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 Cramer Rao Lower Bound (CRLB). Using simulated data resembling the first wave of Covid-19 in Sweden, the iterative method outperforms the state augmentation approach.
Scalable Kalman Filters funded by the Swedish Research Council