Browsing by Author "Liu, Xueying"

Now showing 1 - 4 of 4
  • Thumbnail Image
    Item
    Hawkes process modeling of COVID-19 with mobility leading indicators and spatial covariates
    (Elsevier, 2021-07) Chiang, Chiang; Liu, Xueying; Mohler, George; Computer and Information Science, School of Science
    Hawkes processes are used in statistical modeling for event clustering and causal inference, while they also can be viewed as stochastic versions of popular compartmental models used in epidemiology. Here we show how to develop accurate models of COVID-19 transmission using Hawkes processes with spatial–temporal covariates. We model the conditional intensity of new COVID-19 cases and deaths in the U.S. at the county level, estimating the dynamic reproduction number of the virus within an EM algorithm through a regression on Google mobility indices and demographic covariates in the maximization step. We validate the approach on both short-term and long-term forecasting tasks, showing that the Hawkes process outperforms several models currently used to track the pandemic, including an ensemble approach and an SEIR-variant. We also investigate which covariates and mobility indices are most important for building forecasts of COVID-19 in the U.S.
  • Thumbnail Image
    Item
    Point process modeling of drug overdoses with heterogeneous and missing data
    (Institute of Mathematical Statistics, 2021) Liu, Xueying; Carter, Jeremy; Ray, Brad; Mohler, George; Computer and Information Science, School of Science
    Opioid overdose rates have increased in the United States over the past decade and reflect a major public health crisis. Modeling and prediction of drug and opioid hotspots, where a high percentage of events fall in a small percentage of space–time, could help better focus limited social and health services. In this work we present a spatial-temporal point process model for drug overdose clustering. The data input into the model comes from two heterogeneous sources: (1) high volume emergency medical calls for service (EMS) records containing location and time but no information on the type of nonfatal overdose, and (2) fatal overdose toxicology reports from the coroner containing location and high-dimensional information from the toxicology screen on the drugs present at the time of death. We first use nonnegative matrix factorization to cluster toxicology reports into drug overdose categories, and we then develop an EM algorithm for integrating the two heterogeneous data sets, where the mark corresponding to overdose category is inferred for the EMS data and the high volume EMS data is used to more accurately predict drug overdose death hotspots. We apply the algorithm to drug overdose data from Indianapolis, showing that the point process defined on the integrated data out-performs point processes that use only coroner data (AUC improvement 0.81 to 0.85). We also investigate the extent to which overdoses are contagious, as a function of the type of overdose, while controlling for exogenous fluctuations in the background rate that might also contribute to clustering. We find that drug and opioid overdose deaths exhibit significant excitation with branching ratio ranging from 0.72 to 0.98.
  • Thumbnail Image
    Item
    Temporal Event Modeling of Social Harm with High Dimensional and Latent Covariates
    (2022-08) Liu, Xueying; Mohler, George; Fang, Shiaofen; Wang, Honglang; Hasan, Mohammad A.
    The counting process is the fundamental of many real-world problems with event data. Poisson process, used as the background intensity of Hawkes process, is the most commonly used point process. The Hawkes process, a self-exciting point process fits to temporal event data, spatial-temporal event data, and event data with covariates. We study the Hawkes process that fits to heterogeneous drug overdose data via a novel semi-parametric approach. The counting process is also related to survival data based on the fact that they both study the occurrences of events over time. We fit a Cox model to temporal event data with a large corpus that is processed into high dimensional covariates. We study the significant features that influence the intensity of events.
  • Thumbnail Image
    Item
    Time-to-event modeling of subreddits transitions to r/SuicideWatch
    (IEEE, 2022) Liu, Xueying; Fang, Shiaofen; Mohler, George; Carlson, Joan; Xiao, Yunyu; Computer Science, Luddy School of Informatics, Computing, and Engineering
    Recent data mining research has focused on the analysis of social media text, content and networks to identify suicide ideation online. However, there has been limited research on the temporal dynamics of users and suicide ideation. In this work, we use time-to-event modeling to identify which subreddits have a higher association with users transitioning to posting on r/suicidewatch. For this purpose we use a Cox proportional hazards model that takes as input text and subreddit network features and outputs a probability distribution for the time until a Reddit user posts on r/suicidewatch. In our analysis we find a number of statistically significant features that predict earlier transitions to r/suicidewatch. While some patterns match existing intuition, for example r/depression is positively associated with posting sooner on r/suicidewatch, others were more surprising (for example, the average time between a high risk post on r/Wishlist and a post on r/suicidewatch is 10.2 days). We then discuss these results as well as directions for future research.