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Using geospatial social media data for infectious disease studies: a systematic review

International Journal of Digital Earth 2023年第1期16卷 130-157页

作者： Fengrui Jing Zhenlong Li Shan Qiao Jiajia Zhang Banky Olatosi Xiaoming Li Geoinformation and Big Data Research Laboratory Department of GeographyUniversity of South CarolinaColumbiaSCUSA Big Data Health Science Center University of South CarolinaColumbiaSCUSA Department of Health Promotion Educationand BehaviorArnold School of Public HealthUniversity of South CarolinaColumbiaSCUSA Department of Epidemiology and Biostatistics Arnold School of Public HealthUniversity of South CarolinaColumbiaSCUSA Department of Health Services Policy and Management Arnold School of Public HealthUniversity of South CarolinaColumbiaSCUSA

Geospatial social media(GSM)data has been increasingly used in public health due to its rich,timely,and accessible spatial information,particularly in infectious disease *** review synthesized 86 research articles that use GSM data in infectious diseases published between December 2013 and March *** articles cover 12 infectious disease types ranging from respiratory infectious diseases to sexually transmitted diseases with spatial levels varying from the neighborhood,county,state,and *** categorized these studies into three major infectious disease research domains:surveillance,explanation,and *** the assistance of advanced computing,statistical and spatial methods,GSM data has been widely and deeply applied to these domains,particularly in surveillance and explanation *** further identified four knowledge gaps in terms of contextual information use,application scopes,spatiotemporal dimension,and data limitations and proposed innovation opportunities for future *** will contribute to a better understanding of using GSM data in infectious diseases studies and provide insights into strategies for using GSM data more effectively in future research.

关键词： Public health social media infectious diseases geography spatial analysis

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Are Made and Missed Different? An analysis of Field Goal Attempts of Professional Basketball Players via Depth Based Testing Procedure

arXiv

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arXiv 2024年

作者： Qi, Kai Hu, Guanyu Wu, Wei Microsoft United States Department of Biostatistics and Data Science The University of Texas Health Science Center Houston United States Department of Statistics Florida State University United States

In this paper, we develop a novel depth-based testing procedure on spatial point processes to examine the difference in made and missed field goal attempts for NBA players. Specifically, our testing procedure can statistically detect the differences between made and missed field goal attempts for NBA players. We first obtain the depths of two processes under the polar coordinate system. A two-dimensional Kolmogorov–Smirnov test is then performed to test the difference between the depths of the two processes. Throughout extensive simulation studies, we show our testing procedure with good frequentist properties under both null hypothesis and alternative hypothesis. A comparison against the competing methods shows that our proposed procedure has better testing reliability and testing power. Application to the shot chart data of 191 NBA players in the 2017-2018 regular season offers interesting insights about these players’ made and missed shot patterns. © 2024, CC BY-SA.

关键词： Computational complexity

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Response Rate Estimation in Single-Stage Basket Trials: A Comparison of Estimators That Allow for Borrowing Across Cohorts

arXiv

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arXiv 2025年

作者： Daletzakis, Antonios Bor, Rutger van den van der Noort, Vincent Roes, Kit CB Biometrics Department Netherlands Cancer Institute Amsterdam Netherlands Dept. of Data Science and Biostatistics Julius Center for Health Sciences and Primary Care University Medical Center Utrecht Utrecht Netherlands Department of Health Evidence Section Biostatistics Radboud University Medical Center Nijmegen Netherlands

Therapeutic advancements in oncology have transitioned towards targeted therapy based on specific genomic aberrations. This shift necessitates innovative statistical approaches in clinical trials, notably in the emerging paradigm of master protocol studies. Basket trials, a type of master protocol, evaluate a single treatment across cohorts sharing a common genomic aberration but differing in tumor histology. While offering operational advantages, the analysis of basket trials introduces challenges with respect to statistical inference. Basket trials can be used to decide for which tumor histology the target treatment is promising enough to move to confirmatory clinical evaluation and can employ a Bayesian design to support this decision making. In addition to decision making, estimation of the cohort-specific response rates is highly relevant to inform the design of subsequent trials. This study evaluates seven Bayesian estimation methods for basket trials with a binary outcome, contrasted with the (frequentist) sample proportion estimate, through a simulation study. The objective is to estimate cohort-specific response rates, with a focus on average bias, average mean squared error, and the degree of information borrowing. A variety of scenarios are explored, covering homogeneous, heterogeneous, and clustered response rates across cohorts. The performance of the evaluated methods shows considerable trade-offs in bias and precision, emphasizing the importance of method selection based on trial characteristics. Berry’s method excels in scenarios with limited heterogeneity. No clear winner emerges in a more general scenario, with method performance influenced by the amount of shrinkage towards the overall mean, bias, and the choice of priors and tuning parameters in more complex settings. Challenges include the computational complexity of methods, the need for careful tuning of parameters and prior distribution specification, and the absence of clear guidance on thei

