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In many time-to-event studies, the event of interest is recurrent. Here, the data for each sample unit correspond to a series of gap times between the subsequent events. Given a limited follow-up period, the last gap time might be right-censored. In contrast to classical analysis, gap times and censoring times cannot be assumed independent, i.e., the sequential nature of the data induces dependent censoring. Also, the number of recurrences typically varies among sample units leading to unbalanced data. To model the association pattern between gap times, so far only parametric margins combined with the restrictive class of Archimedean copulas have been considered. Here, taking the specific data features into account, we extend existing work in several directions: we allow for nonparametric margins and consider the flexible class of D-vine copulas. A global and sequential (one- and two-stage) likelihood approach are suggested. We discuss the computational efficiency of each estimation strategy. Extensive simulations show good finite sample performance of the proposed methodology. It is used to analyze the association of recurrent asthma attacks in children. The analysis reveals that a D-vine copula detects relevant insights, on how dependence changes in strength and type over time.

Clustered time-to-event data are quite common in survival analysis and finding a suitable model to account for dispersion as well as censoring is an important issue. In this article, we present a flexible model for repeated, overdispersed time-to-event data with right-censoring. We present here a general model by incorporating generalized gamma and normal random effects in a Weibull distribution to accommodate overdispersion and data hierarchies, respectively. The normal random effect has the property of being symmetrical, which means its probability density function is symmetric around its mean. While the random effects are symmetrically distributed, the resulting frailty model is asymmetric in its survival function because the random effects enter the model multiplicatively via the hazard function, and the exponentiation of a symmetric normal variable leads to lognormal distribution, which is right-skewed. Due to the intractable integrals involved in the likelihood function and its derivatives, the Monte Carlo approach is used to approximate the involved integrals. The maximum likelihood estimates of the parameters in the model are then numerically determined. An extensive simulation study is then conducted to evaluate the performance of the proposed model and the method of inference developed here. Finally, the usefulness of the model is demonstrated by analyzing a data on recurrent asthma attacks in children and a recurrent bladder data set known in the survival analysis literature.

In the experiment reported here, approximately 1,000 words were presented to 75 participants in a go/no-go lexical decision task while event-related potentials (ERPs) were recorded. Partial correlations were computed for variables selected to reflect orthographic, lexical, and semantic processing, as well as for a novel measure of the visual complexity of written words. Correlations were based on the item-level ERPs at each electrode site and time slice while a false-discovery-rate correction was applied. Early effects of visual complexity were seen around 50 ms after word onset, followed by the earliest sustained orthographic effects around 100 to 150 ms, with the bulk of orthographic and lexical influences arising after 200 ms. Effects of a semantic variable (concreteness) emerged later, at around 300 ms. The overall time course of these ERP effects is in line with hierarchical, cascaded, interactive accounts of word recognition, in which fast feed-forward influences are consolidated by top-down feedback via recurrent processing loops.

The prediction and prevention of influenza is a public health issue of great concern, and the study of timely acquisition of influenza transmission trend has become an important research topic. For achieving more quicker and accurate detection and prediction, the data recorded on the Internet, especially on the search engine from Google or Baidu are widely introduced into this field. Moreover, with the development of intelligent technology and machine learning algorithm, many updated and advanced trend tracking and forecasting methods are also being used in this research problem. In this paper, a new recurrent neural network architecture, attention-based long short-term memory model is proposed for influenza surveillance. This is a kind of deep learning model which is trained by processing from Baidu Index series so as to fit the real influenza survey time series. Previous studies on influenza surveillance by Baidu Index mostly used traditional autoregressive moving average model or classical machine learning models such as logarithmic linear regression, support vector regression or multi-layer perception model to fit influenza like illness data, which less considered the deep learning structure. Meanwhile, some new model that considered the deep learning structure did not take into account the application of Baidu index data. This study considers introducing the recurrent neural network with long short-term memory combined with attention mechanism into the influenza surveillance research model, which not only fits the research problems well in model structure, but also provides research methods based on Baidu index. The actual survey data and Baidu Index data are used to train and test the proposed attention-based long short-term memory model and the other comparison models, so as to iterate the value of the model parameters, and to describe and predict the influenza epidemic situation. The experimental results show that our proposed model has better performance in the mean absolute error, mean absolute percentage error, index of agreement and other indicators than the other comparison models. Our proposed attention-based long short-term memory model vividly verifies the ability of this attention-based long short-term memory structure for better surveillance and prediction the trend of influenza. In comparison with some of the latest models and methods in this research field, the model we proposed is also excellent in effect, even more lightweight and robust. Future research direction can consider fusing multimodal data based on this model and developing more application scenarios.

