Establishing and validating syndromic surveillance of gastrointestinal infections using routine emergency department data, Germany, 2019–2023
Bibliographic record
Abstract
Gastrointestinal infections in Germany account for 24.5 million outpatient visits annually. To enhance outbreak detection and trend monitoring, we developed and validated a syndrome definition for syndromic surveillance of gastrointestinal infections in emergency departments. We selected presenting complaints (Canadian Emergency Department Information System) and diagnoses (ICD-10) to develop the syndrome definition. Validation involved cross-correlation analysis of syndromic and laboratory-based surveillance trends (norovirus-gastroenteritis, rotavirus-gastroenteritis, campylobacteriosis and salmonellosis notifications). We included emergency departments from the German AKTIN registry with continuous data transmission (01/2019-06/2023). Our novel syndrome definition combined complaints (diarrhoea, vomiting, nausea) and diagnoses (intestinal infectious diseases). Across 864,353 visits in 7 emergency departments, 2.1% (n = 18,158) were gastrointestinal infection cases. Of those, 57% (n = 10,424) were female; 23% were aged 0-19 years (n = 4108); and 23% 20-39 years (n = 4116). Trends were similar between surveillance systems. Cross-correlation was 0.73 (95%-confidence interval 0.61-0.85; p < 0.001) at lag - 1, indicating a 1-week relative reporting delay of laboratory-based surveillance. Coherent trends and significant cross-correlation validated our syndrome definition. This novel automated syndromic surveillance complements laboratory-based surveillance while offering improved timeliness and reduced workload. Therefore, it was implemented in Germany's national routine surveillance of emergency departments.
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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 | 0.014 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| 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 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".