scholarly journals A stochastic metapopulation state-space approach to modeling and estimating COVID-19 spread

2021 ◽  
Vol 18 (6) ◽  
pp. 7685-7710
Author(s):  
Yukun Tan ◽  
◽  
Durward Cator III ◽  
Martial Ndeffo-Mbah ◽  
Ulisses Braga-Neto ◽  
...  
Keyword(s):  
State Space ◽  
State Space Model ◽  
Synthetic Data ◽  
Public Health Intervention ◽  
Disease Outbreaks ◽  
Real Data ◽  
Epidemiological Data ◽  
Transmission Parameters ◽  
Intervention Measures ◽  
Spatio Temporal

<abstract><p>Mathematical models are widely recognized as an important tool for analyzing and understanding the dynamics of infectious disease outbreaks, predict their future trends, and evaluate public health intervention measures for disease control and elimination. We propose a novel stochastic metapopulation state-space model for COVID-19 transmission, which is based on a discrete-time spatio-temporal susceptible, exposed, infected, recovered, and deceased (SEIRD) model. The proposed framework allows the hidden SEIRD states and unknown transmission parameters to be estimated from noisy, incomplete time series of reported epidemiological data, by application of unscented Kalman filtering (UKF), maximum-likelihood adaptive filtering, and metaheuristic optimization. Experiments using both synthetic data and real data from the Fall 2020 COVID-19 wave in the state of Texas demonstrate the effectiveness of the proposed model.</p></abstract>

scholarly journals Constructing the Singular Roesser State-Space Model Description of 3D Spatio-Temporal Dynamics From the Polynomial System Matrix

IEEE Access ◽  
2021 ◽  
Vol 9 ◽  
pp. 45632-45641
Author(s):  
Mohamed S. Boudellioua ◽  
Bartlomiej Sulikowski ◽  
Krzysztof Galkowski ◽  
Eric Rogers
Keyword(s):  
State Space ◽  
Temporal Dynamics ◽  
Polynomial System ◽  
State Space Model ◽  
Model Description ◽  
System Matrix ◽  
Space Model ◽  
Spatio Temporal

Spatio-temporal statistical models for river monitoring networks

2006 ◽  
Vol 53 (1) ◽  
pp. 9-15 ◽  
Author(s):  
L. Clement ◽  
O. Thas ◽  
P.A. Vanrolleghem ◽  
J.P. Ottoy
Keyword(s):  
Water Quality ◽  
State Space ◽  
River Water ◽  
Spatial Dependence ◽  
State Space Model ◽  
Monitoring Network ◽  
The State ◽  
Dependence Structure ◽  
Space Model ◽  
Spatio Temporal

When introducing new wastewater treatment plants (WWTP), investors and policy makers often want to know if there indeed is a beneficial effect of the installation of a WWTP on the river water quality. Such an effect can be established in time as well as in space. Since both temporal and spatial components affect the output of a monitoring network, their dependence structure has to be modelled. River water quality data typically come from a river monitoring network for which the spatial dependence structure is unidirectional. Thus the traditional spatio-temporal models are not appropriate, as they cannot take advantage of this directional information. In this paper, a state-space model is presented in which the spatial dependence of the state variable is represented by a directed acyclic graph, and the temporal dependence by a first-order autoregressive process. The state-space model is extended with a linear model for the mean to estimate the effect of the activation of a WWTP on the dissolved oxygen concentration downstream.

scholarly journals Using a spatio-temporal dynamic state-space model with the EM algorithm to patch gaps in daily riverflow series

2005 ◽  
Vol 9 (3) ◽  
pp. 209-224 ◽  
Author(s):  
B. A. Amisigo ◽  
N. C. van de Giesen
Keyword(s):  
Em Algorithm ◽  
State Space ◽  
State Space Model ◽  
Dynamic State ◽  
Spatially Correlated ◽  
State Variable ◽  
Volta Basin ◽  
The Em Algorithm ◽  
Runoff Series ◽  
Spatio Temporal

