Identifiability issues in estimating the impact of interventions on Covid-19 spread⁎The work behind this extended abstract was partially supported by the ELLIIT Strategic Research Area, by the Wallenberg AI, Autonomous Systems and Software Program (WASP) funded by the Knut and Alice Wallenberg Foundation, and by the Swedish Foundation for Strategic Research (SSF) via the project ASSEMBLE (contract number: RIT15-0012). Issue 5 (2020)

• Record Type: Journal Article
• Authors: Gustafsson, Fredrik; Jaldén, Joakim; Bernhardsson, Bo; Soltesz, Kristian
• Is Part Of: IFAC-PapersOnLine. Volume 53:Issue 5(2020)
• Page Start: 829
• Page End: 832
• Publication Date: 2020
• Subjects: Epidemiology -- Covid-19 -- Sensitivity -- Identifiability -- Bayesian methods; Automatic control -- Periodicals; 629.805
• DOI: 10.1016/j.ifacol.2021.04.179
• Languages: English
• ISSNs: 2405-8963

Abstract

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.