scholarly journals An Autoregressive Disease Mapping Model for Spatio-Temporal Forecasting

Mathematics ◽  
2021 ◽  
Vol 9 (4) ◽  
pp. 384
Author(s):  
Francisca Corpas-Burgos ◽  
Miguel A. Martinez-Beneito
Keyword(s):  
Time Series ◽  
Disease Mapping ◽  
Mortality Data ◽  
Data Sets ◽  
Modeling Tools ◽  
Polynomial Fit ◽  
Proposed Model ◽  
Temporal Models ◽  
Temporal Model ◽  
Spatio Temporal

One of the more evident uses of spatio-temporal disease mapping is forecasting the spatial distribution of diseases for the next few years following the end of the period of study. Spatio-temporal models rely on very different modeling tools (polynomial fit, splines, time series, etc.), which could show very different forecasting properties. In this paper, we introduce an enhancement of a previous autoregressive spatio-temporal model with particularly interesting forecasting properties, given its reliance on time series modeling. We include a common spatial component in that model and show how that component improves the previous model in several ways, its predictive capabilities being one of them. In this paper, we introduce and explore the theoretical properties of this model and compare them with those of the original autoregressive model. Moreover, we illustrate the benefits of this new model with the aid of a comprehensive study on 46 different mortality data sets in the Valencian Region (Spain) where the benefits of the new proposed model become evident.

scholarly journals Spatiotemporal mapping and detection of mortality cluster due to cardiovascular disease with Bayesian hierarchical framework using integrated nested Laplace approximation: A discussion of suitable statistic applications in Kersa, Oromia, Ethiopia

2018 ◽  
Vol 13 (2) ◽  
Author(s):  
Melkamu Dedefo ◽  
Henry Mwambi ◽  
Sileshi Fanta ◽  
Nega Assefa
Keyword(s):  
Cause Of Death ◽  
Linear Models ◽  
Temporal Distribution ◽  
Mortality Data ◽  
Contributing Factors ◽  
Bayesian Hierarchical ◽  
Hierarchical Framework ◽  
Temporal Models ◽  
Spatio Temporal ◽  
Non Parametric

Cardiovascular diseases (CVDs) are the leading cause of death globally and the number one cause of death globally. Over 75% of CVD deaths take place in low- and middle-income countries. Hence, comprehensive information about the spatio-temporal distribution of mortality due to cardio vascular disease is of interest. We fitted different spatio-temporal models within Bayesian hierarchical framework allowing different space-time interaction for mortality mapping with integrated nested Laplace approximations to analyze mortality data extracted from the health and demographic surveillance system in Kersa District in Hararege, Oromia Region, Ethiopia. The result indicates that non-parametric time trends models perform better than linear models. Among proposed models, one with non-parametric trend, type II interaction and second order random walk but without unstructured time effect was found to perform best according to our experience and. simulation study. An application based on real data revealed that, mortality due to CVD increased during the study period, while administrative regions in northern and south-eastern part of the study area showed a significantly elevated risk. The study highlighted distinct spatiotemporal clusters of mortality due to CVD within the study area. The study is a preliminary assessment step in prioritizing areas for further and more comprehensive research raising questions to be addressed by detailed investigation. Underlying contributing factors need to be identified and accurately quantified.

scholarly journals Comparison between VAR, GSTAR, FFNN-VAR and FFNN-GSTAR Models for Forecasting Oil Production

MATEMATIKA ◽  
2018 ◽  
Vol 34 (1) ◽  
pp. 103-111 ◽  
Author(s):  
Suhartono Suhartono ◽  
Dedy Dwi Prastyo ◽  
Heri Kuswanto ◽  
Muhammad Hisyam Lee
Keyword(s):  
Forecast Accuracy ◽  
Oil Production ◽  
Training Data ◽  
Temporal Data ◽  
Monthly Data ◽  
Vector Autoregressive ◽  
Temporal Models ◽  
Accurate Forecast ◽  
Temporal Model ◽  
Spatio Temporal

Monthly data about oil production at several drilling wells is an example of spatio-temporal data. The aim of this research is to propose nonlinear spatio-temporal model, i.e. Feedforward Neural Network - Vector Autoregressive (FFNN-VAR) and FFNN - Generalized Space-Time Autoregressive (FFNN-GSTAR), and compare their forecast accuracy to linear spatio-temporal model, i.e. VAR and GSTAR. These spatio-temporal models are proposed and applied for forecasting monthly oil production data at three drilling wells in East Java, Indonesia. There are 60 observations that be divided to two parts, i.e. the first 50 observations for training data and the last 10 observations for testing data. The results show that FFNN-GSTAR(11) and FFNN-VAR(1) as nonlinear spatio-temporal models tend to give more accurate forecast than VAR(1) and GSTAR(11) as linear spatio-temporal models. Moreover, further research about nonlinear spatio-temporal models based on neural networks and GSTAR is needed for developing new hybrid models that could improve the forecast accuracy.

