Bo Bernhardsson
Modellering och styrning av osäkra system. Programdirektör för masterprogrammet i maskininlärning, system och styrning.
Identifiability issues in estimating the impact of interventions on Covid-19 spread
Författare
Redaktör
- Toru Namerikawa
Summary, in English
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.
Avdelning/ar
- Institutionen för reglerteknik
- ELLIIT: the Linköping-Lund initiative on IT and mobile communication
Publiceringsår
2020
Språk
Engelska
Sidor
829-832
Publikation/Tidskrift/Serie
IFAC-PapersOnLine
Volym
53
Fulltext
- Available as PDF - 262 kB
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Dokumenttyp
Konferensbidrag
Förlag
Elsevier
Ämne
- Public Health, Global Health, Social Medicine and Epidemiology
Nyckelord
- Bayesian methods
- Covid-19
- Epidemiology
- Identifiability
- Sensitivity
Conference name
3rd IFAC Workshop on Cyber-Physical and Human Systems, CPHS 2020
Conference date
2020-12-03 - 2020-12-05
Conference place
Beijing, China
Status
Published
Projekt
- COVID-19: Dynamical modelling for estimation and prediction
ISBN/ISSN/Övrigt
- ISSN: 2405-8963