Causal Networks of Infodemiological Data: Modelling Dermatitis (2503.08252v2)

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.