scholarly journals An application of the ensemble Kalman filter in epidemiological modelling

PLoS ONE ◽  
2021 ◽  
Vol 16 (8) ◽  
pp. e0256227
Author(s):  
Rajnesh Lal ◽  
Weidong Huang ◽  
Zhenquan Li
Keyword(s):  
Parameter Estimation ◽  
Model Simulation ◽  
Synthetic Data ◽  
Estimation Methods ◽  
Parameter Estimates ◽  
Model Parameters ◽  
Model Parameter ◽  
Reliable Model ◽  
Reported Data ◽  
Parameter Estimation Methods

Since the novel coronavirus (COVID-19) outbreak in China, and due to the open accessibility of COVID-19 data, several researchers and modellers revisited the classical epidemiological models to evaluate their practical applicability. While mathematical compartmental models can predict various contagious viruses’ dynamics, their efficiency depends on the model parameters. Recently, several parameter estimation methods have been proposed for different models. In this study, we evaluated the Ensemble Kalman filter’s performance (EnKF) in the estimation of time-varying model parameters with synthetic data and the real COVID-19 data of Hubei province, China. Contrary to the previous works, in the current study, the effect of damping factors on an augmented EnKF is studied. An augmented EnKF algorithm is provided, and we present how the filter performs in estimating models using uncertain observational (reported) data. Results obtained confirm that the augumented-EnKF approach can provide reliable model parameter estimates. Additionally, there was a good fit of profiles between model simulation and the reported COVID-19 data confirming the possibility of using the augmented-EnKF approach for reliable model parameter estimation.

A Comparison of the Internal Validity of Alternative Parameter Estimation Methods in Decompositional Multiattribute Preference Models

1979 ◽  
Vol 16 (3) ◽  
pp. 313-322 ◽  
Author(s):  
Arun K. Jain ◽  
Franklin Acito ◽  
Naresh K. Malhotra ◽  
Vijay Mahajan
Keyword(s):  
Parameter Estimation ◽  
Data Collection ◽  
Data Base ◽  
Internal Validity ◽  
Detailed Comparison ◽  
Estimation Methods ◽  
Parameter Estimates ◽  
Alternative Approaches ◽  
Preference Models ◽  
Parameter Estimation Methods

Since 1971, interest in the use of decompositional multiattribute preference models in marketing has been increasing. The applications have varied in terms of the type of data used, behavior predicted, and methods used for estimating parameters. The authors examine the effect of different data collection and estimation procedures on parameter estimates and their stability and validity. An actual data base is used. A detailed comparison is made of the alternative approaches of parameter estimation and suggestions are given for the potential users of decompositional multiattribute preference models.

scholarly journals Estimation of soil properties by coupled inversion of electrical resistance, temperature, and moisture content data

2019 ◽  
Author(s):  
Elchin E. Jafarov ◽  
Dylan R. Harp ◽  
Ethan T. Coon ◽  
Baptiste Dafflon ◽  
Anh Phuong Tran ◽  
...  
Keyword(s):  
Parameter Estimation ◽  
Soil Properties ◽  
Electrical Resistance ◽  
Synthetic Data ◽  
The Arctic ◽  
Estimation Methods ◽  
Coupled Models ◽  
Using Data ◽  
Parameter Estimation Methods ◽  
Initial Starting

Abstract. Studies indicate greenhouse gas emissions following permafrost thaw will amplify current rates of atmospheric warming, a process referred to as the permafrost carbon feedback (PCF). However, large uncertainties exist regarding the timing and magnitude of the PCF, in part due to uncertainties associated with subsurface permafrost parameterization and structure. Development of robust parameter estimation methods for permafrost-rich soils is becoming urgent under accelerated warming of the Arctic. Improved parameterization of the subsurface properties in land system models would lead to improved predictions and reduction of modeling uncertainty. In this work we set the groundwork for future parameter estimation (PE) studies by developing and evaluating a joint PE framework that estimates soil properties from time-series of soil temperature, moisture, and electrical resistance measurements. The framework utilizes the PEST (Model Independent Parameter Estimation and Uncertainty Analysis) toolbox and coupled hydro-thermal-geophysical modeling. We test the framework against synthetic data, providing a proof-of-concept for the approach. We use specified subsurface parameters and coupled models to setup a synthetic state, perturb the parameters, then verify that our PE framework is able to recover the parameters and synthetic state. To evaluate the accuracy and robustness of the approach we perform multiple tests for a perturbed set of initial starting parameter combinations. In addition, we evaluate the relative worth of including various types and amount of data needed to improve predictions. The results of the PE tests suggest that using data from multiple observational datasets improves the accuracy of the estimated parameters.

scholarly journals Kinetic Parameter Estimation Using Modified Differential Evolution

2011 ◽  
pp. 113-116
Author(s):  
Rakesh Angira
Keyword(s):  
Parameter Estimation ◽  
Kinetic Parameter ◽  
Differential Evolution ◽  
Chemical Engineering ◽  
Estimation Methods ◽  
Model Parameters ◽  
De Algorithm ◽  
Kinetic Parameter Estimation ◽  
Advanced Control ◽  
Parameter Estimation Methods

For the development of mathematical models in chemical engineering, the parameter estimation methods are very important as design, optimization and advanced control of chemical processes depend on values of model parameters obtained from experimental data. Nonlinearity in models makes the estimation of parameter more difficult and more challenging. This paper presents an evolutionary computation approach for solving such problems. In this work, a modified version of Differential Evolution (DE) algorithm [named Modified Differential evolution (MDE)] is used to solve a kinetic parameter estimation problem from chemical engineering field. The computational efficiency of MDE is compared with that of original DE and Trigonometric Differential Evolution (TDE). Results indicate that performance of MDE algorithm is better than that of DE and TDE.

Identification of Biodegradation models under model and data uncertainty

1996 ◽  
Vol 33 (2) ◽  
pp. 91-105 ◽  
Author(s):  
Peter A. Vanrolleghem ◽  
Karel J. Keesman
Keyword(s):  
Parameter Estimation ◽  
Real World ◽  
Data Uncertainty ◽  
Estimation Methods ◽  
Parameter Estimates ◽  
Local Minima ◽  
Real World Data ◽  
Correlation Matrices ◽  
Parameter Estimation Methods ◽  
Parameter Values

In this paper a number of nonlinear parameter estimation methods are evaluated with respect to their ability to identify biodegradation models from “real-world” data. Important aspects are then the sensitivity to local minima, rate of convergence, required prior knowledge and direct or indirect availability of parameter estimates uncertainty. Furthermore, it is important whether a method is robust against invalid assumptions. In addition to the final parameter values, covariance and correlation matrices, confidence intervals and residual sequences are presented to obtain information about the validity of the models and noise assumptions. Finally, recommendations on the method's applicability range are provided.

Sign in / Sign up

Export Citation Format

Share Document