Initialization of a Disease Transmission Model

TL;DR

This paper proposes methods to initialize a complex COVID-19 epidemiological model for Sweden, combining a detailed Markov chain with a simplified linear system to improve outbreak prediction and intervention evaluation.

Contribution

It introduces a novel approach to estimate initial states of a large epidemiological model using simplified models and compares two estimation techniques.

Findings

Simplified model captures key epidemiological variables.

Model becomes unobservable for certain parameters.

Comparison of estimation methods highlights their strengths and weaknesses.

Abstract

Approaches to the calculation of the full state vector of a larger epidemiological model for the spread of COVID-19 in Sweden at the initial time instant from available data and with a simplified dynamical model are proposed and evaluated. The larger epidemiological model is based on a continuous Markov chain and captures the demographic composition of and the transport flows between the counties of Sweden. Its intended use is to predict the outbreak development in temporal and spatial coordinates as well as across the demographic groups. It can also support evaluating and comparing of prospective intervention strategies in terms of e.g. lockdown in certain areas or isolation of specific age groups. The simplified model is a discrete time-invariant linear system that has cumulative infectious incidence, infected population, asymptomatic population, exposed population, and infectious pressure as the state variables. Since the system matrix of the model depends on a number transition rates, structural properties of the model are investigated for suitable parameter ranges. It is concluded that the model becomes unobservable for some parameter values. Two contrasting approaches to the initial state estimation are considered. One is a version of Rauch-Tung-Striebel smoother and another is based on solving a batch nonlinear optimization problem. The benefits and shortcomings of the considered estimation techniques are analyzed and compared on synthetic data for several Swedish counties.