Validating Behavioral Proxies for Disease Risk Monitoring via Large-Scale E-commerce Data

TL;DR

This study validates that e-commerce purchase data can serve as a reliable, scalable proxy for disease risk monitoring, demonstrated through a case study on feline urinary disease, showing strong correlation with clinical records.

Contribution

It introduces a validated behavioral proxy derived from e-commerce data for disease monitoring, demonstrating its effectiveness compared to traditional clinical data sources.

Findings

Strong correlation (r=0.74) between EC data and clinical risk patterns.

EC data accurately captured seasonal disease dynamics (r=0.82).

Reproduced known protective effects of wet food consumption using EC data.

Abstract

Digital traces of daily activities, such as e-commerce (EC) purchase histories, provide scalable signals for public health surveillance, yet their epidemiological validity remains unclear. This study validates a behavioral proxy for disease onset, defined as transitions from regular to therapeutic diets, by comparing large-scale EC data (N=55,645) against independent insurance-derived clinical records. Using feline lower urinary tract disease (FLUTD) as a case study, the proxy showed strong agreement with clinical data for ingredient-level risk patterns (r=0.74) and seasonal dynamics (r=0.82). Furthermore, analysis using EC data alone reproduced the established protective association of wet food consumption. These results demonstrate that validated behavioral signals from EC data can serve as cost-effective complements to traditional surveillance, with potential applicability to monitoring lifestyle-related diseases in human populations.