关键词： Mean square error

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HOW DO THE PROFESSIONAL PLAYERS SELECT THEIR SHOT LOCATIONS? AN ANALYSIS OF FIELD GOAL ATTEMPTS VIA BAYESIAN ADDITIVE REGRESSION TREES

arXiv

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arXiv 2025年

作者： Cao, Jiahao Yang, Hou-Cheng Hu, Guanyu Center for Spatial Temporal Modeling for Applications in Population Sciences Department of Biostatistics and Data Science The University of Texas Health Science Center HoustonTX United States Department of Statistics Florida State University Florida United States

Basketball analytics has significantly advanced our understanding of the game, with shot selection emerging as a critical factor in both individual and team performance. With the advent of player tracking technologies, a wealth of granular data on shot attempts has become available, enabling a deeper analysis of shooting behavior. However, modeling shot selection presents unique challenges due to the spatial and contextual complexities influencing shooting decisions. This paper introduces a novel approach to the analysis of basketball shot data, focusing on the spatial distribution of shot attempts, also known as intensity surfaces. We model these intensity surfaces using a Functional Bayesian Additive Regression Trees (FBART) framework, which allows for flexible, nonparametric regression, and uncertainty quantification while addressing the nonlinearity and nonstationarity inherent in shot selection patterns to provide a more accurate representation of the factors driving player performance;we further propose the Adaptive Functional Bayesian Additive Regression Trees (AFBART) model, which builds on FBART by introducing adaptive basis functions for improved computational efficiency and model fit. AFBART is particularly well suited for the analysis of two-dimensional shot intensity surfaces and provides a robust tool for uncovering latent patterns in shooting behavior. Through simulation studies and real-world applications to NBA player data, we demonstrate the effectiveness of the model in quantifying shooting tendencies, improving performance predictions, and informing strategic decisions for coaches, players, and team managers. This work represents a significant step forward in the statistical modeling of basketball shot selection and its applications in optimizing game strategies. Copyright © 2025, The Authors. All rights reserved.

关键词： Basketball

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Autonomous self-evolving research on biomedical data: the DREAM paradigm

arXiv

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arXiv 2024年

作者： Deng, Luojia Wu, Yijie Ren, Yongyong Lu, Hui Department of Bioinformatics and Biostatistics School of Life Sciences and Biotechnology Shanghai Jiao Tong University Shanghai China SJTU-Yale Joint Center for Biostatistics and Data Science Technical Center for Digital Medicine National Center for Translational Medicine Shanghai Jiao Tong University Shanghai China

In contemporary biomedical research, the efficiency of data-driven approaches is hindered by large data volumes, tool selection complexity, and human resource limitations, necessitating the development of fully autonomous research systems to meet complex analytical needs. Such a system should include the ability to autonomously generate research questions, write analytical code, configure the computational environment, judge and interpret the results, and iteratively generate more in-depth questions or solutions, all without human intervention. In this study, we developed DREAM, the first biomedical data-dRiven self-Evolving Autonomous systeM, which can independently conduct scientific research without any human involvement. Utilizing a clinical dataset and two omics datasets, DREAM demonstrated its ability to raise and deepen scientific questions, with difficulty scores for clinical data questions surpassing top published articles by 5.7% and outperforming GPT-4 and bioinformatics graduate students by 58.6% and 56.0%, respectively. Overall, DREAM has a success rate of 80% in autonomous clinical data mining. Certainly, human can participate in different steps of DREAM to achieve more personalized goals. After evolution, 10% of the questions exceeded the average scores of top published article questions on originality and complexity. In the autonomous environment configuration of the eight bioinformatics workflows, DREAM exhibited an 88% success rate, whereas GPT-4 failed to configure any workflows. Using the clinical dataset as an example, DREAM was over 10,000 times more efficient than the average scientist with a single computer core, and capable of revealing new discoveries. As a self-evolving autonomous research system, DREAM provides an efficient and reliable solution for future biomedical research. In the future, this research paradigm may also have a revolutionary impact on other data-driven scientific research fields. Copyright © 2024, The Authors. All right

