In this study, we investigate the under-explored intervention planning aimed at disseminating accurate information within dynamic opinion networks by leveraging learning strategies. Intervention planning involves identifying key nodes (search) and exerting control (e.g., disseminating accurate/official information through the nodes) to mitigate the influence of misinformation. However, as the network size increases, the problem becomes computationally intractable. To address this, we first introduce a ranking algorithm to identify key nodes for disseminating accurate information, which facilitates the training of neural network (NN) classifiers that provide generalized solutions for the search and planning problems. Second, we mitigate the complexity of label generation--which becomes challenging as the network grows--by developing a reinforcement learning (RL)-based centralized dynamic planning framework.

Our experimental results demonstrate that the ranking algorithm-based classifiers provide plans that enhance infection rate control, especially with increased action budgets for small networks.

This information would be provided by experts in the chosen domain, i.e. epidemiologists working on epidemic

Gaussian distribution that the inverse of the Gaussian covariance matrix is the partial correlation. These are all publicly available databases. In Figure D.1, we plot the covariates used in the disease In Figure D.2, we plot the estimated infection D.5, we show the additional results for the community-level COVID transmission model in estimating Here we describe the procedure to construct the confidence intervals for the parameters in the spatiotemporal model. We did not permute across areas as it might disturb spatial correlation. Figure D.3: Rooted mean squared errors (RMSEs) in estimating the time-varying parameters in the RMSE value was calculated over all areas and time points in each replication.

We design a weighting scheme to mitigate multiple selection biases inherited in EHRs of COVID patients.

Our experimental results demonstrate that the ranking algorithm-based classifiers provide plans that enhance infection rate control, especially with increased action budgets for small networks.

This study presents an integrated approach to understanding epidemic dynamics through a stochastic spatio-temporal simulation model on a multiplex network, blending physical and informational layers. The physical layer maps the geographic movement of individuals, while the information layer tracks the spread of knowledge and health behavior via social interactions. We explore the interplay between physical mobility, information flow, and epidemic outcomes by simulating disease spread within this dual-structured network. Our model employs stochastic elements to mirror human behavior, mobility, and information dissemination uncertainties. Through simulations, we assess the impact of network structure, mobility patterns, and information spread speed on epidemic dynamics. The findings highlight the crucial role of effective communication in curbing disease transmission, even in highly mobile societies. Additionally, our agent-based simulation allows for real-time scenario analysis through a user interface, offering insights into leveraging physical and informational networks for epidemic control. This research sheds light on designing strategic interventions in complex social systems to manage disease outbreaks.