ArticleScientific reports2022
Correlation between air pollution and prevalence of conjunctivitis in South Korea using analysis of public big data.
Article in Scientific reports, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed, 13 citations in OpenAlex.
- Environmental Triggers and Ocular Disease Networks: Analyzing the Impact of Air Pollutants and Meteorological Factors Using Fuzzy Cognitive Maps.Environment & health (Washington, D.C.) · 2026Article
- Models Estimating the Effects of Changes in the Concentration of Specific Climatic Pollutants and Temperature on the Prevalence of Acute Ophthalmic Inflammatory Conditions: A Retrospective Cross-Sectional Observational Study.Journal of ophthalmology · 2026Article
- Ambient air pollutants and outpatient visits for conjunctivitis in Hefei, China: a time-series study of hospital-based data during 2014-2023.Frontiers in public health · 2026Article
- Weather, Air Pollution, and Seasonality: Predictors of Pathogen-Associated Conjunctivitis Outbreaks in Kathmandu Valley, Nepal.The American journal of tropical medicine and hygiene · 2025Article
- Epidemiological characteristics of elderly osteoporosis fractures and their association with air pollutants: a multi-center study in Hebei Province.Scientific reports · 2024Article
- Effects of climate factors and Demodex infestation on meibomian gland dysfunction-associated dry eye diseases.Scientific reports · 2024Article
- Impacts of climate change on ocular health: A scoping review.The journal of climate change and healthArticle
Corrections and comments
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Authors and funding
7 authors at 4 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
This study investigated how changes in weather factors affect the prevalence of conjunctivitis using public big data in South Korea. A total of 1,428 public big data entries from January 2013 to December 2019 were collected. Disease data and basic climate/air pollutant concentration records were collected from nationally provided big data. Meteorological factors affecting eye diseases were identified using multiple linear regression and machine learning analysis methods such as extreme gradient boosting (XGBoost), decision tree, and random forest. The prediction model with the best performance was XGBoost (1.180), followed by multiple regression (1.195), random forest (1.206), and decision tree (1.544) when using root mean square error (RMSE) values. With the XGBoost model, province was the most important variable (0.352), followed by month (0.289) and carbon monoxide exposure (0.133). Other air pollutants including sulfur dioxide, PM
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.