Measuring short-term mobility patterns in North America using Facebook Advertising data, with an application to adjusting Covid-19 mortality rates
Bibliographic record
Abstract
Patterns and trends in short-term mobility are important to understand, but data required to measure such movements are often not available from traditional sources. We collected daily data from Facebook’s Advertising Platform to measure short-term mobility across all states and provinces in the United States and Canada. We show that rates of short-term travel vary substantially over geographic area, but also by age and sex, with the highest rates of travel generally for males. Strong seasonal patterns are apparent in travel to many areas, with different regions experiencing either increased travel or decreased travel over winter, depending on climate. Further, some areas appear to show marked changes in mobility patterns since the onset of the pandemic. We used the traveler rates constructed from Facebook to adjust Covid-19 mortality rates over the period July 2020 to July 2021, and showed that accounting for travelers leads to on average a 3 per cent difference in implied mortality rates, with substantial variation across demographic groups and regions.
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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 | 0.004 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".