Spatiotemporal modeling of relative risk of dengue disease in Colombia

Spatiotemporal modeling of relative risk of dengue disease provides useful risk maps for surveillance and forecasting. The objective of the study was to generate smoothed estimates of relative risk applying hierarchical Bayesian spatiotemporal models, including covariates derived from satellite images containing land surface temperature (LST) and normalized difference vegetation index, for the period January 2009–December 2015, in a medium–sized Colombian city. Our models are based on the spatiotemporal interaction modeling framework of relative risk, where the interaction effects are unstructured, temporal, spatial or inseparable, at a small spatial and temporal scales. We fitted the models using Markov chain MonteCarlo Simulations, selecting the best model using leave-one-out cross-validation and widely applicable information criteria. Our best model was the inseparable spatiotemporal interaction-effects plus LST with constant coefficient model. We found a weak, positive association between LST and cases of dengue. We discussed the strengths and weaknesses of our spatiotemporal models given the spatial and temporal resolution selected in the study.     

A novel geostatistical approach combining Euclidean and gradual-flow covariance models to estimate fecal coliform along the Haw and Deep rivers in North Carolina

Incorporating flow in the covariance function is important for geostatistical water quality estimation that accounts for hydrological transport. Very few studies have successfully incorporated flow due to various reasons including implementation difficulties. To address this critical issue, we introduce here the first implementation of a flow weighted covariance model that uses gradual flow, and we use this model in a novel hybrid Euclidean/Gradual-flow covariance model to estimate fecal coliform along the Haw and Deep rivers from 2006 to 2010. The hybrid Euclidean/Gradual-flow model results in a 12.4% reduction in estimation mean square error compared to the Euclidean model, indicating that this is the first study to successfully incorporate gradual flow and demonstrate an improvement in estimation accuracy over the purely Euclidean approach. Furthermore, results show that the Euclidean/Gradual-flow model is more accurate and easier to implement than the Euclidean/Pipe-flow model. Our assessment found that 96 river miles were detected as being impaired according to the Euclidean/Gradual-flow method, which is more than twice the 39 river miles found according to the Euclidean estimate. These results demonstrate that the Euclidean/Gradual-flow model substantially increase the sensitivity in detecting fecal impairment, which provide critical new information for watershed management and public health protection measures.     

Elucidating transmission parameters of African swine fever through wild boar carcasses by combining spatio-temporal notification data and agent-based modelling

Mechanistic epidemiological modelling has a role in predicting the spatial and temporal spread of emerging disease outbreaks and purposeful application of control treatment in animal populations. Especially in the case of infectious diseases newly emerging in an ecological habitat, lack of knowledge may hamper direct parameterisation of model algorithms. Along with experimental studies observational data is usually based on case notifications. These data are widely acknowledged as having “biological precision” due to e.g. convenient sampling procedures, host or human activity patterns or diagnostic limitations under field conditions. Nevertheless, the data comprises the complex spatio-temporal distribution patterns of the infection. In the literature, this data value is non-systematically used to inform model development although the need for and value of the data is well recognised. Here we address the newly emerging epidemic of African swine fever spreading in Eurasian wild boar using an existing spatio-temporally explicit individual-based model of wild boar. The disease etiology required the implementation of a sub-model regarding transmission by carcasses left after infected individuals have died. However, the experimental evidence about the mechanism involved in carcass-mediated spread of the infection still has to be established. We propose a mechanistic quantitative procedure to optimise calibration of several uncertain parameters based on the spatio-temporal model output from the simulation environment and the spatio-temporal case data of infectious disease notifications. The best agreement with the spatio-temporal spreading pattern was achieved by parameterisation that suggests ubiquitous accessibility to carcasses but with marginal chance of being contacted by conspecifics e.g., avoidance behaviour. The parameter estimation procedure is fully general and applicable to problems where spatio-temporal explicit data recording and spatial-explicit dynamic modelling was performed.     

Use of the Beta-Dagum and Beta-Singh-Maddala distributions for modeling hydrologic data

Starting from a recent paper by Murshed (Stoch Environ Res Risk Assess 25:897–911, 2011) in which a good performance of the Beta-k distribution in analyzing extreme hydrologic events is shown, in this paper, we propose the use of two new four-parameters distribution functions strongly related to the Beta-k distribution, namely the Beta-Dagum and the Beta-Singh-Maddala distributions. More in detail, the new distributions are a generalization of a reparametrization of Beta-k and Beta-p distributions, respectively. For these distributions some particular interpretations in terms of maximum and minimum of sequences of random variables can be derived and the maximal and minimal domain of attraction can be obtained. Moreover, the method of maximum likelihood, the method of moments and the method of L-moments are examined to estimate the parameters. Finally, two different applications on real data regarding maxima and minima of river flows are reported, in order to show the potentiality of these two models in the extreme events analysis.     

Robust vulnerability analysis of nuclear facilities subject to external hazards

Natural hazards have the potential to trigger complex chains of events in technological installations leading to disastrous effects for the surrounding population and environment. The threat of climate change of worsening extreme weather events exacerbates the need for new models and novel methodologies able to capture the complexity of the natural-technological interaction in intuitive frameworks suitable for an interdisciplinary field such as that of risk analysis. This study proposes a novel approach for the quantification of risk exposure of nuclear facilities subject to extreme natural events. A Bayesian Network model, initially developed for the quantification of the risk of exposure from spent nuclear material stored in facilities subject to flooding hazards, is adapted and enhanced to include in the analysis the quantification of the uncertainty affecting the output due to the imprecision of data available and the aleatory nature of the variables involved. The model is applied to the analysis of the nuclear power station of Sizewell B in East Anglia (UK), through the use of a novel computational tool. The network proposed models the direct effect of extreme weather conditions on the facility along several time scenarios considering climate change predictions as well as the indirect effects of external hazards on the internal subsystems and the occurrence of human error. The main novelty of the study consists of the fully computational integration of Bayesian Networks with advanced Structural Reliability Methods, which allows to adequately represent both aleatory and epistemic aspects of the uncertainty affecting the input through the use of probabilistic models, intervals, imprecise random variables as well as probability bounds. The uncertainty affecting the output is quantified in order to attest the significance of the results and provide a complete and effective tool for risk-informed decision making.     

