arXiv subjects

Qingtao Cao

Publications and source records attributed to Qingtao Cao.

3 recordsLinked to original sources

arXiv2026

When Networks Substitute for Outcome Surveillance? A Substitution-Complementarity Framework for Behavioral Signals in Predictive Monitoring

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.

physics.soc-ph↗

arXiv2023

Deterministic Random Walk Model in NetLogo and the Identification of Asymmetric Saturation Time in Random Graph

Interactive programming environments are powerful tools for promoting innovative network thinking, teaching science of complexity, and exploring emergent phenomena. This paper reports on our recent development of the deterministic random walk model in NetLogo, a leading platform for computational thinking, eco-system thinking, and multi-agent cross-platform programming environment. The deterministic random walk is foundational to modeling dynamical processes on complex networks. Inspired by the temporal visualizations offered in NetLogo, we investigated the relationship between network topology and diffusion saturation time for the deterministic random walk model. Our analysis uncovers that in Erdős-Rényi graphs, the saturation time exhibits an asymmetric pattern with a considerable probability of occurrence. This behavior occurs when the hubs, defined as nodes with relatively higher number of connections, emerge in Erdős-Rényi graphs. Yet, our analysis yields that the hubs in Barabási-Albert model stabilize the the convergence time of the deterministic random walk model. These findings strongly suggest that depending on the dynamical process running on complex networks, complementing characteristics other than the degree need to be taken into account for considering a node as a hub. We have made our development open-source, available to the public at no cost at https://github.com/bravandi/NetLogo-Dynamical-Processes.

cs.MA↗

arXiv2019

Mixed-Integer Optimization Approach to Learning Association Rules for Unplanned ICU Transfer

After admission to emergency department (ED), patients with critical illnesses are transferred to intensive care unit (ICU) due to unexpected clinical deterioration occurrence. Identifying such unplanned ICU transfers is urgently needed for medical physicians to achieve two-fold goals: improving critical care quality and preventing mortality. A priority task is to understand the crucial rationale behind diagnosis results of individual patients during stay in ED, which helps prepare for an early transfer to ICU. Most existing prediction studies were based on univariate analysis or multiple logistic regression to provide one-size-fit-all results. However, patient condition varying from case to case may not be accurately examined by the only judgment. In this study, we present a new decision tool using a mathematical optimization approach aiming to automatically discover rules associating diagnostic features with high-risk outcome (i.e., unplanned transfers) in different deterioration scenarios. We consider four mutually exclusive patient subgroups based on the principal reasons of ED visits: infections, cardiovascular/respiratory diseases, gastrointestinal diseases, and neurological/other diseases at a suburban teaching hospital. The analysis results demonstrate significant rules associated with unplanned transfer outcome for each subgroups and also show comparable prediction accuracy, compared to state-of-the-art machine learning methods while providing easy-to-interpret symptom-outcome information.

cs.LG↗