Validity of air quality as a measure of human mobility. The COVID-19 context.
Bibliographic record
Abstract
Abstract Background Mobility patterns are valuable in identifying transmission patterns for infectious diseases and in parametrizing mathematical models. Aggregated location data from mobile phones which have been the main means of measuring human mobility on a population level come with several limitations. Methods We explored the viability of using ground monitored air quality data as an alternative to aggregated location data from mobile phones in two cities in Uganda. We determined associations between air quality and human mobility and the effect of mobility restrictions on mobility and air quality using Pearson correlation (R), multivariable regression and visualized relationships using scatter plots. Results Fine particulate matter (PM 2.5 ) was negatively correlated with the government response stringency index for Kampala (R = -0.31, p<0.001) and Wakiso (R = -0.21, p<0.001). In Kampala, PM 2.5 was positively associated with movement in grocery and pharmacy (R = 0.24, p<0.001), parks (R = 0.25, p<0.001), retail and recreation (R = 0.24, p<0.001), transit stations (R = 0.3, p<0.001) and work places (R = 0.2, p<0.001); and negatively correlated with movement in residential places (R = -0.3, p<0.001). Only associations between PM 2.5 and movement in workplaces and residential places were statistically significant in Wakiso (R = 0.14, p<0.001 and R = -0.19, p = 0.003 respectively). Conclusions These findings suggest that air quality data are linked to human mobility data and could thus be used to monitor human movement patterns. This is a pioneer study to assess the value of air quality as a surrogate for human mobility.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.043 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.003 | 0.010 |
| Research integrity | 0.000 | 0.003 |
| Insufficient payload (model declined to judge) | 0.032 | 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; both teacher heads agree on what is shown here.
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".