Prominent Spatiotemporal Waves of COVID-19 Incidence in the United States: Implications for Causality, Forecasting, and Control
Bibliographic record
Abstract
Abstract Better understanding of the spatiotemporal structure of the COVID-19 epidemic in the USA may help inform more effective prevention and control strategies. By analyzing daily COVID-19 case data in the United States, Mexico and Canada, we found four continental-scale epidemic wave patterns, including travelling waves, that spanned multiple state and even international boundaries. These major epidemic patterns co-varied strongly with continental-scale seasonal temperature change patterns. Geo-contiguous states shared similar timing and amplitude of epidemic wave patterns irrespective of similarities or differences in state government political party affiliations. These analyses provide evidence that seasonal factors, probably weather changes, have exerted major effects on local COVID-19 incidence rates. Seasonal wave patterns observed during the first year of the epidemic may become repeated in the subsequent years. One Sentence Summary The COVID-19 epidemic in the United States has consisted of four continental-scale spatiotemporal waves of case incidence that have spanned multiple states and even international boundaries.
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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 | 0.003 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".