Predicting infectious disease outbreak risk via migratory waterfowl vectors

The spread of an emerging infectious disease is a major public health threat. Given the uncertainties associated with vector-borne diseases, in terms of vector dynamics and disease transmission, it is critical to develop statistical models that address how and when such an infectious disease could spread throughout a region such as the USA. This paper considers a spatio-temporal statistical model for how an infectious disease could be carried into the USA by migratory waterfowl vectors during their seasonal migration and, ultimately, the risk of transmission of such a disease to domestic fowl. Modeling spatio-temporal data of this type is inherently difficult given the uncertainty associated with observations, complexity of the dynamics, high dimensionality of the underlying process, and the presence of excessive zeros. In particular, the spatio-temporal dynamics of the waterfowl migration are developed by way of a two-tiered functional temporal and spatial dimension reduction procedure that captures spatial and seasonal trends, as well as regional dynamics. Furthermore, the model relates the migration to a population of poultry farms that are known to be susceptible to such diseases, and is one of the possible avenues toward transmission to domestic poultry and humans. The result is a predictive distribution of those counties containing poultry farms that are at the greatest risk of having the infectious disease infiltrate their flocks assuming that the migratory population was infected. The model naturally fits into the hierarchical Bayesian framework.     

A Spatio-Temporal Model for Functional Magnetic Resonance Imaging Data – with a View to Resting State Networks

Abstract.  Functional magnetic resonance imaging (fMRI) is a technique for studying the active human brain. During the fMRI experiment, a sequence of MR images is obtained, where the brain is represented as a set of voxels. The data obtained are a realization of a complex spatio-temporal process with many sources of variation, both biological and technical. We present a spatio-temporal point process model approach for fMRI data where the temporal and spatial activation are modelled simultaneously. It is possible to analyse other characteristics of the data than just the locations of active brain regions, such as the interaction between the active regions. We discuss both classical statistical inference and Bayesian inference in the model. We analyse simulated data without repeated stimuli both for location of the activated regions and for interactions between the activated regions. An example of analysis of fMRI data, using this approach, is presented.     

精准扶贫背景下贫困山区旅游扶贫效率研究——以安徽皖南山区18个县(区、市)为例

皖南山区旅游扶贫效率接近中等水平,区域差异大但呈波状缩小趋势。现阶段,旅游扶贫的主要支撑为规模效率,快速的旅游发展、不断扩大的旅游业规模是提升旅游扶贫效率的有效途径。技术进步是导致全要素生产率指数波动的主要因素,旅游体制机制、旅游模式创新是进一步提高其扶贫效率的核心路径。旅游扶贫效率由"局部突出,环线梯度"的点状格局向"四周连片,中部塌陷"的漏斗状格局演化,旅游产业出现外溢。为提升区域旅游扶贫效率,应根据潜力期、朝阳期、黄金期、夕阳期不同的效率形态类型,实施精准的旅游扶贫模式。     

A spatio-temporal model for assessing winter damage risk to east coast vineyards

Climate is an essential component in site suitability for agriculture in general, and specifically in viticulture. With the recent increase in vineyards on the East Coast, an important climactic consideration in site suitability is extreme winter temperature. Often, maps of annual minimum temperatures are used to determine cold hardiness. However, cold hardiness of grapes is a more complicated process, since the temperature that grapes are able to withstand without damage is not constant. Rather, recent temperature cause acclimation or deacclimation and hence, have a large influence on cold hardiness. By combining National Oceanic and Atmospheric Administration (NOAA) weather station data and leveraging recently created cold hardiness models for grapes, we develop a dynamic spatio-temporal model to determine the risk of winter damage due to extreme cold for several grape varieties commonly grown in the eastern United States. This analysis provides maps of winter damage risk to three grape varieties, Chardonnay, Cabernet Sauvignon, and Concord.     

