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Human mobility and the infectious disease transmission:a systematic review

地球空间信息科学学报（英文版） 2024年第6期27卷中插6,1824-1851页

作者： M.Naser Lessani Zhenlong Li Fengrui Jing Shan Qiao Jiajia Zhang Bankole Olatosi Xiaoming Li Geoinformation and Big Data Research Lab Department of GeographyUniversity of South CarolinaColumbiaSCUSA Big Data Health Science Center University of South CarolinaColumbiaSCUSA Big Data Health Science Center University of South CarolinaColumbiaSCUSA Department of Health Promotion Educationand BehaviorUniversity of South CarolinaColumbiaSCUSA Big Data Health Science Center University of South CarolinaColumbiaSCUSA Department of Epidemiology and Biostatistics Arnold School of Public HealthUniversity of South CarolinaColumbiaSCUSA Big Data Health Science Center University of South CarolinaColumbiaSCUSA Department of Health Services Policy and Management Arnold School of Public HealthUniversity of South CarolinaColumbiaSCUSA

Recent decades have witnessed several infectious disease outbreaks,including the coronavirus disease(COVID-19)pandemic,which had catastrophic impacts on societies around the *** the same time,the twenty-first century has experienced an unprecedented era of techno-logical development and demographic changes:exploding population growth,increased air-line flights,and increased rural-to-urban migration,with an estimated 281 million international migrants worldwide in 2020,despite COVID-19 movement *** this review,we synthesized 195 research articles that examined the association between human movement and infectious disease outbreaks to understand the extent to which human mobility has increased the risk of infectious disease *** article covers eight infectious diseases,ranging from respiratory illnesses to sexually transmitted and vector-borne *** review revealed a strong association between human mobility and infectious disease spread,particularly strong for respiratory illnesses like COVID-19 and *** significant research into the relationship between infectious diseases and human mobility,four knowl-edge gaps were identified based on reviewed literature in this study:1)although some studies have used big data in investigating infectious diseases,the efforts are limited(with the exception of COVID-19 disease),2)while some research has explored the use of multiple data sources,there has been limited focus on fully integrating these data into comprehensive analyses,3)limited research on the global impact of mobility on the spread of infectious disease with most studies focusing on local or regional outbreaks,and 4)lack of standardiza-tion in the methodology for measuring the impacts of human mobility on infectious disease *** tackling the recognized knowledge gaps and adopting holistic,interdisciplinary methods,forthcoming research has the potential to substantially enhance our comprehension of the intricate inter

关键词： Human mobility geography infectious diseases sexually transmitted diseases systematic review

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

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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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Urban Waterlogging Prediction Based on Feature Extraction and Transfer 2nd

Urban Waterlogging Prediction Based on Feature Extraction an...

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2nd International Conference on Frontiers of Energy and Environmental Engineering, CFEEE 2023

作者： Zhang, Zongjia Jian, Xinyao Chen, Yiye Huang, Zhejun Yang, Lili School of Public Management/Emergency Management Jinan University GD 20 Guangzhou China Department of Biostatistics Epidemiology and Informatics Perelman School of Medicine University of Pennsylvania PhiladelphiaPA19104-6021 United States Department of Statistics and Data Science Southern University of Science and Technology GD 755 Shenzhen China Shenzhen Technology Institute of Urban Public Safety and Key Laboratory of Urban Safety Risk Monitoring and Early Warning Ministry of Emergency Management GD 755 Shenzhen China

ISBN: (纸本)9789819703715

In waterlogging prediction, all or part of the real-time waterlogging data may be missing due to sensor failure, too sparse sampling interval setting, or sensor sensitivity problems, resulting in the failure of waterlogging prediction. In this study, we propose an urban waterlogging depth prediction method based on the transfer of waterlogging point feature extraction. The method quantifies the relationship between rainfall and waterlogging depth by extracting and constructing rainfall features at waterlogging points. Using only current or future rainfall data as model input to achieve future waterlogging depth prediction, it can effectively overcome the limitations of sparse distribution of monitoring stations and insufficient current real-time waterlogging data and can achieve more accurate medium-term waterlogging prediction and transfer prediction of water level at potential waterlogging-prone points. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Forecasting

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Combining Experimental and Historical data for Policy Evaluation 41

Combining Experimental and Historical Data for Policy Evalua...

