Comparison of Climatic Factors Contributing to Hand-Foot-and-Mouth Disease Outbreak in the Northern and the Central Regions of Thailand
Bibliographic record
Abstract
Background: Hand-foot-and-mouth disease (HFMD) has been noted as one of the most common contagious diseases in Thailand. Each year the highest reported cases have been mostly found in the Northern and the Central regions. These regions are quite different in terms of topography and climate. Therefore, the interest of this research was to compare the climatic factors that affect the incidence of HFMD outbreak. Objective: The research objective was to identify the climatic factors influencing HFMD in the two regions. Methods: The research applied spatial autocorrelation via the stepwise regression analysis to elaborate the influence of climatic factors on HFMD outbreaks during 2006-2016. Results: The HFMD distribution patterns mapping in this study indicated that there were large infectious areas in almost every province in both the Northern and the Central regions during 2012-2016. The stepwise regression analysis evaluated all possible combinations of the explanatory input candidate variables, including average temperature, average rainfall, air pressure, and relative humidity. The study finds found that the major climate factors pertaining to HFMD occurrence in the Northern region were temperature and humidity (R2 = 0.56), whereas humidity and rainfall (R2 = 0.49) played important roles in the Central region. The results confirmed the meteorological factors which were statistically significant in association with HFMD cases in seasonal of Thailand. Conclusion: It was concluded that the use of spatial autocorrelation in GIS and stepwise regression approach should be encouraged in epidemiology in estimating the involvement of meteorological indicators on the spatial distribution of HFMD and health geography in climate change situation.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".