Impact of Air Quality on COVID-19 Confirmed and Death Cases: Evidence from China
Bibliographic record
Abstract
Abstract Coronavirus disease 2019 (COVID-19) has triggered the most significant public health crisis in recent years. However, studies on the impact of air quality on COVID-19 confirmed cases and death cases are still limited. This study examines the impact of the air quality on daily COVID-19 confirmed cases and deaths in prefecture-level cities in China from January 22, 2020, to December 31, 2020. The zero-inflated Poisson regression is applied in this paper to explore the impact of air pollution levels on daily confirmed cases and daily death cases. We find significant evidence for air quality index (AQI) to impact daily confirmed COVID-19 cases positively. By further decomposing AQI to its components, we find a significant positive impact from SO2, NO2, O3, and CO on daily confirmed COVID-19 cases. Results also show a positively significant impact from PM10, SO2, NO2, O3, and CO on daily dead COVID-19 cases. This study provides new insights into the relationship between air quality on daily COVID-19 confirmed cases and death cases in China and has direct implications for COVID-19 epidemic prevention and control.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".