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41st International Conference on Machine Learning, ICML 2024

作者： Li, Ting Shi, Chengchun Wen, Qianglin Sui, Yang Qin, Yongli Lai, Chunbo Zhu, Hongtu School of Statistics and Management Shanghai University of Finance and Economics China Department of Statistics London School of Economics and Political Science United Kingdom Yunnan Key Laboratory of Statistical Modeling and Data Analysis Yunnan University China Didi Chuxing China Department of Biostatistics The University of North Carolina Chapel Hill United States

This paper studies policy evaluation with multiple data sources, especially in scenarios that involve one experimental dataset with two arms, complemented by a historical dataset generated under a single control arm. We propose novel data integration methods that linearly integrate base policy value estimators constructed based on the experimental and historical data, with weights optimized to minimize the mean square error (MSE) of the resulting combined estimator. We further apply the pessimistic principle to obtain more robust estimators, and extend these developments to sequential decision making. Theoretically, we establish non-asymptotic error bounds for the MSEs of our proposed estimators, and derive their oracle, efficiency and robustness properties across a broad spectrum of reward shift scenarios. Numerical experiments and real-data-based analyses from a ridesharing company demonstrate the superior performance of the proposed estimators. Copyright 2024 by the author(s)

关键词： data assimilation

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management of unilateral neck irradiation in elderly patients with head and neck cancer: Which place for hypofractionation? A single-centre experience of 43 patients

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Cancer/Radiotherapie 2024年第8期28卷 693-698页

作者： Rakotosamimanana, Manou Renard-Oldrini, Sophie Sahki, Nassim Faivre, Jean-Christophe Department of Radiation Oncology centre Georges-François-Leclerc 1 rue du Professeur-Marion Dijon 21000 France Department of Radiation Oncology Institut de cancérologie de Lorraine Vandœuvre-lès-Nancy France Department of Biostatistics and Data Management Institut de cancérologie de Lorraine Vandœuvre-lès-Nancy France

Purpose of the study: The purpose of the study was to evaluate retrospectively the acute toxicity and efficacy in terms of locoregional control of a cervical lymph node irradiation using a hypofractionated regimen in frail elderly patients. Material and methods: Patients receiving unilateral hypofractionated cervical lymph node irradiation at the Institut de cancérologie de Lorraine (France) were retrospectively included. The treatment regimen consisted of delivering 54 Gy in 18 fractions to the high-risk tumour or lymph node volume and 45 Gy to the prophylactic volume using the technique of simultaneous integrated-boost without concomitant systemic treatment. Toxicity data according to the Common Terminology Criteria for Adverse Events version 4 and survival data were collected. Results: Forty-three patients were treated between January 2013 and July 2021. The median age was 83 years (range: 78–86.5 years), 32 patients (74.4 %) were male and 25.6 % had WHO status ≥ 2. Histological types were cutaneous squamous cell carcinoma (39 patients), salivary gland carcinoma (three patients) and Merkel cell carcinoma (one patient). The mean duration of treatment was 27 days. Seventeen patients (39.5 %) received radiotherapy alone. Acute toxicity events of grade ≥ 2 at the end of radiotherapy in the 43 patients were described as follows: 58.1 % radiodermatitis (25 patients), 24.6 % mucositis (11 patients), and 18.1 % xerostomia (eight patients). Seven patients (16.3 %) experienced grade ≥ 2 odynodysphagia, with four patients (9.3 %) requiring enteral nutrition. No grade 3 toxicity events were reported at 3 months, 6 months or 1 year. After a median follow-up of 9.2 months (range: 6–18.5 months), 11 patients (25.6 %) of those treated by exclusive irradiation had a complete locoregional response as assessed by CT-scan 3 months. At 6 months, two of 20 patients retained grade 2 xerostomia and only four of 20 patients reported residual grade 1 pain. Conclusion: Moderately hypofract

关键词： Head and neck Hypofractionated Locally Merkel cell carcinoma Radiotherapy Salivary gland Squamous cell carcinoma

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Covariate Adjustment in Randomized Experiments Motivated by Higher-Order Influence Functions

arXiv

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arXiv 2024年

作者： Zhao, Sihui Wang, Xinbo Liu, Lin Zhang, Xin School of Mathematical Sciences CMA-Shanghai Institute of Natural Sciences MOE-LSC Shanghai Jiao Tong University China Department of Bioinformatics and Biostatistics School of Life Sciences Shanghai Jiao Tong University China SJTU-Yale Joint Center for Biostatistics and Data Science Shanghai Jiao Tong University China Global Biometrics & Data Management Pfizer Inc

