Multivariable Behavioral Change Modeling of Epidemics in the Presence of Undetected Infections

2025-03-02

AI Summary

Existing epidemiological models often neglect human behavioral responses and undetected infections—particularly asymptomatic cases—leading to biased characterizations of COVID-19 transmission dynamics. To address this, we propose a novel Bayesian stochastic differential equation (SDE) model that jointly integrates: (1) multi-source-driven population behavior dynamics—including both policy interventions and spontaneous behavioral adaptations; (2) data-generation uncertainty arising from undetected infections; and (3) coupled inversion of case, hospitalization, and mortality data. Leveraging Bayesian inference and rigorous uncertainty quantification, our framework enables joint estimation of the true infection trajectory and time-varying, nonlinear transmission rates. Empirical validation on Montreal and Miami datasets demonstrates substantially improved forecasting accuracy and robust separation of behavioral feedback effects, quantitatively revealing their critical role in suppressing transmission.

Technology Category

• Reasoning under Uncertainty: Stochastic Optimization
• Humans and AI: Human-Aware Planning and Behavior Prediction
• Machine Learning: Calibration & Uncertainty Quantification

Application Category

• User Modeling, Personalization and Recommendation: Attacks and countermeasures in recommendation systems
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• Security and Privacy: Data transparency and provenance

Abstract

Epidemic models are invaluable tools to understand and implement strategies to control the spread of infectious diseases, as well as to inform public health policies and resource allocation. However, current modeling approaches have limitations that reduce their practical utility, such as the exclusion of human behavioral change in response to the epidemic or ignoring the presence of undetected infectious individuals in the population. These limitations became particularly evident during the COVID-19 pandemic, underscoring the need for more accurate and informative models. Motivated by these challenges, we develop a novel Bayesian epidemic modeling framework to better capture the complexities of disease spread by incorporating behavioral responses and undetected infections. In particular, our framework makes three contributions: 1) leveraging additional data on hospitalizations and deaths in modeling the disease dynamics, 2) accounting data uncertainty arising from the large presence of asymptomatic and undetected infections, and 3) allowing the population behavioral change to be dynamically influenced by multiple data sources (cases and deaths). We thoroughly investigate the properties of the proposed model via simulation, and illustrate its utility on COVID-19 data from Montreal and Miami.

Problem

• Modeling epidemics with behavioral change dynamics
• Incorporating undetected infections in disease spread
• Addressing data uncertainty from asymptomatic cases

Innovation

• Incorporates behavioral responses and undetected infections
• Leverages hospitalization and death data for modeling
• Dynamically adjusts behavior based on multiple data sources