Measuring short-term mobility patterns in North America using Facebook advertising data, with an application to adjusting COVID-19 mortality rates
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
BACKGROUND: Patterns in short-term population mobility are important to understand, but the data required to measure such movements are often not available from traditional sources. OBJECTIVE: To investigate patterns in short-term population mobility in all states and provinces in the United States and Canada using data collected from Facebook’s advertising platform. METHODS: We collected daily traveler data from Facebook’s advertising platform, summarized the main characteristic patterns observed across geographic regions, and also used the traveler rates to adjust COVID-19 mortality rates over the period July 2020 to July 2021. RESULTS: Rates of short-term travel vary substantially by 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. In addition, accounting for travelers in population denominators leads to about a 1% difference in implied mortality rates, with substantial variation across demographic groups and regions. CONCLUSIONS: Short-term population mobility can vary substantially over the course of a year, which has implications for resource planning and the population at risk of health outcomes by geography. CONTRIBUTION: This work highlights the potential for data collected through social media websites to provide insight into short-term mobility patterns.
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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 | 0.009 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| 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 it