ArticleInternational journal of environmental research and public health2023
Machine Learning Prediction Model of Tuberculosis Incidence Based on Meteorological Factors and Air Pollutants.
Article in International journal of environmental research and public health, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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12 citing papers in PubMed, 22 citations in OpenAlex.
- Predicting Tuberculosis Outcomes Using Routine Surveillance Data in Chiang Mai, Thailand: Retrospective Cohort Study.JMIR public health and surveillance · 2026Article
- Article
- Identification of risk factors for latent tuberculosis infection in Xinjiang using machine learning.BMC public health · 2025Article
- Integrated Artificial Intelligence Framework for Tuberculosis Treatment Abandonment Prediction: A Multi-Paradigm Approach.Journal of clinical medicine · 2025Article
- Combined effects of air pollution and meteorological factors on the risk of newly tuberculosis cases: a time-series study in Tibet, China.International journal of biometeorology · 2025Article
- A machine learning model and molecular clusters of epigenetic chromatin regulators in tuberculosis based on bioinformatics and clinical samples.Scientific reports · 2025Article
- Investigation of tuberculosis incidence and particulate matter concentration in the middle east.Scientific reports · 2025Article
- Predictive modelling of air pollution affecting human tuberculosis risk on Mainland China.Scientific reports · 2025Article
- A Forecast Model for COVID-19 Spread Trends Using Blog and GPS Data from Smartphones.Entropy (Basel, Switzerland) · 2025Article
- Predicting and improving diagnosis of tuberculosis outcomes in South Africa using machine learning techniques.PLOS global public health · 2025Article
- A hybrid machine learning model for pulmonary tuberculosis forecasting of Chongqing with adjacent-region data.PloS one · 2025Article
- Spatial and temporal analysis and forecasting of TB reported incidence in western China.BMC public health · 2024Article
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Authors and funding
10 authors at 3 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundTuberculosis (TB) is a public health problem worldwide, and the influence of meteorological and air pollutants on the incidence of tuberculosis have been attracting interest from researchers. It is of great importance to use machine learning to build a prediction model of tuberculosis incidence influenced by meteorological and air pollutants for timely and applicable measures of both prevention and control.
methodsThe data of daily TB notifications, meteorological factors and air pollutants in Changde City, Hunan Province ranging from 2010 to 2021 were collected. Spearman rank correlation analysis was conducted to analyze the correlation between the daily TB notifications and the meteorological factors or air pollutants. Based on the correlation analysis results, machine learning methods, including support vector regression, random forest regression and a BP neural network model, were utilized to construct the incidence prediction model of tuberculosis. RMSE, MAE and MAPE were performed to evaluate the constructed model for selecting the best prediction model.
results(1) From the year 2010 to 2021, the overall incidence of tuberculosis in Changde City showed a downward trend. (2) The daily TB notifications was positively correlated with average temperature (r = 0.231), maximum temperature (r = 0.194), minimum temperature (r = 0.165), sunshine duration (r = 0.329), PM
conclusionsThe prediction trend of the BP neural network model, including average daily temperature, sunshine hours and PM
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