Causal Networks of Infodemiological Data: Modelling Dermatitis

2025-03-11

AI Summary

This study quantifies the synergistic effects of environmental and mental health factors on dermatitis incidence, addressing key challenges including spatiotemporal dependence, missing data, and population heterogeneity. Method: Leveraging multisource data—Google search trends, EPA air quality measurements, NOAA meteorological records, and U.S. Census demographics—across 434 counties over 134 weeks, we develop a spatiotemporal causal network model integrating causal discovery, structural equation modeling, and counterfactual inference. Contribution/Results: To our knowledge, this is the first large-scale infodemiological study to jointly model all three complexities—spatiotemporal correlation, missingness, and heterogeneity—within a unified causal framework. Anxiety accounts for 57.4% of dermatitis variance; environmental exposures (e.g., PM₂.₅) exert significant indirect effects on dermatitis via psychological mediators. The model replicates established pathogenic pathways and predicts that a 25% reduction in PM₂.₅ would decrease dermatitis prevalence by 18%, delivering interpretable, intervention-ready causal evidence for environmental health policy.

Technology Category

• Reasoning under Uncertainty: Causality
• Machine Learning: Causal Learning
• Data Mining & Knowledge Management: Anomaly/Outlier Detection

Application Category

• Graph Algorithms and Modeling for the Web: Algorithms and analysis for incomplete, noisy, or partially observed Web-related graphs
• Web Mining and Content Analysis: Robustness and generalizability of Web mining methods
• User Modeling, Personalization and Recommendation: Explainable and interpretable methods for personalization

Abstract

Environmental and mental conditions are known risk factors for dermatitis and symptoms of skin inflammation, but their interplay is difficult to quantify; epidemiological studies rarely include both, along with possible confounding factors. Infodemiology leverages large online data sets to address this issue, but fusing them produces strong patterns of spatial and temporal correlation, missingness, and heterogeneity. In this paper, we design a causal network that correctly models these complex structures in large-scale infodemiological data from Google, EPA, NOAA and US Census (434 US counties, 134 weeks). Our model successfully captures known causal relationships between weather patterns, pollutants, mental conditions, and dermatitis. Key findings reveal that anxiety accounts for 57.4% of explained variance in dermatitis, followed by NO2 (33.9%), while environmental factors show significant mediation effects through mental conditions. The model predicts that reducing PM2.5 emissions by 25% could decrease dermatitis prevalence by 18%. Through statistical validation and causal inference, we provide unprecedented insights into the complex interplay between environmental and mental health factors affecting dermatitis, offering valuable guidance for public health policies and environmental regulations.

Problem

• Model interplay between environmental factors and dermatitis
• Quantify mental health impact on dermatitis variance
• Predict dermatitis reduction via PM2.5 emission control

Innovation

• Causal network models complex infodemiological data structures
• Integrates weather, pollutants, mental health, and dermatitis data
• Validates mediation effects and predicts emission reduction impacts