In the analysis of recurrent event data, some covariates have time-varying effect such as efficacy of certain treatments, while others are internally time-dependent, like blood pressure. Considering the variability of the covariate effects and covariate observation over time, a time-varying coefficient rate model with intermittently observed covariates was proposed. Generally, time-dependent covariates cannot be continuously observed. They are only recorded intermittently. The unobserved time-dependent covariates need to be imputed. Estimators were obtained using kernel likelihood for the time-varying coefficient and kernel smoothing for the time-dependent covariate. The proposed estimator was proved to be asymptotically unbiased and normally distributed. Some simulations were conducted to evaluate the estimation method, and the proposed method was applied to analyze a real data.

Understanding how organisms distribute themselves in response to interacting species, ecosystems, climate, human development and time is fundamental to ecological study and practice. A measure to quantify the relationship among organisms and their environments is intensity of use: the rate of use of a specific resource in a defined unit of time. Estimating the intensity of use differs from estimating probabilities of occupancy or selection, which can remain constant even when the intensity of use varies. We describe a method to evaluate the intensity of use across conditions that vary in both space and time. We demonstrate its application on a large mammal community where linear developments and human activity are conjectured to influence the interactions between white‐tailed deer (Odocoileus virginianus) and wolves (Canis lupus) with possible consequences on threatened woodland caribou (Rangifer tarandus caribou). We collect and quantify intensity of use data for multiple, interacting species with the goal of assessing management efficacy, including a habitat restoration strategy for linear developments. We test whether blocking linear developments by spreading logs across a 200‐m interval can be applied as an immediate mitigation to reduce the intensities of use by humans, predator and prey species in a boreal caribou range. We deployed camera traps on linear developments with and without restoration treatments in a landscape exposed to both timber and oil development. We collected a three‐year dataset and employed spatial recurrent event models to analyse intensity of use by an interacting human and large mammal community across a range of environmental and climatic conditions. Spatial recurrent event models revealed that intensity of use by humans influenced the intensity of use by all five large mammal species evaluated, and the intensities of use by wolves and deer were inextricably linked in space and time. Conditions that resist travel on linear developments had a strong negative effect on the intensity of human and large mammal use. Mitigation strategies that resist, or redirect, animal travel on linear developments can reduce the effects of resource development on interacting human and predator–prey interactions. Our approach is easily applied to other continuous time point‐based survey methodologies and shows that measuring the intensity of use within animal communities can help scientists monitor, mitigate and understand ecological states and processes. The authors' study reveals how to estimate intensity functions using camera trap data. Intensity functions tell us how frequently a resource unit is used per unit time. Estimating intensity of use can help scientists understand spatial and temporal interactions among predators, prey, and their habitats.

We propose Multi-scale convolutional recurrent neural networks (MCRNN) and data augmentation methods to detect polyphonic sound event with few training data. MCRNN consists of Multi-scale convolutional neural networks (MCNN) and recurrent neural networks (RNN). MCNN concatenates the higher level features extracted using multiple convolution kernels with different scales from the time domain and frequency domain at the same time. RNN is able to capture the longer term temporal context characteristics. A novel background spectrum random replacement (BSRR) data augmentation method is applied to expand training data, which uses standard normal distribution data with randomly selected position and length instead of the original time-domain, frequency-domain or time-frequency domain background spectrum features. Our method is tested on the datasets of DCASE 2019 Task3 (T3). The experimental results showed that the MCRNN and BSRR data augmentation method are efficient. We achieved better results than the first place and the single advanced on the T3 by applying BSRR and SpecAugment data augmentation method simultaneously. On the evaluation dataset (T3-eval), our best result shows 0.05 and 0.975 of error rate (ER) and F1 respectively. Our method got the best performance and relatively improved 17% and 1% than the corresponding values of the single advanced.

