Validity of Air Quality as a Measure of Human Mobility in Uganda. The COVID-19 Context.
Bibliographic record
Abstract
Abstract Background Mobility patterns are valuable in identifying transmission patterns for infectious diseases and in deriving contact matrices that are used to parametrize mathematical models. Aggregated location data from mobile phones have been the main means of measuring human mobility on a population level. However, these data come with several limitations related to individual privacy, access and restriction of the GPS location by the user that limit their use. Methods We explored the viability of using ground monitored air quality data as an alternative to aggregated location data from mobile phones, as a measure of human mobility 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), multivariate regression and visualized these relationships using scatter plots. Results Daily mean levels for PM2.5 in both cities were consistently higher than the WHO guideline limit, with a mean of 77.0µg/m3 (Range = 22.0–309) for Kampala and 60.0µg/m3 (Range = 18.2–331) for Wakiso. PM10 levels had a mean of 84.6µg/m3 (Range = 25.0–318) in Kampala and 67.9µg/m3 (Range = 21.0–340) in Wakiso. PM2.5 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, PM2.5 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 PM2.5 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 work represents a pioneer study to empirically and quantitatively assess the value of air quality data as a surrogate for human mobility in Uganda.
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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 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.000 | 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".