Semantic Attribution Signals for Allergic Rhinitis Nowcasting

Digital epidemiology has largely treated search queries and social-media posts as volume proxies for incidence. Such counts capture attention rather than interpretation and are sensitive to media shocks and platform shifts. We argue for a semantic attribution layer that encodes how users attribute symptoms to putative causes. Focusing on allergic rhinitis (AR) in England, we type causal statements in TikTok descriptions into environmental, infectious and other triggers, and aggregate them into weekly, confidence-weighted indices that are Beta-smoothed, exponentially smoothed, and volume-gated per platform. These indices are fused with Google Trends and TikTok via exogenous regressors and confidence-weighted gating. In an illustrative nowcasting study across horizons h = 0...4, adding TikTok to Google Trends improves short-horizon performance for non-linear models; raw causal counts are unstable; and typed, gated attribution yields more consistent gains for tree-based and boosting mod els while often harming linear/ARIMA baselines. We outline a compact specification for attribution-aware pipelines and practical guidance for integrating this semantic layer into digital surveillance.