Spatio-Temporal Clustering of Multi-Location Time Series to Model Seasonal Influenza Spread
Bibliographic record
Abstract
Although seasonal influenza disease spread is a spatio-temporal phenomenon, public surveillance systems aggregate data only spatially, and are rarely predictive. We develop a hierarchical clustering-based machine learning tool to anticipate flu spread patterns based on historical spatio-temporal flu activity, where we use historical influenza-related emergency department records as a proxy for flu prevalence. This analysis replaces conventional geographical hospital clustering with clusters based on both spatial and temporal distance between hospital flu peaks to generate a network illustrating whether flu spreads between pairs of clusters (direction) and how long that spread takes (magnitude). To overcome data sparsity, we take a model-free approach, treating hospital clusters as a fully-connected network, where arcs indicate flu transmission. We perform predictive analysis on the clusters' time series of flu ED visits to determine direction and magnitude of flu travel. Detection of recurrent spatio-temporal patterns may help policymakers and hospitals better prepare for outbreaks. We apply this tool to Ontario, Canada using a five-year historical dataset of daily flu-related ED visits, and find that in addition to expected flu spread between major cities/airport regions, we were able to illuminate previously unsuspected patterns of flu spread between non-major cities, providing new insights for public health officials. We showed that while a spatial clustering outperforms a temporal clustering in terms of the direction of the spread (81% spatial v. 71% temporal), the opposite is true in terms of the magnitude of the time lag (20% spatial v. 70% temporal).
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".