Time Dynamics of the Spread of Virus Mutants with Increased Infectiousness in Austria

Martin Bicher; Claire Rippinger; Niki Popper

doi:10.1016/j.ifacol.2022.09.135

Time Dynamics of the Spread of Virus Mutants with Increased Infectiousness in Austria

IFAC Pap OnLine. 2022;55(20):445-450. doi: 10.1016/j.ifacol.2022.09.135. Epub 2022 Sep 23.

Authors

Martin Bicher^{1

2}, Claire Rippinger², Niki Popper^{1

2

3}

Affiliations

¹ Institute of Information Systems Engineering, TU Wien, Favoritenstraße 11, 1050 Vienna, Austria.
² dwh GmbH, Neustiftgasse 57-59, 1070 Vienna, Austria.
³ DEXHELPP, Association for Decision Support for Health Policy and Planning, Neustiftgasse 57, A-1070 Vienna, Austria.

Abstract

In spring 2021, it became eminent that the emergence of higher infectious virus mutants of SARS-CoV-2 is an unpredictable and omnipresent threat for fighting the pandemic and has wide-ranging implications on containment policies and herd immunity goals. To quantify the risk related to a more infectious virus variant, extensive surveillance and proper data analysis are required. Key observable of the analysis is the excess infectiousness defined as the quotient between the effective reproduction rate of the new and the previous variants. A proper estimate of this parameter allows forecasts for the epidemic situation after the new variant has taken over and enables estimates by how much the new variant will increase the herd immunity threshold. Here, we present and analyse methods to estimate this crucial parameter based on surveillance data. We specifically focus on the time dynamics of the ratio of mutant infections among the new confirmed cases and discuss, how the excess infectiousness can be estimated based on surveillance data for this ratio. We apply a modified susceptible-infectious-recovered approach and derive formulas which can be used to estimate this parameter. We will provide adaptations of the formulas which are able to cope with imported cases and different generation-times of mutant and previous variants and furthermore fit the formulas to surveillance data from Austria. We conclude that the derived methods are well capable of estimating the excess infectiousness, even in early phases of the replacement process. Yet, a high ratio of imported cases from regions with higher variant prevalence may cause a major overestimation of the excess infectiousness, if not considered. Consequently, the analysis of Austrian data allowed a proper estimate for the Alpha variant, but results for the Delta variant are inconclusive.

Keywords: COVID-19; Differential Equation Modelling; Epidemiology; Mutants; Parameter Identification; SARS-CoV-2; SIR Model.