The use of wireless location technology and mobile phone data appears to offer a broad range of new opportunities for sophisticated applications in traffic management and monitoring, particularly in the field of incident management. Indeed, due to the high market penetration of mobile phones, it allows the use of very detailed spatial data at lower costs than traditional data collection techniques. Albeit recent, the literature in the field is wide-ranging, although not adequately structured. The aim of this paper is to provide a systematic overview of the main studies and projects addressing the use of data derived from mobile phone networks to obtain location and traffic estimations of individuals, as a starting point for further research on incident and traffic management. The advantages and limitations of the process of retrieving location information and transportation parameters from cellular phones are also highlighted. The issues are presented by providing a description of the current background and data types retrievable from the GSM network. In addition to a literature review, the main findings on the so-called Current City project are presented. This is a test system in Amsterdam (The Netherlands) for the extraction of mobile phone data and for the analysis of the spatial network activity patterns. The main purpose of this project is to provide a full picture of the mobility and area consequences of an incident in near real time to create situation awareness. The first results from this project on how telecom data can be utilized for understanding individual presence and mobility in regular situations and during nonrecurrent events where regular flows of people are disrupted by an incident are presented. Furthermore, various interesting studies and projects carried out so far in the field are analyzed, leading to the identification of important research issues related to the use of mobile phone data in transportation applications. Relevant issues concern, on the one hand, factors that influence accuracy, reliability, data quality and techniques used for validation, and on the other hand, the specific role of private mobile companies and transportation agencies.

Background Survivors of myocardial infarction (MI) or ischemic stroke (IS) are at high risk for subsequent cardiovascular events. Hypothesis Older patients with prior MI or IS are at risk for recurrent cardiovascular events, and comorbidities such as diabetes may increase this risk. Methods Two cohorts were studied in a retrospective Medicare 20% random sample—a 2008 cohort with up to 6 years of follow‐up (MI, N = 26 460; IS, N = 17 566) and a 2012 cohort with 1 year of follow‐up (MI, N = 26 548; IS, N = 17 728). Results In older patients who survived an event of MI or IS (2012 cohort), 7.2% had a recurrent MI and 6.7% had a recurrent IS in the first year; 32% died. Accounting for multiple recurrent events (2012 cohort), the event rates per 100 patient‐years were 11.6 and 10.2 for the MI and IS cohorts, respectively. Cumulative incidence of recurrence (2008 cohort) increased from 7.7% at 1 year to 14.3% at 6 years for recurrent MI and from 6.7% at 1 year to 13.4% at 6 years for recurrent IS. Comorbid diabetes (2012 cohort) was significantly associated (adjusted risk ratio) with MI recurrence (1.44) and risk of coronary revascularization (1.23) in the MI cohort and with IS recurrence (1.26) in the IS cohort. Conclusion In this older population with prior MI or IS, high rates of recurrent cardiovascular events and multiple recurrent events were observed. These findings highlight the need for aggressive intervention for secondary prevention and management of comorbidities in high‐risk patients, particularly those with diabetes.

Objectives This prospective cohort study investigates work-related risk factors for occupational back injury among healthcare workers. Methods The study comprised 5017 female healthcare workers in eldercare from 36 municipalities in Denmark who responded to a baseline and follow-up questionnaire in 2005 and 2006, respectively. Using logistic regression, the odds for occupational back injury (ie, sudden onset episodes) in 2006 from patient transfers in 2005 was modeled. Results In the total study population, 3.9% experienced back injury during follow-up, of which 0.5% were recurrent events. When adjusting for lifestyle (body mass index, leisure-time physical activity, smoking), workrelated characteristics (seniority and perceived influence at work), and history of back pain and injury, daily patient transfers increased the risk for back injury (trend, P=0.03): odds ratio (OR) 1.75 [95% confidence interval (95% CI) 1.05-2.93] for 1-2 transfers per day, OR 1.81 (95% CI 1.14-2.85) for 3-10 transfers per day, and OR 1.56 (95% CI 0.96-2.54) for < 10 transfers per day, referencing those with > 1 patient transfer on average per day. The population attributable fraction of daily patient transfer fo r back injury was estimated to be 36%. Among those with daily patient transfer (N=3820), using an assistive device decreased the risk for back injury for \"often\" and \"very often\" use [OR 0.59 (95% CI 0.36-0.98) and OR 0.62 (95% CI 0.38-1.00), respectively] referencing those who \"seldom\" use assistive devices. Conclusion Daily patient transfer was associated with increased risk for back injury among healthcare workers. Persistent use of an assistive device was associated with reduced risk for back injury among healthcare workers with daily patient transfers.