scholarly journals Quantifying non-communicable diseases' burden in Egypt using State-Space model

2021 ◽  
Author(s):  
Somaya Mahmoud El-Saadani ◽  
Mohamed Mostafa Saleh ◽  
Sarah Assem Ibrahim
Keyword(s):  
Time Series ◽  
Health Services ◽  
State Space ◽  
Bayesian Methods ◽  
Time Series Data ◽  
State Space Model ◽  
Series Data ◽  
Non Communicable Diseases ◽  
Space Model ◽  
Health Burden

The study aimed to model and quantify the health burden induced by four non-communicable diseases in Egypt, the first to be conducted in the context of a less developing county. The study used the State-Space model and adopted two Bayesian methods: Particle Filter and Particle Independent Metropolis-Hastings to model and estimate the NCDs' health burden trajectories. We drew on time-series data of the International Health Metric Evaluation, CAPMAS's Annual Bulletin of Health Services Statistics, the World Bank, and WHO data. Both Bayesian methods showed that the burden trajectories are on the rise. Most of the findings agreed with our assumptions and are in line with the literature. Previous year burden strongly predicts the burden of the current year. High prevalence of the risk factors, disease prevalence, and the disease's severity level all increase illness burden. Years of life lost due to death has high loadings in most of the diseases. Contrary to the study assumption, results found a negative relationship between disease burden and health services utilization which can be attributed to the lack of full health insurance coverage and the pattern of health care seeking behavior in Egypt. Our study highlights that Particle Independent Metropolis-Hastings is sufficient in estimating the parameters of the study model, in the case of time-constant parameters. The study recommends using state Space models with Bayesian estimation approaches with time-series data in public health and epidemiology research.

scholarly journals Quantifying non-communicable diseases’ burden in Egypt using State-Space model

PLoS ONE ◽  
2021 ◽  
Vol 16 (8) ◽  
pp. e0245642
Author(s):  
Somaya El-Saadani ◽  
Mohamed Saleh ◽  
Sarah A. Ibrahim
Keyword(s):  
Time Series ◽  
Health Services ◽  
State Space ◽  
Bayesian Methods ◽  
Time Series Data ◽  
State Space Model ◽  
Series Data ◽  
Non Communicable Diseases ◽  
Space Model ◽  
Health Burden

The study aimed to model and quantify the health burden induced by four non-communicable diseases (NCDs) in Egypt, the first to be conducted in the context of a less developing county. The study used the State-Space model and adopted two Bayesian methods: Particle Filter and Particle Independent Metropolis-Hastings to model and estimate the NCDs’ health burden trajectories. We drew on time-series data of the International Health Metric Evaluation, the Central Agency for Public Mobilization and Statistics (CAPMAS) Annual Bulletin of Health Services Statistics, the World Bank, and WHO data. Both Bayesian methods showed that the burden trajectories are on the rise. Most of the findings agreed with our assumptions and are in line with the literature. Previous year burden strongly predicts the burden of the current year. High prevalence of the risk factors, disease prevalence, and the disease’s severity level all increase illness burden. Years of life lost due to death has high loadings in most of the diseases. Contrary to the study assumption, results found a negative relationship between disease burden and health services utilization which can be attributed to the lack of full health insurance coverage and the pattern of health care seeking behavior in Egypt. Our study highlights that Particle Independent Metropolis-Hastings is sufficient in estimating the parameters of the study model, in the case of time-constant parameters. The study recommends using state Space models with Bayesian estimation approaches with time-series data in public health and epidemiology research.

scholarly journals LSTM-Guided Coaching Assistant for Table Tennis Practice

Sensors ◽  
2018 ◽  
Vol 18 (12) ◽  
pp. 4112 ◽  
Author(s):  
Se-Min Lim ◽  
Hyeong-Cheol Oh ◽  
Jaein Kim ◽  
Juwon Lee ◽  
Jooyoung Park
Keyword(s):  
Time Series ◽  
State Space ◽  
Time Series Data ◽  
State Space Model ◽  
Skill Assessment ◽  
Series Data ◽  
High Dimensional ◽  
Table Tennis ◽  
Space Model ◽  
Low Dimensional

