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Limitations of time-delayed case isolation in heterogeneous SIR models

Hansson, Jonas LU orcid ; Govaert, Alain LU ; Pates, Richard LU ; Tegling, Emma LU and Soltesz, Kristian LU orcid (2022) American Control Conference, 2022 2022. p.2994-2999
Abstract
The lack of methods to evaluate mechanical function of donated hearts in the context of transplantation imposes large precautionary margins, translating into a low utilization rate of donor organs. This has spawned research into cyber-physical models constituting artificial afterloads (arterial trees), that can serve to evaluate the contractile capacity of the donor heart. The Windkessel model is an established linear time-invariant afterload model, that researchers committed to creating a cyber-physical afterload have used as a template. With aortic volumetric flow as input and aortic pressure as output, it is not directly obvious how a Windkessel model will respond to changes in heart contractility. We transform the classic Windkessel... (More)
The lack of methods to evaluate mechanical function of donated hearts in the context of transplantation imposes large precautionary margins, translating into a low utilization rate of donor organs. This has spawned research into cyber-physical models constituting artificial afterloads (arterial trees), that can serve to evaluate the contractile capacity of the donor heart. The Windkessel model is an established linear time-invariant afterload model, that researchers committed to creating a cyber-physical afterload have used as a template. With aortic volumetric flow as input and aortic pressure as output, it is not directly obvious how a Windkessel model will respond to changes in heart contractility. We transform the classic Windkessel model to relate power, rather than flow, to pressure. This alters the model into a differential-algebraic equation, albeit one that is straightforward to simulate. We then propose a power signal model, that is based on pressure and flow measurements and optimal in a Bayesian sense within the class of C2 signals. Finally, we show how the proposed signal model can be used to create relevant simulation scenarios, and use this to illustrate why it is problematic to use the Windkessel model as a basis for designing a clinically relevant artificial afterload. (Less)
Please use this url to cite or link to this publication:
author
; ; ; and
organization
publishing date
type
Chapter in Book/Report/Conference proceeding
publication status
published
subject
host publication
American Control Conference
volume
2022
pages
2994 - 2999
conference name
American Control Conference, 2022
conference location
GA, United States
conference dates
2022-06-08 - 2022-06-10
external identifiers
  • scopus:85138490998
DOI
10.23919/ACC53348.2022.9867465
project
COVID-19: Dynamical modelling for estimation and prediction
language
English
LU publication?
yes
id
f1586e9e-0f3b-4b50-bd83-24777b885498
alternative location
https://arxiv.org/abs/2209.09186
date added to LUP
2022-03-04 14:48:18
date last changed
2023-12-01 11:24:41
@inproceedings{f1586e9e-0f3b-4b50-bd83-24777b885498,
  abstract     = {{The lack of methods to evaluate mechanical function of donated hearts in the context of transplantation imposes large precautionary margins, translating into a low utilization rate of donor organs. This has spawned research into cyber-physical models constituting artificial afterloads (arterial trees), that can serve to evaluate the contractile capacity of the donor heart. The Windkessel model is an established linear time-invariant afterload model, that researchers committed to creating a cyber-physical afterload have used as a template. With aortic volumetric flow as input and aortic pressure as output, it is not directly obvious how a Windkessel model will respond to changes in heart contractility. We transform the classic Windkessel model to relate power, rather than flow, to pressure. This alters the model into a differential-algebraic equation, albeit one that is straightforward to simulate. We then propose a power signal model, that is based on pressure and flow measurements and optimal in a Bayesian sense within the class of C2 signals. Finally, we show how the proposed signal model can be used to create relevant simulation scenarios, and use this to illustrate why it is problematic to use the Windkessel model as a basis for designing a clinically relevant artificial afterload.}},
  author       = {{Hansson, Jonas and Govaert, Alain and Pates, Richard and Tegling, Emma and Soltesz, Kristian}},
  booktitle    = {{American Control Conference}},
  language     = {{eng}},
  pages        = {{2994--2999}},
  title        = {{Limitations of time-delayed case isolation in heterogeneous SIR models}},
  url          = {{http://dx.doi.org/10.23919/ACC53348.2022.9867465}},
  doi          = {{10.23919/ACC53348.2022.9867465}},
  volume       = {{2022}},
  year         = {{2022}},
}