Optimal allocation of water and land resources for maximizing the farm income and minimizing the irrigation-induced environmental problems

The continuous decrease in good quality water and land resources and concurrent increase in global population accentuates the need of optimal allocation of these resources to fulfilling the rising food requirements. This study presents the formulation and application a management model for the optimal allocation of available good quality water and land resources to maximize the farm revenue of a canal command area. A groundwater balance constraint was imposed on the model, which moderates the irrigation-induced environmental problems of waterlogging and salinization, while making the optimal allocation of resources. The model results show a reduction in mustard, rice, and gram crop areas against an increase in sorghum, millets, and wheat areas. The net annual revenue from the command area increased by about 18 % under the optimal allocation plans. The farmers and stakeholders concerned in the actual agricultural production process are suggested to use groundwater and canal water conjunctively to maximizing the farm income. This strategy would also mitigate the hydrological imbalances to the groundwater system without installing costly drainage systems which is not viable as the quality of groundwater is poor and drainage water may cause a serious disposal problem. The developed model can be used as a reliable decision tool for taking the farm and regional level decisions of optimal land and water resources allocation and is able to solve the irrigation-induced environmental problems of agricultural systems.     

Probabilistic seasonal streamflow forecasts of the Citarum River,Indonesia, based on general circulation models

In this study we discuss probabilistic forecasts of Citarum River streamflow, which supplies 80 % of the water demands in Jakarta, Indonesia, based on general circulation model (GCM) output, for the September–November (SON) season. Retrospective forecasts of precipitation made over the period 1982–2010 with two coupled-ocean atmosphere GCMs, initialized in August, are used in conjunction historical streamflow records, with a cross-validated regression model. Pearson’s product moment correlation skill values of 0.58–0.67 are obtained, with relative operating characteristic scores of 0.67–0.84 and 0.74–0.92 for the lower and upper tercile categories of flows respectively. Both GCMs thus demonstrate promising ability to forecast below/above normal streamflow for the Citarum River flow during the SON season.     

Representing spatial dependence and spatial discontinuity in ecological epidemiology: a scale mixture approach

Variation in disease risk underlying observed disease counts is increasingly a focus for Bayesian spatial modelling, including applications in spatial data mining. Bayesian analysis of spatial data, whether for disease or other types of event, often employs a conditionally autoregressive prior, which can express spatial dependence commonly present in underlying risks or rates. Such conditionally autoregressive priors typically assume a normal density and uniform local smoothing for underlying risks. However, normality assumptions may be affected or distorted by heteroscedasticity or spatial outliers. It is also desirable that spatial disease models represent variation that is not attributable to spatial dependence. A spatial prior representing spatial heteroscedasticity within a model accommodating both spatial and non-spatial variation is therefore proposed. Illustrative applications are to human TB incidence. A simulation example is based on mainland US states, while a real data application considers TB incidence in 326 English local authorities.     

Metallogeny of the Lannigou Sedimentary Rock-hosted Disseminated Gold Deposit in Southwestern Guizhou Province, China

The Lannigou deposit is a large-sized sedimentary rock-hosted disseminated gold （SRHDG） deposit located in the Youjiang Basin. It is hosted by the Middle Triassic turbidite. Wall rock alterations, including silicification, pyritization, arsenopyritization, carbonatization and argillization, commonly occur along fractures. PGE study demonstrates that either Permian basalts or Triassic ultrabasic intrnsives are unlikely to be the main source of gold mineralization. Coupled with the lack of other nmgmatic activity in the vicinity of the mining area, an amagmatic origin is proposed. Organic matter compositions and GC-MS analysis of the ores and host rocks show that the organics in the ores and the host rocks have a common source; the organic matter in the ores was mainly indigenous. The positive correlation between S2 and Au contents, along with the common occurrence of organic inclusions, suggest involvement of organic matter in the ore-forming process in terms of promoting Au leaching from the source rocks, making colloidal Au migration possible, as well as hydrocarbon reduction of sulphate. Geological and geochemical characteristics of the Lannigou deposit suggest that it was formed through circulation of meteoric water and probably less importantly organic bearing formation water driven by high geothermal gradient caused by late Yanshanian extension, which leached Au from the source bed, and then migrated as Au-bisnlfides and colloidal Au, culminating in deposition by reduction-adsorption and surface complexation of gold onto the growth surface of arsenlan pyrite.     

基于神经网络的结构震后快速损伤评估

为了利用结构地震响应观测数据在震后对结构进行损伤快速评估,本文提出了基于BP传播神经网络多参数预测震后结构损伤程度的方法。本文设计了9个不同设防烈度和层数的钢筋混凝土框架结构,利用OpenSees有限元软件进行了非线性时程分析,并用损伤指数量化了结构损伤程度。利用有限元模拟结果,创建了神经网络的数据集,训练神经网络建立了结构参数与结构损伤指数之间的映射,对比了不同参数组合预测结构损伤水平的能力,提出了最优参数组合。结果表明：此方法预测结构损伤指数准确度高,耗时短,可为建筑工程震后损伤快速评估提供支撑。