论“南孔文化”对南宋书院的影响

"南孔文化"传承了洙泗遗风,对南宋书院的发展产生了重大影响。这种影响既有外在的,也有内在的。南宗族人在教育方面的作为对书院的影响是外在的,衢州孔氏家庙所蕴含的文化内涵对书院的影响是内在的。"南孔文化"影响了江南世人的心理结构与思维方式,使南宋学者在思想上逼近儒学原点,并使南宋出现重教兴学之风,从而推动了南宋书院的发展。南宋书院的时空分布也受到了"南孔文化"的影响,在空间上主要分布在以衢州为中心的江南地区,在时间上则先后形成了孝宗朝和理宗朝的两次发展高峰。     

A Conditional Autoregressive Gaussian Process for Irregularly Spaced Multivariate Data with Application to Modelling Large Sets of Binary Data

A Gaussian conditional autoregressive (CAR) formulation is presented that permits the modelling of the spatial dependence and the dependence between multivariate random variables at irregularly spaced sites so capturing some of the modelling advantages of the geostatistical approach. The model benefits not only from the explicit availability of the full conditionals but also from the computational simplicity of the precision matrix determinant calculation using a closed form expression involving the eigenvalues of a precision matrix submatrix. The introduction of covariates into the model adds little computational complexity to the analysis and thus the method can be straightforwardly extended to regression models. The model, because of its computational simplicity, is well suited to application involving the fully Bayesian analysis of large data sets involving multivariate measurements with a spatial ordering. An extension to spatio-temporal data is also considered. Here, we demonstrate use of the model in the analysis of bivariate binary data where the observed data is modelled as the sign of the hidden CAR process. A case study involving over 450 irregularly spaced sites and the presence or absence of each of two species of rain forest trees at each site is presented; Markov chain Monte Carlo (MCMC) methods are implemented to obtain posterior distributions of all unknowns. The MCMC method works well with simulated data and the tree biodiversity data set.     

Simulation-based inference for simultaneous processes on regular lattices

The article proposes a simulation-based inferential method for simultaneous processes defined on a regular lattice. The focus is on spatio-temporal processes with a simultaneous component, that is such that contemporaneous spatial neighbors are potential explanatory variables in the model. The new method has the advantage of being simpler to implement than maximum likelihood and allows us to propose a robust estimator. We give asymptotic properties, present a Monte Carlo study and an illustrative example.     

Estimating likelihoods for spatio-temporal models using importance sampling

This paper describes how importance sampling can be applied to estimate likelihoods for spatio-temporal stochastic models of epidemics in plant populations, where observations consist of the set of diseased individuals at two or more distinct times. Likelihood computation is problematic because of the inherent lack of independence of the status of individuals in the population whenever disease transmission is distance-dependent. The methods of this paper overcome this by partitioning the population into a number of sectors and then attempting to take account of this dependence within each sector, while neglecting that between-sectors. Application to both simulated and real epidemic data sets show that the techniques perform well in comparison with existing approaches. Moreover, the results confirm the validity of likelihood estimates obtained elsewhere using Markov chain Monte Carlo methods.     

Bayesian P-splines and advanced computing in R for a changepoint analysis on spatio-temporal point processes

ABSTRACT

This work presents advanced computational aspects of a new method for changepoint detection on spatio-temporal point process data. We summarize the methodology, based on building a Bayesian hierarchical model for the data and declaring prior conjectures on the number and positions of the changepoints, and show how to take decisions regarding the acceptance of potential changepoints. The focus of this work is about choosing an approach that detects the correct changepoint and delivers smooth reliable estimates in a feasible computational time; we propose Bayesian P-splines as a suitable tool for managing spatial variation, both under a computational and a model fitting performance perspective. The main computational challenges are outlined and a solution involving parallel computing in R is proposed and tested on a simulation study. An application is also presented on a data set of seismic events in Italy over the last 20 years.     

用时空能量图和小波变换方法表征和识别步态

该文提出一种基于时空能量图和小波变换的步态特征表达和步态识别方法,步态特征由时空能量图经过小波变换后来表示,不必进行严格的时间配准或步态周期的定位。时空能量图将一个序列的多帧图像用一幅灰度图替代,集成了人的运动信息中时间与空间变化的特点,减少了特征的数据量,经证明,时空能量图对噪声不敏感。实验结果表明该算法具有较好的识别性能和较低的空间需求和计算量。