Models for surveillance data under reporting delay: applications to US veteran first-time suicide attempters

Surveillance data provide a vital source of information for assessing the spread of a health problem or disease of interest and for planning for future health-care needs. However, the use of surveillance data requires proper adjustments of the reported caseload due to underreporting caused by reporting delays within a limited observation period. Although methods are available to address this classic statistical problem, they are largely focused on inference for the reporting delay distribution, with inference about caseload of disease incidence based on estimates for the delay distribution. This approach limits the complexity of models for disease incidence to provide reliable estimates and projections of incidence. Also, many of the available methods lack robustness since they require parametric distribution assumptions. We propose a new approach to overcome such limitations by allowing for separate models for the incidence and the reporting delay in a distribution-free fashion, but with joint inference for both modeling components, based on functional response models. In addition, we discuss inference about projections of future disease incidence to help identify significant shifts in temporal trends modeled based on the observed data. This latter issue on detecting ‘change points’ is not sufficiently addressed in the literature, despite the fact that such warning signs of potential outbreak are critically important for prevention purposes. We illustrate the approach with both simulated and real data, with the latter involving data for suicide attempts from the Veteran Healthcare Administration.     

Bayesian dynamic network actor models with application to South Korean COVID-19 patient movement data

Statistical Methods & Applications - Motivated by the ongoing COVID-19 pandemic, this article introduces Bayesian dynamic network actor models for the analysis of infected individuals’...     

城市医生资源与新冠肺炎防控效率

本文利用非参数估计法考察了我国城市层面的新冠肺炎病情防控效率,讨论了医生资源对防控效率的影响。结果表明,城市疫情防控措施显著降低了每日新增病例,但存在明显地区异质性,城市医生资源差异是解释防控效率异质性的重要因素。医生资源对防控效率的影响具有非线性特征,在资源紧缺城市边际促进作用更大。包括病床和医院数量在内的医疗物质投入没有产生边际影响,表明医疗物质资源还未达到约束状态。进一步分析医生职业结构发现,临床医生的作用最为显著。地区应急响应启动越早,医生资源的作用越大,因此政府相关政策是医生资源作用的有力保障。新冠肺炎疫情一定程度上暴露了我国医生资源短板,需要多措并举有效提升城市医生资源供给。     

Time series forecasting with neural networks: a comparative study using the air line data

This case-study fits a variety of neural network (NN) models to the well-known air line data and compares the resulting forecasts with those obtained from the Box–Jenkins and Holt–Winters methods. Many potential problems in fitting NN models were revealed such as the possibility that the fitting routine may not converge or may converge to a local minimum. Moreover it was found that an NN model which fits well may give poor out-of-sample forecasts. Thus we think it is unwise to apply NN models blindly in 'black box' mode as has sometimes been suggested. Rather, the wise analyst needs to use traditional modelling skills to select a good NN model, e.g. to select appropriate lagged variables as the 'inputs'. The Bayesian information criterion is preferred to Akaike's information criterion for comparing different models. Methods of examining the response surface implied by an NN model are examined and compared with the results of alternative nonparametric procedures using generalized additive models and projection pursuit regression. The latter imposes less structure on the model and is arguably easier to understand.     

Estimation of final standings in football competitions with a premature ending: the case of COVID-19

AStA Advances in Statistical Analysis - We study an alternative approach to determine the final league table in football competitions with a premature ending. For several countries, a premature...     

Asymmetric autoregressive models: statistical aspects and a financial application under COVID-19 pandemic

In the present study, we provide a motivating example with a financial application under COVID-19 pandemic to investigate autoregressive (AR) modeling and its diagnostics based on asymmetric distributions. The objectives of this work are: (i) to formulate asymmetric AR models and their estimation and diagnostics; (ii) to assess the performance of the parameters estimators and of the local influence technique for these models; and (iii) to provide a tool to show how data following an asymmetric distribution under an AR structure should be analyzed. We take the advantages of the stochastic representation of the skew-normal distribution to estimate the parameters of the corresponding AR model efficiently with the expectation-maximization algorithm. Diagnostic analytics are conducted by using the local influence technique with four perturbation schemes. By employing Monte Carlo simulations, we evaluate the statistical behavior of the corresponding estimators and of the local influence technique. An illustration with financial data updated until 2020, analyzed using the methodology introduced in the present work, is presented as an example of effective applications, from where it is possible to explain atypical cases from the COVID-19 pandemic.     