Higher-Order Influence Functions (HOIF), developed in a series of papers over the past twenty years, is a fundamental theoretical device for constructing rate-optimal causal-effect estimators from observational studies. However, the value of HOIF for analyzing well-conducted randomized controlled trials (RCTs) has not been explicitly explored. In the recent U.S. Food and Drug Administration (FDA) and European Medicines Agency (EMA) guidelines on the practice of covariate adjustment in analyzing RCTs, in addition to the simple, unadjusted difference-in-mean estimator, it was also recommended to report the estimator adjusting for baseline covariates via a simple parametric working model, such as a linear model. In this paper, we show that a HOIF-motivated estimator for the treatment-specific mean has significantly improved statistical properties compared to popular adjusted estimators in practice when the number of baseline covariates p is relatively large compared to the sample size n. We also characterize the conditions under which the HOIF-motivated estimator improves upon the unadjusted one. Furthermore, we demonstrate that a novel debiased adjusted estimator proposed recently by Lu et al. is, in fact, another HOIF-motivated estimator in disguise. Numerical and empirical studies are conducted to corroborate our theoretical findings. Copyright © 2024, The Authors. All rights reserved.

关键词： Controlled drug delivery

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POS0883 PREDICTORS OF PATIENT-REPORTED TREATMENT SUCCESS IN PATIENTS WITH PSORIATIC ARTHRITIS

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Annals of the Rheumatic Diseases 2023年 82卷 748-749页

作者： C. Samuel A. Grace-Finney T. Grader-Beck U. Haque J. Miller S. Grieb L. Prichett A.M. Orbai The Johns Hopkins University School of Medicine Rheumatology Baltimore United States of America The Johns Hopkins University School of Medicine Biostatistics Epidemiology and Data Management (BEAD) Core Baltimore United States of America

Background Determinants of patient-reported treatment success in psoriatic arthritis (PsA) have not been defined. Objectives To determine predictors of patient-reported treatment success in PsA. Methods Rheumatologist-diagnosed PsA patients, who met the CASPAR classification, were recruited from a single center. PsA outcome measures included 68/66 joint counts, enthesitis, dactylitis, psoriasis body surface area (BSA), patient reported outcomes including PROMIS CAT. The study primary outcome was patient-reported treatment success: “Today, considering the level of control of your psoriatic arthritis and psoriasis, do you consider your treatment has been successful?” with response options Yes/No. Multivariate logistic regression analyses were performed to determine predictors of patient-reported treatment success in PsA. We hypothesized that disease activity measures, including arthritis, psoriasis, enthesitis, and dactylitis, symptoms, and treatment type would contribute to patient-reported treatment success. We did not include multiple patient-reported outcomes in a model due to collinearity. Results A total of 178 participants had a baseline visit. Mean (SD) CASPAR score was 3.7 (0.9) age 51.7 (13.5) years, BMI 31.3 (7.2), while 52.2% were women, and 86.0% Caucasian. Treatment success was reported by 116 patients (65%). Overall, 105 participants had complete data for all the variables included in the logistic regression models. The total joint swelling 66, total joint tenderness 68, PROMIS pain interference, and PROMIS fatigue were significantly negatively associated with treatment success while controlling for age, race, gender, and BMI. Each additional swollen joint was associated with a 23% decreased odds of treatment success, each tender joint was associated with 17% decreased odds of success, each additional point on the PROMIS fatigue and on the PROMIS pain interference was associated with 6% and 15% decreased odds of success, respectively (Table 1). Patients

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Distributed algorithms for u-statistics-based empirical risk minimization

The Journal of Machine Learning Research

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The Journal of Machine Learning Research 2023年第1期24卷 12301-12343页

作者： Lanjue Chen Alan T.K. Wan Shuyi Zhang Yong Zhou Key Laboratory of Advanced Theory and Application in Statistics and Data Science Ministry of Education Academy of Statistics and Interdisciplinary Sciences and School of Statistics East China Normal University Shanghai China Department of Management Sciences School of Data Science and Department of Biostatistics City University of Hong Kong Kowloon Hong Kong

