@article{wan_zhang_song_2021, title={Multi-Objective Model-based Reinforcement Learning for Infectious Disease Control}, DOI={10.1145/3447548.3467303}, abstractNote={Severe infectious diseases such as the novel coronavirus (COVID-19) pose a huge threat to public health. Stringent control measures, such as school closures and stay-at-home orders, while having significant effects, also bring huge economic losses. In the face of an emerging infectious disease, a crucial question for policymakers is how to make the trade-off and implement the appropriate interventions timely given the huge uncertainty. In this work, we propose a Multi-Objective Model-based Reinforcement Learning framework to facilitate data-driven decision-making and minimize the overall long-term cost. Specifically, at each decision point, a Bayesian epidemiological model is first learned as the environment model, and then the proposed model-based multi-objective planning algorithm is applied to find a set of Pareto-optimal policies. This framework, combined with the prediction bands for each policy, provides a real-time decision support tool for policymakers. The application is demonstrated with the spread of COVID-19 in China.}, journal={KDD '21: PROCEEDINGS OF THE 27TH ACM SIGKDD CONFERENCE ON KNOWLEDGE DISCOVERY & DATA MINING}, author={Wan, Runzhe and Zhang, Xinyu and Song, Rui}, year={2021}, pages={1634–1644} }
 @article{li_yao_zhang_2020, title={A change-point detection and clustering method in the recurrent-event context}, volume={90}, ISSN={["1563-5163"]}, DOI={10.1080/00949655.2020.1718149}, abstractNote={Change-point detection in the context of recurrent-event is a valuable analysis tool for the identification of the intensity rate changes. It has been an interesting topic in many fields, such as medical studies, travel safety analysis, etc. If subgroups exist, clustering can be incorporated into the change-point detection to improve the quality of the results. This paper develops a new algorithm named Recurrent-K-means to detect the change-points of the intensity rates and identify clusters of objects with recurrent events. It also proposes a test-based method to perform a heuristic search in determining the number of underlying clusters. In this study, the objects are assumed to fall in several clusters while the objects in the same cluster share identical change-points. The event count for an object is assumed to be a non-homogeneous Poisson process with a piecewise-constant intensity function. The methodology estimates the change-point as well as the intensity rates before and after the change-point for each cluster. The methodology establishes a clustering analysis based on K-means algorithm but enhances the procedure to be model based. The simulation study shows that the methodology performs well in parameter estimation and determination of the number of clusters in different scenarios. The methodology is applied to the UK coal mining disaster data to show its possible role in shaping government regulations and improving coal industry safety.}, number={6}, journal={JOURNAL OF STATISTICAL COMPUTATION AND SIMULATION}, author={Li, Qing and Yao, Kehui and Zhang, Xinyu}, year={2020}, month={Apr}, pages={1131–1149} }