Interpretable Machine Learning and Spatiotemporal Modeling of Meteorological and Environmental Drivers for Tuberculosis Incidence in China.

Zihao Wang, Siyuan Li, Xiaotong Jiang, Kang Hu, Yangzhou Wu

Abstract read

In one paragraph

Article in Toxics, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it

0cells of the map it votes in

0citing papers in PubMed

–field-weighted citation impact

1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

5 authors.

Zihao WangState Key Laboratory of Spatial Datum, College of Geographical Sciences, Henan University, Zhengzhou 450046, China.

Siyuan LiState Key Laboratory of Spatial Datum, College of Geographical Sciences, Henan University, Zhengzhou 450046, China.

Xiaotong JiangShandong Key Laboratory of Eco-Environmental Science for the Yellow River Delta, Shandong University of Aeronautics, Binzhou 256603, China.ORCID 0000-0003-0954-5480

Kang HuJiangsu Collaborative Innovation Center of Atmospheric Environment and Equipment Technology, Nanjing University of Information Science & Technology, Nanjing 210044, China.

Yangzhou WuSchool of Environment and Architecture, University of Shanghai for Science and Technology, Shanghai 200093, China.

Funding

National Natural Science Foundation of China 42407131

6 · The paper itself

Abstract

Tuberculosis (TB) remains a major public health burden in China. Although meteorological and environmental factors are recognized to influence TB transmission, their non-linear effects and spatiotemporal heterogeneity have not been fully elucidated. Based on monthly TB incidence data from 31 provinces in China during 2005-2020, this study systematically investigated these effects by integrating nine meteorological and air pollution variables within a combined machine learning and spatial statistical modeling framework. The results indicated that the Extreme Gradient Boosting (XGBoost) model effectively captured the complex non-linear relationships between environmental exposure and TB incidence. SHAP interpretability analysis identified surface pressure (SP), vegetation coverage, and PM

Indexed as

air pollutionGeographically and Temporally Weighted Regression (GTWR)machine learningmeteorological driversspatial heterogeneitytuberculosis

Identifiers

PMID42347435

DOI10.3390/toxics14060537

PMCPMC13307691

What OpenQuestion holds

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Registered trials

None linked

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.