scholarly journals Nonlinear dynamics of the human smooth pursuit system in health and disease: Model structure and parameter estimation

2017 ◽  
Author(s):  
Viktor Bro ◽  
Alexander V. Medvedev
Keyword(s):  
Nonlinear Dynamics ◽  
Parameter Estimation ◽  
Smooth Pursuit ◽  
Disease Model ◽  
Model Structure ◽  
Pursuit System ◽  
Health And Disease

Towards Conditional Parameter Estimation for Automatic Model Structure Identification: Using Mixed-Integer Calibration for Model Development

2020 ◽  
Author(s):  
Diana Spieler ◽  
Juliane Mai ◽  
Bryan Tolson ◽  
James Craig ◽  
Niels Schütze
Keyword(s):  
Parameter Estimation ◽  
Mixed Integer ◽  
Structure Identification ◽  
Model Parameters ◽  
Suitable Model ◽  
Model Structure ◽  
Hydrologic Modelling ◽  
Vast Number ◽  
Calibration Process ◽  
Decision Variables

<p>A recently introduced framework for Automatic Model Structure Identification (AMSI) allows to simultaneously optimize model structure choices (integer decision variables) and parameter values (continuous decision variables) in hydrologic modelling. By combining the mixed-integer optimization algorithm DDS and the flexible hydrologic modelling framework RAVEN, AMSI is able to test a vast number of model structure and parameter combinations in order to identify the most suitable model structure for representing the rainfall runoff behavior of a catchment. The model structure and all potentially active model parameters are calibrated simultaneously. This causes a certain degree of inefficiency during the calibration process, as variables might be perturbed that are not currently relevant for the tested model structure. In order to avoid this, we propose an adaption of the current DDS algorithm allowing for conditional parameter estimation. Parameters will only be perturbed during the calibration process if they are relevant for the model structure that is currently tested. The conditional parameter estimation setup will be compared to the standard DDS algorithm for multiple AMSI test cases. We will show if and how conditional parameter estimation increases the efficiency of AMSI.</p>

scholarly journals Nonlinear dynamics of immunogenic tumors: Parameter estimation and global bifurcation analysis

1994 ◽  
Vol 56 (2) ◽  
pp. 295-321 ◽  
Author(s):  
Vladimir A. Kuznetsov ◽  
Iliya A. Makalkin ◽  
Mark A. Taylor ◽  
Alan S. Perelson
Keyword(s):  
Nonlinear Dynamics ◽  
Parameter Estimation ◽  
Bifurcation Analysis ◽  
Global Bifurcation

scholarly journals Modelling the impact of tuberculosis preventive therapy: the importance of disease progression assumptions

2019 ◽  
Author(s):  
Tom Sumner ◽  
Richard G. White
Keyword(s):  
Disease Model ◽  
Parametric Uncertainty ◽  
Preventive Therapy ◽  
Transmission Model ◽  
Number Needed To Treat ◽  
Model Structure ◽  
Risk Of Progression ◽  
Baseline Incidence ◽  
Modelling Studies ◽  
The Impact

AbstractBackgroundFollowing infection with Mycobacterium tuberculosis (M.tb) individuals may rapidly develop tuberculosis (TB) disease or enter “latent” infection state with a low risk of progression to disease. The mechanisms underlying this process are incompletely known. Mathematical models use a variety of structures and parameterisations to represent this progression from infection with M.tb to disease. This structural and parametric uncertainty may affect the predicted impact of interventions leading to incorrect conclusions and decision making.MethodsWe used a simple dynamic transmission model to explore the effect of uncertainty in model structure and parameterisation on the predicted impact of scaling up preventive therapy. We compared three commonly used model structures and used parameter values from two different data sources. Models 1 and 2 are equally consistent with observations of the time from infection to disease. Model 3, produces a worse fit to the data, but is widely used in published modelling studies. We simulated treatment of 5% of all M.tb infected individuals per year in a population of 10,000 and calculated the reduction in TB incidence and number needed to treat to avert one TB case over 10 years.ResultsThe predicted impact of the preventive therapy intervention depended on both the model structure and the parameterisation of that structure. For example, at a baseline annual TB incidence of 500/100,000, the impact ranged from 11% to 27% and the number needed to treat to avert one TB case varied between 38 and 124. The relative importance of structure and parameters varied depending on the baseline incidence of TB.DiscussionOur analysis shows that the choice of model structure and the parameterisation can influence the predicted impact of interventions. Modelling studies should consider incorporating structural uncertainty in their analysis. Not doing so may lead to incorrect conclusions on the impact of interventions.

scholarly journals How much time have we got?

2021 ◽  
Vol 4 ◽  
Author(s):  
Kyrre Kausrud ◽  
Karin Lagesen ◽  
Ryan Easterday ◽  
Jason Whittington ◽  
Wendy Turner ◽  
...  
Keyword(s):  
Infectious Diseases ◽  
Simulation Model ◽  
Multiple Stressors ◽  
Disease Model ◽  
Emerging Infectious Diseases ◽  
Probability Of Detection ◽  
Training Data ◽  
Model Structure ◽  
Past Performance ◽  
Diverse Range

Here we present a developing probabilistic simulation model and tool to assess likely lead times from emergence to detection and arrival for new emerging infectious diseases (EIDs). Key aspects include combining real-world data available on multiple scales with a flexible underlying disease model. As demonstrated by the SARS-CoV-2 pandemic and other emerging infectious diseases, there is a need for scenario exploration for mitigation, surveillance and preparedness strategies. Existing simulation engines have been assessed but found to offer an insufficient set of features with regards to flexibility and control over processes, disease model structure and data sets incorporated for a wider enough range of diseases, circumstances, cofactors and scenarios (Heslop et al. 2017) to suit our aims. We are therefore developing the first version of a simulation model designed to be able to incorporate a diverse range of disease models and data sources including multiple transmission and infectivity stages, multiple host species, varying and evolving virulence, socioeconomic differences, climate events and public health countermeasures. It is designed to be flexible with respect to implementing both improvements in the model structure and data as they become available. It is based on a discrete-time (daily) structure where spatial movement and transition between categories and detection are stochastic rates dependent on spatial data and past states in the model, while being informed by the most suitable data available (Fig. 1). The probability of detection is in itself treated as a probabilistic process and treated as a variable dependent on socioeconomic factors and parameterized by past performance, yet open for manipulation in scenario exploration regarding surveillance and reporting effectiveness. Pathogen hotspot data are sourced from literature and included as a probabilistic assessment of emergence as well as a source of cofactor data (Allen et al. 2017), population data are adressed (Leyk et al. 2019) for utility and combined with data on local connectivity (Nelson et al. 2019) and transnational movement patterns (Recchi et al. 2019Fig. 1), as well as an increasing set of ecological and socioeconomic candidate variables. Model parameterization relies on a machine learning framework with matching to the often partial data available for known relevant disease cases as the training data, and assessing them for plausible ranges of input for new, hypothetical EIDs. As parameterizations improve, the range of scenarios to explore will incorporate effects of climate change and multiple stressors. When a suitable version becomes available it will be shared under a MIT license.

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