Empirical risk minimization, where the underlying loss function depends on a pair of data points, covers a wide range of application areas in statistics including pairwise ranking and survival analysis. The common empirical risk estimator obtained by averaging values of a loss function over all possible pairs of observations is essentially a U-statistic. One well-known problem with minimizing U-statistic type empirical risks, is that the computational complexity of U-statistics increases quadratically with the sample size. When faced with big data, this poses computational challenges as the colossal number of observation pairs virtually prohibits centralized computing to be performed on a single machine. This paper addresses this problem by developing two computationally and statistically efficient methods based on the divide-and-conquer strategy on a decentralized computing system, whereby the data are distributed among machines to perform the tasks. One of these methods is based on a surrogate of the empirical risk, while the other method extends the one-step updating scheme in classical M-estimation to the case of pairwise loss. We show that the proposed estimators are as asymptotically efficient as the benchmark global U-estimator obtained under centralized computing. As well, we introduce two distributed iterative algorithms to facilitate the implementation of the proposed methods, and conduct extensive numerical experiments to demonstrate their merit.

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Submucosal Uterine Fibroids Are Associated With Bacterial Vaginosis

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Women's Reproductive Health 2024年第3期11卷 729-744页

作者： Cheng, Joyce M. Sims, Holly Singh, Bhuchitra Olson, Sarah Voegtline, Kristin Ensign, Laura M. Segars, James H. Johns Hopkins University School of Medicine Baltimore MD United States Division of Reproductive Sciences & Women’s Health Research Department of Gynecology and Obstetrics Johns Hopkins University School of Medicine Baltimore MD United States Biostatistics Epidemiology and Data Management Core Johns Hopkins School of Medicine Baltimore MD United States Center for Nanomedicine at the Wilmer Eye Institute Department of Ophthalmology Johns Hopkins University School of Medicine Baltimore MD United States

Multivariate logistic regression models were used to assess the adjusted relationship between uterine fibroids and BV. Of the 189 patients with uterine fibroids, individuals with BV were more likely to have a submucosal fibroid(s) as compared to those without BV (28.3% vs 10.5%, P = 0.003). The odds of a subsequent diagnosis of BV were higher among patients with submucosal fibroids as compared to those with fibroids in other locations (Odds Ratio: 3.0, 95% CI: 1.3–7.1). Patients with BV demonstrated higher likelihoods of abnormal uterine bleeding (65.5% vs 26.3%, P < 0.001) and anemia (52.2% vs 30.3%, P = 0.003). © 2024 Society for Menstrual Cycle Research.

关键词： abnormal uterine bleeding bacterial vaginosis leiomyoma Uterine fibroids vaginal microbiome

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Study Duration Prediction for Clinical Trials with Time-to-Event Endpoints Using Mixture Distributions Accounting for Heterogeneous Population

arXiv

引用

arXiv 2023年

作者： Zhang, Hong Pu, Jie Deng, Shibing Roychoudhury, Satrajit Chu, Haitao Robinson, Douglas Global Biometrics and Data Management Pfizer Inc. New YorkNY United States Division of Biostatistics School of Public Health University of Minnesota MinneapolisMN United States

In the era of precision medicine, more and more clinical trials are now driven or guided by biomarkers, which are patient characteristics objectively measured and evaluated as indicators of normal biological processes, pathogenic processes, or pharmacologic responses to therapeutic interventions. With the overarching objective to optimize and personalize disease management, biomarker-guided clinical trials increase the efficiency by appropriately utilizing prognostic or predictive biomarkers in the design. However, the efficiency gain is often not quantitatively compared to the traditional all-comers design, in which a faster enrollment rate is expected (e.g. due to no restriction to biomarker positive patients) potentially leading to a shorter duration. To accurately predict biomarker-guided trial duration, we propose a general framework using mixture distributions accounting for heterogeneous population. Extensive simulations are performed to evaluate the impact of heterogeneous population and the dynamics of biomarker characteristics and disease on the study duration. Several influential parameters including median survival time, enrollment rate, biomarker prevalence and effect size are identitied. Re-assessments of two publicly available trials are conducted to empirically validate the prediction accuracy and to demonstrate the practical utility. The R package detest is developed to implement the proposed method and is publicly available on CRAN. © 2023, CC BY.

关键词： Biomarkers

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