Drivers of earlier infectious disease outbreak detection: a systematic literature review
Bibliographic record
Abstract
BACKGROUND: The early detection of infectious disease outbreaks can reduce the ultimate size of the outbreak, with lower overall morbidity and mortality due to the disease. Numerous approaches to the earlier detection of outbreaks exist, and methods have been developed to measure progress on timeliness. Understanding why these surveillance approaches work and do not work will elucidate key drivers of early detection, and could guide interventions to achieve earlier detection. Without clarity about the conditions necessary for earlier detection and the factors influencing these, attempts to improve surveillance will be ad hoc and unsystematic. METHODS: A systematic review was conducted using the PRISMA framework (Preferred Reporting Items for Systematic Reviews and Meta-analyses) to identify research published between January 1, 1990 and December 31, 2015 in the English language. The MEDLINE (PubMed) database was searched. Influencing factors were organized according to a generic five-step infectious disease detection model. RESULTS: Five studies were identified and included in the review. These studies evaluated the effect of electronic-based reporting on detection timeliness, impact of laboratory agreements on timeliness, and barriers to notification by general practitioners. Findings were categorized as conditions necessary for earlier detection and factors that influence whether or not these conditions can be in place, and were organized according to the detection model. There is some evidence on reporting, no evidence on assessment, and speculation about local level recognition. CONCLUSION: Despite significant investment in early outbreak detection, there is very little evidence with respect to factors that influence earlier detection. More research is needed to guide intervention planning.
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 machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.029 | 0.135 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.009 | 0.014 |
| Bibliometrics | 0.018 | 0.018 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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 source (direct Gemma or distilled Codex), 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".