Joint Estimation of States and Parameters in Stochastic SIR Model

The classical SIR model is a fundamental building block in most epidemiological models. Despite its widespread use, its properties in filtering and estimation applications are much less well explored. Independently of how the basic SIR model is integrated into more complex models, the fundamental question is whether the states and parameters can be estimated from a fusion of available numeric measurements. The problem studied in this paper focuses on the parameter and state estimation of a stochastic SIR model from assumed direct measurements of the number of infected people in the population, and the generalisation to other measurements is left for future research. In terms of parameter estimation, two components are discussed separately. The first component is model parameter estimation assuming that the all states are measured directly. The second component is state estimation assuming known parameters. These two components are combined into an iterative state and parameter estimator. This iterative method is compared to a straightforward approach based on state augmentation of the unknown parameters. Feasibility of the problem is studied from an information-theoretic point of view using the Cramér Rao Lower Bound (CRLB). Using simulated data resembling the first wave of Covid-19 in Sweden, the iterative method outperforms the state augmentation approach. © 2022 IEEE.

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

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 nowfamous 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. Copyright (C) 2020 The Authors.

[Modeling the effect of non-pharmaceutical interventions during the corona pandemic]

The Imperial College COVID-19 Response Team (ICCRT) concluded in a series of high-profile reports that lockdown had been the most effective non-pharmaceutical intervention in 11 European countries during the initial phase of the corona pandemic. As the ICCRT used a transparent modeling framework, we were able to examine assumptions made in the model. We found that the ICCRT modified the assumptions made in their model as more data became available in a way that maintained the conclusion that lockdown was most effective. These observations suggest that modeling of non-pharmaceutical interventions during an ongoing pandemic must be interpreted with caution as sources of error can be found both in the technical execution of the modeling and the assumptions made. The secondary analysis was made possible only because the ICCRT published their methodology in detail, which is a prerequisite for scientific progress in the pandemic modeling area.