An Efficient Method for Forecasting Using Fuzzy Time Series

2017 ◽  
pp. 287-304 ◽  
Author(s):  
Pritpal Singh
Keyword(s):  
Time Series ◽  
Time Series Data ◽  
Weather Forecasting ◽  
Small Error ◽  
Fuzzy Time Series ◽  
Series Data ◽  
Data Sets ◽  
Proposed Model ◽  
Temperature Forecasting ◽  
The University

Forecasting using fuzzy time series has been applied in several areas including forecasting university enrollments, sales, road accidents, financial forecasting, weather forecasting, etc. Recently, many researchers have paid attention to apply fuzzy time series in time series forecasting problems. In this paper, we present a new model to forecast the enrollments in the University of Alabama and the daily average temperature in Taipei, based on one-factor fuzzy time series. In this model, a new frequency based clustering technique is employed for partitioning the time series data sets into different intervals. For defuzzification function, two new principles are also incorporated in this model. In case of enrollments as well daily temperature forecasting, proposed model exhibits very small error rate.

scholarly journals Temporal Interlacing Network

2020 ◽  
Vol 34 (07) ◽  
pp. 11966-11973
Author(s):  
Hao Shao ◽  
Shengju Qian ◽  
Yu Liu
Keyword(s):  
State Of The Art ◽  
Temporal Information ◽  
Spatial Representations ◽  
The Past ◽  
Temporal Representation ◽  
Temporal Features ◽  
Temporal Models ◽  
Long Time ◽  
Temporal Model ◽  
Spatio Temporal

For a long time, the vision community tries to learn the spatio-temporal representation by combining convolutional neural network together with various temporal models, such as the families of Markov chain, optical flow, RNN and temporal convolution. However, these pipelines consume enormous computing resources due to the alternately learning process for spatial and temporal information. One natural question is whether we can embed the temporal information into the spatial one so the information in the two domains can be jointly learned once-only. In this work, we answer this question by presenting a simple yet powerful operator – temporal interlacing network (TIN). Instead of learning the temporal features, TIN fuses the two kinds of information by interlacing spatial representations from the past to the future, and vice versa. A differentiable interlacing target can be learned to control the interlacing process. In this way, a heavy temporal model is replaced by a simple interlacing operator. We theoretically prove that with a learnable interlacing target, TIN performs equivalently to the regularized temporal convolution network (r-TCN), but gains 4% more accuracy with 6x less latency on 6 challenging benchmarks. These results push the state-of-the-art performances of video understanding by a considerable margin. Not surprising, the ensemble model of the proposed TIN won the 1st place in the ICCV19 - Multi Moments in Time challenge. Code is made available to facilitate further research.1

scholarly journals A General Framework for Spatio-Temporal Modeling of Epidemics With Multiple Epicenters: Application to an Aerially Dispersed Plant Pathogen

2021 ◽  
Vol 7 ◽  
Author(s):  
Awino M. E. Ojwang' ◽  
Trevor Ruiz ◽  
Sharmodeep Bhattacharyya ◽  
Shirshendu Chatterjee ◽  
Peter S. Ojiambo ◽  
...  
Keyword(s):  
Downy Mildew ◽  
Data Driven ◽  
Disease Spread ◽  
Data Sets ◽  
Multiple Sources ◽  
Modeling Framework ◽  
Long Distance ◽  
Cucurbit Downy Mildew ◽  
Temporal Models ◽  
Spatio Temporal

