The Impact of Meteorological Factors on Influenza Virus Prevalence in Hong Kong: An Analysis of Type A/B Positive Samples and Weather Variables
Bibliographic record
Abstract
This research looks at how weather (temp, air pressure, humidity, UV index and the like) and population density affect influenza A/B in Hong Kong through analysis of 539 observations (Jan 2015–Apr 2025). By means of Ordinary least squares (OLS) regression and Granger causality tests, weekly data obtained from the Hong Kong’s Open Data Platform, the Hong Kong Observatory and the Centre for Health Protection was analyzed. According to the findings, cases was found to be negatively associated with temperature (β = -36.85, p < 0.001) and air pressure (β = -6.998, p < 0.001), while it was found to be positively associated with relative humidity (β = 6.43, p = 0.009), UV index (β = 58.35, p = 0.003), and population density (β = 1.10, p < 0.001) through regression analysis. The evidence of Granger causality has shown that the temperature, air pressure, and UV index influence influenza. However, humidity and UV index affects flu in the opposite way. This is probably due to behavioral feedback leading to indoor crowding. The findings informed targeted surveillance in subtropical urban settings, highlighting the impact of temperature-driven risks and density-adapted interventions. Public health alerts should be triggered by temperature declines and pressure rises for policy implications and early warning systems. The Hong Kong government should upgrades take priority in crowded districts during cold-humid seasons.
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 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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".