Key elements of infectious disease syndromic surveillance systems: A scoping review
Bibliographic record
Abstract
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.010 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.008 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".