The spread dynamics of long-distance-dispersed pathogens are influenced by the dispersal characteristics of a pathogen, anisotropy due to multiple factors, and the presence of multiple sources of inoculum. In this research, we developed a flexible class of phenomenological spatio-temporal models that extend a modeling framework used in plant pathology applications to account for the presence of multiple sources and anisotropy of biological species that can govern disease gradients and spatial spread in time. We use the cucurbit downy mildew pathosystem (caused by Pseudoperonospora cubensis) to formulate a data-driven procedure based on the 2008 to 2010 historical occurrence of the disease in the U.S. available from standardized sentinel plots deployed as part of the Cucurbit Downy Mildew ipmPIPE program. This pathosystem is characterized by annual recolonization and extinction cycles, generating annual disease invasions at the continental scale. This data-driven procedure is amenable to fitting models of disease spread from one or multiple sources of primary inoculum and can be specified to provide estimates of the parameters by regression methods conditional on a function that can accommodate anisotropy in disease occurrence data. Applying this modeling framework to the cucurbit downy mildew data sets, we found a small but consistent reduction in temporal prediction errors by incorporating anisotropy in disease spread. Further, we did not find evidence of an annually occurring, alternative source of P. cubensis in northern latitudes. However, we found a signal indicating an alternative inoculum source on the western edge of the Gulf of Mexico. This modeling framework is tractable for estimating the generalized location and velocity of a disease front from sparsely sampled data with minimal data acquisition costs. These attributes make this framework applicable and useful for a broad range of ecological data sets where multiple sources of disease may exist and whose subsequent spread is directional.

Zero-inflated spatio-temporal models for disease mapping

2017 ◽  
Vol 59 (3) ◽  
pp. 430-444 ◽  
Author(s):  
Mahmoud Torabi
Keyword(s):  
Disease Mapping ◽  
Temporal Models ◽  
Spatio Temporal

scholarly journals A comparison of modelling the spatio-temporal pattern of disease: a case study of schistosomiasis in Anhui Province, China

2021 ◽  
Author(s):  
Qing Su ◽  
Robert Bergquist ◽  
Yongwen Ke ◽  
Jianjun Dai ◽  
Zonggui He ◽  
...  
Keyword(s):  
Difference Equation ◽  
Land Surface ◽  
Prediction Error ◽  
Temporal Pattern ◽  
Vegetation Index ◽  
Anhui Province ◽  
Environmental Data ◽  
Temporal Models ◽  
Temporal Model ◽  
Spatio Temporal

Abstract Background: Various methods have been proposed in modelling spatio-temporal pattern of diseases in recent years. The construction of the spatio-temporal models can be either descriptive methods or dynamic. While the former mainly explores correlations by functions, the latter also depicts the process of transmission quantitatively. In this study, we aim to evaluate the differences in model fitting between a descriptive, spatio-temporal model and dynamic spatio-temporal model of schistosomiasis transmission in Guichi, Anhui Province, China. Methods: The parasitological and environmental data at the village level from 1991 to 2014 were obtained by cross-sectional survey. The space-time changes of schistosomiasis risk were explored by two different spatio-temporal models, the Fixed Rank Kriging (FRK) model and the Integral-Difference Equation (IDE) model, and the performance of the two models in fitting schistosomiasis risk were compared.Results: In both models, the average daily precipitation and the normalized difference vegetation index (NDVI) are significantly positively associated with schistosomiasis prevalence while distance to water bodies, the hours of daylight and day land surface temperature (LSTday) are significantly negatively associated. The overall root mean squared prediction error (MSPE) of the Integral-Difference Equation (IDE) and the FRK model were 0.35e-02 and 0.54e-02, respectively, and the correlation between the predicted and observed values of the IDE model (0.71) (p<0.01) was larger than the FRK one (0.53) (p=0.02). Our results also showed that the prediction error of the IDE model is lower than that of FRK model.Conclusions: Regions close to rivers are key areas for further implementation of schistosomiasis prevention and control strategies. The IDE model fits better than the descriptive FRK model in capturing the geographic variation of schistosomiasis. Dynamic Spatio-temporal models have the advantage of quantifying the process of disease transmission and may provide more accurate predictions, which is of great importance with reference to future modelling.

scholarly journals Disentangling Multidimensional Spatio-Temporal Data into their Common and Aberrant Responses

2014 ◽  
Author(s):  
Young Hwan Chang ◽  
Jim Korkola ◽  
Dhara N. Amin ◽  
Mark M. Moasser ◽  
Jose M. Carmena ◽  
...  
Keyword(s):  
Gene Expression ◽  
Time Series ◽  
Cell Lines ◽  
Biological Data ◽  
Series Data ◽  
State Transitions ◽  
Data Sets ◽  
Wide Range ◽  
Spatio Temporal ◽  
Experimental Trials

