, with specific focus on gastrointestinal pathogens.

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