关键词： Clinical research

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Assessing effects of reopening policies on COVID-19 pandemic in Texas with a data-driven transmission model

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Infectious Disease Modelling 2021年第1期6卷 461-473页

作者： Duo Yu Gen Zhu Xueying Wang Chenguang Zhang Babak Soltanalizadeh Xia Wang Sanyi Tang Hulin Wu Department of Biostatistics and Data Science The University of Texas Health Science Center at HoustonHoustonUSA School of Mathematics and Information Science Shaanxi Normal UniversityXi’anPR China

While the Coronavirus Disease 2019(COVID-19)pandemic continues to threaten public health and safety,every state has strategically reopened the business in the United *** is urgent to evaluate the effect of reopening policies on the COVID-19 pandemic to help with the decision-making on the control measures and medical resource *** this study,a novel SEIR model was developed to evaluate the effect of reopening policies based on the real-world reported COVID-19 data in *** earlier reported data before the reopening were used to develop the SEIR model;data after the reopening were used for *** simulation results show that if continuing the“stay-at-home order”without reopening the business,the COVID-19 pandemic could end in December 2020 in *** the other hand,the pandemic could be controlled similarly as the case of noreopening only if the contact rate was low and additional high magnitude of control measures could be *** the control measures are only slightly enhanced after reopening,it could flatten the curve of the COVID-19 epidemic with reduced numbers of infections and deaths,but it might make the epidemic last *** on the reported data up to July 2020 in Texas,the real-world epidemic pattern is between the cases of the low and high magnitude of control measures with a medium risk of contact rate after *** this case,the pandemic might last until summer 2021 to February 2022 with a total of 4-10 million infected cases and 20,080e58,604 deaths.

关键词： COVID-19 pandemic SEIR model Texas state Reopening business Infectious disease transmission

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Propensity-score matching analysis in COVID-19-related studies: a method and quality systematic review

arXiv

引用

arXiv 2024年

作者： Gu, Chunhui Li, Ruosha Zhang, Guoqiang Department of Neurology McGovern Medical School University of Texas Health Science Center HoustonTX United States Department of Biostatistics and Data Science School of Public Health University of Texas Health Science Center HoustonTX United States

Objectives: To provide an overall quality assessment of the methods used for COVID-19-related studies using propensity score matching (PSM). Study Design and Setting: A systematic search was conducted in June 2021 on PubMed to identify COVID-19-related studies that use the PSM analysis between 2020 and 2021. Key information about study design and PSM analysis were extracted, such as covariates, matching algorithm, and reporting of estimated treatment effect type. Results: One-hundred-and-fifty (87.72%) cohort studies and thirteen (7.60%) case-control studies were found among 171 identified articles. Forty-five studies (26.32%) provided a reasonable justification for covariates selection. One-hundred-and-three (60.23%) and Sixty-nine (40.35%) studies did not provide the model that was used for calculating the propensity score or did not report the matching algorithm, respectively. Seventy-three (42.69%) studies reported the method(s) for checking covariates balance. Forty studies (23.39%) had a statistician co-author. All the case-control studies (n=13) did not have a statistician co-author (p=0.006) and all studies that clarified the treatment effect estimation (n=6) had a statistician co-author (p Copyright © 2024, The Authors. All rights reserved.