With the advent of high-throughput measurement techniques, scientists and engineers are starting to grapple with massive data sets and encountering challenges with how to organize, process and extract information into meaningful structures. Multidimensional spatio-temporal biological data sets such as time series gene expression with various perturbations with different cell lines, or neural spike data sets across many experimental trials have the potential to acquire insight across multiple dimensions. For this potential to be realized, we need a suitable representation to turn data into insight. Since a wide range of experiments and the (unknown) complexity of underlying system make biological data more heterogeneous than those in other fields, we propose the method based on Robust Principal Component Analysis (RPCA), which is well suited for extracting principal components where we have corrupted observations. The proposed method provides us a new representation of these data sets which consists of its common and aberrant response. This representation might help users to acquire a new insight from data. %For example, identifying common event-related neural features across many experimental trials can be used as a signature to detect discrete events or state transitions. Also, the proposed method can be useful to biologists in clustering and analyzing gene expression time series data with a new perspective, for example, it can not only extract canonical cell signaling response but also inform them to get insight into the heterogeneity of different responses across different cell lines.

scholarly journals Adversarial Dynamic Shapelet Networks

2020 ◽  
Vol 34 (04) ◽  
pp. 5069-5076 ◽  
Author(s):  
Qianli Ma ◽  
Wanqing Zhuang ◽  
Sen Li ◽  
Desen Huang ◽  
Garrison Cottrell
Keyword(s):  
Time Series ◽  
Gradient Descent ◽  
Time Series Data ◽  
Back Propagation ◽  
Series Data ◽  
Data Sets ◽  
Training Strategy ◽  
Original Intention ◽  
Proposed Model ◽  
Adversarial Training

Shapelets are discriminative subsequences for time series classification. Recently, learning time-series shapelets (LTS) was proposed to learn shapelets by gradient descent directly. Although learning-based shapelet methods achieve better results than previous methods, they still have two shortcomings. First, the learned shapelets are fixed after training and cannot adapt to time series with deformations at the testing phase. Second, the shapelets learned by back-propagation may not be similar to any real subsequences, which is contrary to the original intention of shapelets and reduces model interpretability. In this paper, we propose a novel shapelet learning model called Adversarial Dynamic Shapelet Networks (ADSNs). An adversarial training strategy is employed to prevent the generated shapelets from diverging from the actual subsequences of a time series. During inference, a shapelet generator produces sample-specific shapelets, and a dynamic shapelet transformation uses the generated shapelets to extract discriminative features. Thus, ADSN can dynamically generate shapelets that are similar to the real subsequences rather than having arbitrary shapes. The proposed model has high modeling flexibility while retaining the interpretability of shapelet-based methods. Experiments conducted on extensive time series data sets show that ADSN is state-of-the-art compared to existing shapelet-based methods. The visualization analysis also shows the effectiveness of dynamic shapelet generation and adversarial training.

Topography and displacement of polar glaciers from multi-temporal SAR interferograms

Polar Record ◽  
2007 ◽  
Vol 43 (4) ◽  
pp. 331-343 ◽  
Author(s):  
Franz J. Meyer
Keyword(s):  
Real Data ◽  
Data Sets ◽  
Temporal Data ◽  
Error Sources ◽  
Processing Step ◽  
Multi Temporal ◽  
Least Squares Adjustment ◽  
Temporal Model ◽  
Spatio Temporal ◽  
A New Technique

ABSTRACTThis paper describes a new technique simultaneously to estimate topography and motion of polar glaciers from multi-temporal SAR interferograms. The approach is based on a combination of several SAR interferograms in a least-squares adjustment using the Gauss-Markov model. For connecting the multi-temporal data sets, a spatio-temporal model is proposed that describes the properties of the surface and its temporal evolution. Rigorous mathematical modelling of functional and stochastic relations allows for a systematic description of the processing chain. It is also an optimal tool to set parameters for the statistics of every individual processing step, and the propagation of errors into the results. Within the paper theoretical standard deviations of the unknowns are calculated depending on the configuration of the data sets. The influence of gross errors in the observations and the effect of non-modelled error sources on the unknowns are estimated. A validation of the approach based on real data concludes the paper.

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