When Networks Substitute for Outcome Surveillance? A Substitution-Complementarity Framework for Behavioral Signals in Predictive Monitoring
For designers of predictive monitoring systems, this provides a cost-aware rule for when to integrate behavioral signals versus relying on autoregressive baselines.
The paper studies when behavioral data (mobility networks) provides predictive information beyond outcome-based surveillance, using COVID-19 forecasting in Massachusetts. It finds that network features yield large out-of-sample gains (Predict-R2 from 0.60 to 0.83-0.89) when primary surveillance is degraded, but only marginal lift when local histories are available.
Monitoring systems increasingly fuse dynamic behavioral data with outcome-based surveillance, raising a basic question: when does behavioral data carry predictive information that outcome history lacks? We study this using epidemic forecasting on mobility networks, asking whether mobility networks provide independent predictive signal beyond local outcome-based surveillance. We formalize this as a substitution-complementarity problem over directed, weighted mobility networks. Using a Frisch-Waugh-Lovell variance decomposition, our analytical framework derives domain-agnostic conditions under which network-topology features retain incremental explanatory power beyond autoregressive outcome histories. We instantiate the framework using town-level COVID-19 forecasting in Massachusetts (April 2020-April 2021), constructing mobility networks among 300+ towns from smartphone-derived origin-destination aggregates to extract centrality metrics. An agent-based model on synthetic networks confirms that the regime boundary arises from a generic interaction between macro-scale epidemic state and network topology, rather than dataset-specific artifacts. Prevalence-gated interactions between statewide incidence and network features yield large out-of-sample gains when primary surveillance is degraded (Predict-R2 increases from about 0.60 to 0.83-0.89) but only marginal lift when granular local histories are available (+0.5 percentage points). Gains concentrate during epidemic waves when behavioral responses shift network connectivity rapidly. Framed as a value-of-information problem, the substitution gain reflects the marginal value of behavioral data relative to primary-channel quality. This yields a transferable, cost-aware design rule: integrate topology-aware behavioral signals when primary surveillance is degraded or the network changes rapidly; otherwise, rely on autoregressive baselines.