Identifiability issues in estimating the impact of interventions on Covid-19 spread

Fredrik Gustafsson; Joakim Jaldén; Bo Bernhardsson; Kristian Soltesz

doi:10.1016/j.ifacol.2021.04.179

Identifiability issues in estimating the impact of interventions on Covid-19 spread

IFAC Pap OnLine. 2020;53(5):829-832. doi: 10.1016/j.ifacol.2021.04.179. Epub 2021 May 26.

Authors

Fredrik Gustafsson¹, Joakim Jaldén², Bo Bernhardsson³, Kristian Soltesz³

Affiliations

¹ Linköping University, Div. of Automatic Control, Linköping, Sweden.
² KTH Royal Institute of Technology, Div. Division of Information Science and Engineering, Stockholm, Sweden.
³ Lund University, Dept. Automatic Control, Lund, Sweden.

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.

Keywords: Bayesian methods; Covid-19; Epidemiology; Identifiability; Sensitivity.