Abstract. A spatio-temporal linear dynamic model has been developed for patching short gaps in daily river runoff series. The model was cast in a state-space form in which the state variable was estimated using the Kalman smoother (RTS smoother). The EM algorithm was used to concurrently estimate both parameter and missing runoff values. Application of the model to daily runoff series in the Volta Basin of West Africa showed that the model was capable of providing good estimates of missing runoff values at a gauging station from the remaining time series at the station and at spatially correlated stations in the same sub-basin.

scholarly journals Using a spatio-temporal dynamic state-space model with the EM algorithm to patch gaps in daily riverflow series, with examples from the Volta Basin, West Africa

2005 ◽  
Vol 2 (2) ◽  
pp. 449-481 ◽  
Author(s):  
B. A. Amisigo ◽  
N. C. van de Giesen
Keyword(s):  
West Africa ◽  
Em Algorithm ◽  
State Space ◽  
State Space Model ◽  
Dynamic State ◽  
Spatially Correlated ◽  
Volta Basin ◽  
The Em Algorithm ◽  
Runoff Series ◽  
Spatio Temporal

Abstract. A spatio-temporal linear dynamic model has been developed for patching short gaps in daily river runoff series. The model was cast in a state-space form in which the state variable was estimated using the Kalman smoother (RTS smoother). The EM algorithm was used to concurrently estimate both parameter and missing runoff values. Application of the model to daily runoff series in the Volta Basin of West Africa showed that the model was capable of providing good estimates of missing runoff values at a gauging station from the remaining series at the station and at spatially correlated stations in the same sub-basin.

scholarly journals Spatio-Temporal Dynamic Fields Estimating and Modeling of Missing Points in Data Sets Using a Flexible State-Space Model

2021 ◽  
Vol 11 (19) ◽  
pp. 9050
Author(s):  
Zhichao Shi ◽  
Xiaoguang Zhou
Keyword(s):  
Time Series ◽  
State Space ◽  
State Space Model ◽  
Universal Kriging ◽  
Statistical Characteristics ◽  
Data Sets ◽  
Observation Data ◽  
Temporal Dynamic ◽  
Space Model ◽  
Spatio Temporal

Modelling and estimating spatio-temporal dynamic field are common challenges in much applied research. Most existing spatio-temporal interpolation methods require massive prior calculations and consistent observational data, resulting in low interpolation efficiency. This paper presents a flexible state-space model for iteratively fitting time-series from random missing points in data sets, namely Flexible Universal Kriging state-space model(FUKSS). In this work, a recursive method similar to Kalman filter is used to estimate the time-series, avoiding the problem of increasing data caused by Kriging space-time extension. Based on the statistical characteristics of Kriging, this method introduces a spatial selection matrix to make the different observation data and state vectors identical at different times, which solves the problem of missing data and reduces the calculation complexity. In addition, a dynamic linear autoregressive model is introduced to solve the problem that the universal Kriging state-space model cannot predict. We have demonstrated the superiority of our method by comparing it with different methods through experiments, and verified the effectiveness of this method through practical cases.

Method of determination of the text direction on the image with the use of convolutional neural network

2020 ◽  
pp. 96-99
Author(s):  
P.L. Nikolaev
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Deep Neural Network ◽  
Binary Classification ◽  
Synthetic Data ◽  
Real Data ◽  
Method Of Determination ◽  
Classification Of Images

This article deals with method of binary classification of images with small text on them Classification is based on the fact that the text can have 2 directions – it can be positioned horizontally and read from left to right or it can be turned 180 degrees so the image must be rotated to read the sign. This type of text can be found on the covers of a variety of books, so in case of recognizing the covers, it is necessary first to determine the direction of the text before we will directly recognize it. The article suggests the development of a deep neural network for determination of the text position in the context of book covers recognizing. The results of training and testing of a convolutional neural network on synthetic data as well as the examples of the network functioning on the real data are presented.

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