关键词： Coronavirus

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Analysis of General Event Types and Contributing Factors in Patient Safety System data

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Proceedings of the International Symposium on Human Factors and Ergonomics in Health Care 2023年第1期12卷 103 - 107页

作者： Azade Tabaie Allan Fong Center of Biostatistics Informatics and Data Science MedStar Health Research Institute Washington DC USANational Center for Human Factors in Healthcare MedStar Health Research Institute Washington DC USA

Patient safety event reporting systems (PSRS) aim to identify safety hazards by encouraging hospital staff to report adverse events, errors, and potential errors in the healthcare system. PSRS collect different information regarding a safety event such as contributing factors and general event types. Users select contributing factors and general event types of a safety incident from predefined lists; therefore, most frequent general event types and contributing factors are commonly recorded together. Contributing factors are often related to the leading cause of an unsafe condition; therefore, identifying contributing factors can help with detecting and mitigating safety issues. In this study, we identified the general event types and contributing factors which were frequently recorded together. We calculated point-wise mutual information (PMI) between each pair to find the association between a contributing factor and a general event type. Our analysis demonstrated strong association between general event type and contributing factors which were most frequently recorded together. Fall and lost/impaired balance (PMI=15.12), skin/tissue and friction/shear (PMI=14.32), and blood bank and patient/sample incorrectly identified (PMI=13.78) were the three pairs of general event type and contributing factors which presented strong association.

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Propensity Score Analysis with Guaranteed Subgroup Balance

arXiv

引用

arXiv 2024年

作者： Li, Yan Kuo, Yong-Fang Li, Liang Department of Quantitative Health Science Division of Clinical Trials and Biostatistics Mayo Clinic RochesterMN United States Department of Biostatistics and Data Science The University of Texas Medical Branch GalvestonTX United States Department of Biostatistics The University of Texas MD Anderson Cancer Center HoustonTX United States

Estimating the causal treatment effects by subgroups is important in observational studies when the treatment effect heterogeneity may be present. Existing propensity score methods rely on a correctly specified propensity score model. Model misspecification results in biased treatment effect estimation and covariate imbalance. We proposed a new algorithm, the propensity score analysis with guaranteed subgroup balance (G-SBPS), to achieve covariate mean balance in all subgroups. We further incorporated nonparametric kernel regression for the propensity scores and developed a kernelized G-SBPS (kG-SBPS) to improve the subgroup mean balance of covariate transformations in a rich functional class. This extension is more robust to propensity score model misspecification. Extensive numerical studies showed that G-SBPS and kG-SBPS improve both subgroup covariate balance and subgroup treatment effect estimation, compared to existing approaches. We applied G-SBPS and kG-SBPS to a dataset on right heart catheterization to estimate the subgroup average treatment effects on the hospital length of stay and a dataset on diabetes self-management training to estimate the subgroup average treatment effects for the treated on the hospitalization rate. © 2024, CC BY-NC-ND.

关键词： Hospitals

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Moving Beyond Medical Statistics:A Systematic Review on Missing data Handling in Electronic Health Records

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Health data science 2024年第1期4卷 27-44页

作者： Wenhui Ren Zheng Liu Yanqiu Wu Zhilong Zhang Shenda Hong Huixin Liu the Missing data in Electronic health Records(MINDER)Group Department of Clinical Epidemiology and Biostatistics Peking University People’s HospitalBeijingChina National Institute of Health Data Science Peking UniversityBeijingChina Institute of Medical Technology Health Science Center of Peking UniversityBeijingChina 不详

Background:Missing data in electronic health records(EHRs)presents signiffcant challenges in medical *** methods have been proposed,but uncertainty exists regarding the current state of missing data addressing methods applied for EHR and which strategy performs better within speciffc ***:All studies referencing EHR and missing data methods published from their inception until 2024 March 30 were searched via the MEDLINE,EMBASE,and Digital Bibliography and Library Project *** characteristics of the included studies were *** also compared the performance of various methods under different missingness ***:After screening,46 studies published between 2010 and 2024 were *** missingness mechanisms were simulated when evaluating the missing data methods:missing completely at random(29/46),missing at random(20/46),and missing not at random(21/46).Multiple imputation by chained equations(MICE)was the most popular statistical method,whereas generative adversarial network-based methods and the k nearest neighbor(KNN)classiffcation were the common deep-learning-based or traditional machine-learning-based methods,*** the 26 articles comparing the performance among medical statistical and machine learning approaches,traditional machine learning or deep learning methods generally outperformed statistical *** and context-aware time-series imputation performed better for longitudinal datasets,whereas probabilistic principal component analysis and MICE-based methods were optimal for cross-sectional ***:Machine learning methods show signiffcant promise for addressing missing data in ***,no single approach provides a universally generalizable *** benchmarking analyses are essential to evaluate these methods across different missingness scenarios.

关键词： missing sectional Beyond

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