Identifiability issues in estimating the impact of interventions on Covid-19 spread
Gustafsson, Fredrik ; Jaldén, Joakim ; Bernhardsson, Bo LU
and Soltesz, Kristian
LU
(2020)
3rd IFAC Workshop on Cyber-Physical and Human Systems, CPHS 2020
In IFAC-PapersOnLine
53.
p.829-832
- Abstract
The Covid-19 pandemic has spawned numerous dynamic modeling attempts aimed at estimation, prediction, and ultimately control. The predictive power of these attempts has varied, and there remains a lack of consensus regarding the mechanisms of virus spread and the effectiveness of various non-pharmaceutical interventions that have been enforced regionally as well as nationally. Setting out in data available in the spring of 2020, and with a now-famous model by Imperial College researchers as example, we employ an information-theoretical approach to shed light on why the predictive power of early modeling approaches have remained disappointingly poor.
Please use this url to cite or link to this publication:
https://lup.lub.lu.se/record/8da07ae4-81b2-439b-99d7-36701270aca2
- author
- Gustafsson, Fredrik
; Jaldén, Joakim
; Bernhardsson, Bo
LU
and Soltesz, Kristian
LU
- organization
- publishing date
- 2020
- type
- Chapter in Book/Report/Conference proceeding
- publication status
- published
- subject
- keywords
- Bayesian methods, Covid-19, Epidemiology, Identifiability, Sensitivity
- host publication
- 3rd IFAC Workshop on Cyber-Physical & Human Systems CPHS 2020
- series title
- IFAC-PapersOnLine
- editor
- Namerikawa, Toru
- volume
- 53
- edition
- 5
- pages
- 829 - 832
- publisher
- Elsevier
- conference name
- 3rd IFAC Workshop on Cyber-Physical and Human Systems, CPHS 2020
- conference location
- Beijing, China
- conference dates
- 2020-12-03 - 2020-12-05
- external identifiers
-
- scopus:85107860873
- ISSN
- 2405-8963
- DOI
- 10.1016/j.ifacol.2021.04.179
- project
- COVID-19: Dynamical modelling for estimation and prediction
- language
- English
- LU publication?
- yes
- id
- 8da07ae4-81b2-439b-99d7-36701270aca2
- date added to LUP
- 2021-07-09 15:23:29
- date last changed
- 2024-03-15 09:00:38
@inproceedings{8da07ae4-81b2-439b-99d7-36701270aca2,
abstract = {{<p>The Covid-19 pandemic has spawned numerous dynamic modeling attempts aimed at estimation, prediction, and ultimately control. The predictive power of these attempts has varied, and there remains a lack of consensus regarding the mechanisms of virus spread and the effectiveness of various non-pharmaceutical interventions that have been enforced regionally as well as nationally. Setting out in data available in the spring of 2020, and with a now-famous model by Imperial College researchers as example, we employ an information-theoretical approach to shed light on why the predictive power of early modeling approaches have remained disappointingly poor.</p>}},
author = {{Gustafsson, Fredrik and Jaldén, Joakim and Bernhardsson, Bo and Soltesz, Kristian}},
booktitle = {{3rd IFAC Workshop on Cyber-Physical & Human Systems CPHS 2020}},
editor = {{Namerikawa, Toru}},
issn = {{2405-8963}},
keywords = {{Bayesian methods; Covid-19; Epidemiology; Identifiability; Sensitivity}},
language = {{eng}},
pages = {{829--832}},
publisher = {{Elsevier}},
series = {{IFAC-PapersOnLine}},
title = {{Identifiability issues in estimating the impact of interventions on Covid-19 spread}},
url = {{https://lup.lub.lu.se/search/files/173678612/CovidIdentifiability.pdf}},
doi = {{10.1016/j.ifacol.2021.04.179}},
volume = {{53}},
year = {{2020}},
}