Recently, wearable devices have become a prominent health care application domain by incorporating a growing number of sensors and adopting smart machine learning technologies. One closely related topic is the strategy of combining the wearable device technology with skill assessment, which can be used in wearable device apps for coaching and/or personal training. Particularly pertinent to skill assessment based on high-dimensional time series data from wearable sensors is classifying whether a player is an expert or a beginner, which skills the player is exercising, and extracting some low-dimensional representations useful for coaching. In this paper, we present a deep learning-based coaching assistant method, which can provide useful information in supporting table tennis practice. Our method uses a combination of LSTM (Long short-term memory) with a deep state space model and probabilistic inference. More precisely, we use the expressive power of LSTM when handling high-dimensional time series data, and state space model and probabilistic inference to extract low-dimensional latent representations useful for coaching. Experimental results show that our method can yield promising results for characterizing high-dimensional time series patterns and for providing useful information when working with wearable IMU (Inertial measurement unit) sensors for table tennis coaching.

scholarly journals Estimating Distributions of Parameters in Nonlinear State Space Models with Replica Exchange Particle Marginal Metropolis–Hastings Method

Entropy ◽  
2022 ◽  
Vol 24 (1) ◽  
pp. 115
Author(s):  
Hiroaki Inoue ◽  
Koji Hukushima ◽  
Toshiaki Omori
Keyword(s):  
State Space ◽  
Latent Variables ◽  
Time Series Data ◽  
State Space Model ◽  
The State ◽  
Series Data ◽  
Replica Exchange ◽  
Space Model ◽  
Marginal Posterior Distribution ◽  
Exchange Particle

Extracting latent nonlinear dynamics from observed time-series data is important for understanding a dynamic system against the background of the observed data. A state space model is a probabilistic graphical model for time-series data, which describes the probabilistic dependence between latent variables at subsequent times and between latent variables and observations. Since, in many situations, the values of the parameters in the state space model are unknown, estimating the parameters from observations is an important task. The particle marginal Metropolis–Hastings (PMMH) method is a method for estimating the marginal posterior distribution of parameters obtained by marginalization over the distribution of latent variables in the state space model. Although, in principle, we can estimate the marginal posterior distribution of parameters by iterating this method infinitely, the estimated result depends on the initial values for a finite number of times in practice. In this paper, we propose a replica exchange particle marginal Metropolis–Hastings (REPMMH) method as a method to improve this problem by combining the PMMH method with the replica exchange method. By using the proposed method, we simultaneously realize a global search at a high temperature and a local fine search at a low temperature. We evaluate the proposed method using simulated data obtained from the Izhikevich neuron model and Lévy-driven stochastic volatility model, and we show that the proposed REPMMH method improves the problem of the initial value dependence in the PMMH method, and realizes efficient sampling of parameters in the state space models compared with existing methods.

Estimating long-term tree mortality rate time series by combining data from periodic inventories and harvest reports in a Bayesian state-space model

2013 ◽  
Vol 292 ◽  
pp. 64-74 ◽  
Author(s):  
Katalin Csilléry ◽  
Maëlle Seignobosc ◽  
Valentine Lafond ◽  
Georges Kunstler ◽  
Benoît Courbaud
Keyword(s):  
Time Series ◽  
Mortality Rate ◽  
State Space ◽  
Tree Mortality ◽  
State Space Model ◽  
Space Model ◽  
Combining Data

scholarly journals Comparison of autoregressive moving average and state space methods for monthly time series modelling of Labrador and South-East Quebec river flows

2015 ◽  
Vol 51 (3) ◽  
pp. 200-218 ◽  
Author(s):  
Carissa Sparkes ◽  
Leonard M. Lye ◽  
Susan Richter
Keyword(s):  
Time Series ◽  
State Space ◽  
Time Series Data ◽  
Moving Average ◽  
Series Data ◽  
Hydrologic Modelling ◽  
Stream Flows ◽  
Time Series Modelling ◽  
High Level ◽  
Accuracy Of Prediction

Time series data such as monthly stream flows can be modelled using time series methods and then used to simulate or forecast flows for short term planning. Two methods of time series modelling were reviewed and compared: the well-known auto regressive moving average (ARMA) method and the state-space time-series (SSTS) method. ARMA has been used in hydrology to model and simulate flows with good results and is widely accepted for this purpose. SSTS modelling is a more recently developed method that is relatively unused for hydrologic modelling. This paper focuses on modelling the stream flows from basins of different sizes using these two time series modelling methods and comparing the results. Three rivers in Labrador and South-East Quebec were modelled: the Romaine, Ugjoktok and Alexis Rivers. Both models were compared for accuracy of prediction, ease of software use and simplicity of model to determine the preferred time series methodology approach for modelling these rivers. The SSTS was considered very easy to use but model diagnostics were found to require a high level of statistical understanding. Ultimately, the ARMA method was determined to be the better method for the typical engineer to use, considering the diagnostics were simple and the monthly flows could be easily simulated to verify results.

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