An improved meta-analysis for analyzing cylindrical-type time series data with applications to forecasting problem in environmental study

In this paper, we propose an improved generalized least square (GLS) meta-analysis in a linear-circular regression, and show its utility in the analysis of a certain environmental issue. The existing GLS meta-analysis proposed in Becker and Wu has a serious flaw since information about the covariance among coefficients across studies is not utilized. In our proposed meta-analysis, we take the correlations between adjacent studies into account, and improve the existing GLS meta-analysis. We provide numerical examples to compare the proposed method with several other existing methods by using Akaike's Information Criterion, Bayesian Information Criterion and mean square prediction errors with applications to forecasting problem in Environmental study.     

Online monitoring of high-dimensional binary data streams with application to extreme weather surveillance

With the rapid development of modern sensor technology, high-dimensional data streams appear frequently nowadays, bringing urgent needs for effective statistical process control (SPC) tools. In such a context, the online monitoring problem of high-dimensional and correlated binary data streams is becoming very important. Conventional SPC methods for monitoring multivariate binary processes may fail when facing high-dimensional applications due to high computational complexity and the lack of efficiency. In this paper, motivated by an application in extreme weather surveillance, we propose a novel pairwise approach that considers the most informative pairwise correlation between any two data streams. The information is then integrated into an exponential weighted moving average (EWMA) charting scheme to monitor abnormal mean changes in high-dimensional binary data streams. Extensive simulation study together with a real-data analysis demonstrates the efficiency and applicability of the proposed control chart.     

A statistical signalling model for use in surveillance of adverse drug reaction data

SUMMARY This paper presents a statistically superior lag-adjusted model for detecting increased frequency of reports of adverse drug event (ADE) rates. The effect of a significant lag time between ADE occurrence and report dates is studied. The approach in this paper to analyzing ADE data of this nature involves proposing a statistical model that utilizes a lag density function. The statistical method proposed was the development of an 'exact' procedure to monitor drugs that have a low incidence of ADEs. The approach determines statistically whether a change in the frequency of a specific ADE exists between two predetermined time intervals. There exist immense public health implications associated with the early detection of serious ADEs. The reduced risk of unfavorable outcomes associated with medication therapy is the goal of all involved. Simulated illustrations and discussion are provided, along with a detailed FORTRAN program used to implement the newly suggested lag-adjusted procedure.     

Modeling and forecasting realized covariance matrices with accounting for leverage

The existing dynamic models for realized covariance matrices do not account for an asymmetry with respect to price directions. We modify the recently proposed conditional autoregressive Wishart (CAW) model to allow for the leverage effect. In the conditional threshold autoregressive Wishart (CTAW) model and its variations the parameters governing each asset's volatility and covolatility dynamics are subject to switches that depend on signs of previous asset returns or previous market returns. We evaluate the predictive ability of the CTAW model and its restricted and extended specifications from both statistical and economic points of view. We find strong evidence that many CTAW specifications have a better in-sample fit and tend to have a better out-of-sample predictive ability than the original CAW model and its modifications.     

The promise and pitfalls of composite endpoints in sepsis and COVID-19 clinical trials

Composite endpoints reveal the tendency for statistical convention to arise locally within subfields. Composites are familiar in cardiovascular trials, yet almost unknown in sepsis. However, the VITAMINS trial in patients with septic shock adopted a composite of mortality and vasopressor-free days, and an ordinal scale describing patient status rapidly became standard in COVID studies. Aware that recent use could incite interest in such endpoints, we are motivated to flag their potential value and pitfalls for sepsis research and COVID studies.     

Missing data and forecasting in multivariate time series: An application of the common components dynamic linear model

Summary The paper deals with missing data and forecasting problems in multivariate time series making use of the Common Components Dynamic Linear Model (DLMCC), presented in Quintana (1985), and West and Harrison (1989). Some results are presented and discussed: exploiting the correlation between series, estimated by the DLMCC, the paper shows as it is possible to update state vector posterior distributions for the unobserved series. This is realized on the base of the updating of the observed series state vectors, for which the usual Kalman filter equations can be applied. An application concerning some Italian private consumption series provides an example of the model capabilities.     

Remote Work in Library Technical Services: Connecting Historical Perspectives to Realities of the Developing COVID-19 Pandemic

Abstract

The concept and practice of remote work in library technical services is not new, but the scale and speed of the transition to remote work for many libraries due to the COVID-19 pandemic is unprecedented. This column provides an overview of pre-pandemic literature on remote work in library technical services and briefly examines the history, planning, case studies, technology and equity concerns, challenges, and potential benefits of remote work. Initial connections are drawn between existing literature and the impact of the pandemic on remote work, and future directions for research and discussion are offered.     

Time-series forecasting with a novel fuzzy time-series approach: an example for Istanbul stock market

Artificial intelligence procedures such as artificial neural networks (ANNs), genetic algorithms and particle swarm optimization and other procedures such as fuzzy clustering have been successfully used in the various stages of different fuzzy time-series forecasting approaches. Fuzzy clustering, genetic algorithm and particle swarm optimization are generally used in the fuzzification stage, and this simplifies the applicability of this stage and makes the fuzzy time-series approach more systematic. ANNs have also been applied successfully in the fuzzy relationship determination stage. In this study, we propose a new hybrid fuzzy time-series approach in which fuzzy c-means clustering procedure is employed in the fuzzification stage and feed-forward neural networks are used in the fuzzy relationship determination stage. This study also includes an empirical analysis pertaining to the forecasting of Index 100 for the stocks and bonds exchange market of Istanbul.     

Variance component models for longitudinal count data with baseline information: epilepsy data revisited

Random effect models have often been used in longitudinal data analysis since they allow for association among repeated measurements due to unobserved heterogeneity. Various approaches have been proposed to extend mixed models for repeated count data to include dependence on baseline counts. Dependence between baseline counts and individual-specific random effects result in a complex form of the (conditional) likelihood. An approximate solution can be achieved ignoring this dependence, but this approach could result in biased parameter estimates and in wrong inferences. We propose a computationally feasible approach to overcome this problem, leaving the random effect distribution unspecified. In this context, we show how the EM algorithm for nonparametric maximum likelihood (NPML) can be extended to deal with dependence of repeated measures on baseline counts.     

Kalman filter-based modelling and forecasting of stochastic volatility with threshold

We propose a parametric nonlinear time-series model, namely the Autoregressive-Stochastic volatility with threshold (AR-SVT) model with mean equation for forecasting level and volatility. Methodology for estimation of parameters of this model is developed by first obtaining recursive Kalman filter time-update equation and then employing the unrestricted quasi-maximum likelihood method. Furthermore, optimal one-step and two-step-ahead out-of-sample forecasts formulae along with forecast error variances are derived analytically by recursive use of conditional expectation and variance. As an illustration, volatile all-India monthly spices export during the period January 2006 to January 2012 is considered. Entire data analysis is carried out using EViews and matrix laboratory (MATLAB) software packages. The AR-SVT model is fitted and interval forecasts for 10 hold-out data points are obtained. Superiority of this model for describing and forecasting over other competing models for volatility, namely AR-Generalized autoregressive conditional heteroscedastic, AR-Exponential GARCH, AR-Threshold GARCH, and AR-Stochastic volatility models is shown for the data under consideration. Finally, for the AR-SVT model, optimal out-of-sample forecasts along with forecasts of one-step-ahead variances are obtained.     

Dynamic structural models with covariates for short-term forecasting of time series with complex seasonal patterns

This work presents a framework of dynamic structural models with covariates for short-term forecasting of time series with complex seasonal patterns. The framework is based on the multiple sources of randomness formulation. A noise model is formulated to allow the incorporation of randomness into the seasonal component and to propagate this same randomness in the coefficients of the variant trigonometric terms over time. A unique, recursive and systematic computational procedure based on the maximum likelihood estimation under the hypothesis of Gaussian errors is introduced. The referred procedure combines the Kalman filter with recursive adjustment of the covariance matrices and the selection method of harmonics number in the trigonometric terms. A key feature of this method is that it allows estimating not only the states of the system but also allows obtaining the standard errors of the estimated parameters and the prediction intervals. In addition, this work also presents a non-parametric bootstrap approach to improve the forecasting method based on Kalman filter recursions. The proposed framework is empirically explored with two real time series.     

基于vSEIdRm模型的人口迁移以及离汉交通管控对新冠肺炎疫情发展的影响分析

不同于传统( Susceptible-Exposed-Infected-Removed)SEIR流行病传播动力学模型,本文在近期研究的Varying Coefficient Susceptible-Exposed-Infected-Diagnosed-Removed (vSEIdR)模型基础上加上人口迁徙(Migration) 模块,设计开发了vSEIdRm模型,该模型考虑了跨区域人口迁徙对疫情传播的影响,并允许流行病传播参数随时间变化。本文首先对人口迁移数据进行统计分析,建立其与各省新冠肺炎疫情发展的联系。之后,基于vSEIdRm模型估计了疫情初期各省份来自武汉的输入病例数,并定量刻画了离汉交通管控的效果。研究结果显示,离汉交通管控措施有效地减少了各省份的疫情规模。