Abstract
How will the novel coronavirus evolve? I study a simple epidemiological model, in which mutations may change the properties of the virus and its associated disease stochastically and antigenic drifts allow new variants to partially evade immunity. I show analytically that variants with higher infectiousness, longer disease duration, and shorter latent period prove to be fitter. “Smart” containment policies targeting symptomatic individuals may redirect the evolution of the virus, as they give an edge to variants with a longer incubation period and a higher share of asymptomatic infections. Reduced mortality, on the other hand, does not per se prove to be an evolutionary advantage. I then implement this model as an agent-based simulation model in order to explore its aggregate dynamics. Monte Carlo simulations show that a) containment policy design has an impact on both speed and direction of viral evolution, b) the virus may circulate in the population indefinitely, provided that containment efforts are too relaxed and the propensity of the virus to escape immunity is high enough, and crucially c) that it may not be possible to distinguish between a slowly and a rapidly evolving virus by looking only at short-term epidemiological outcomes. Thus, what looks like a successful mitigation strategy in the short run, may prove to have devastating long-run effects. These results suggest that optimal containment policy must take the propensity of the virus to mutate and escape immunity into account, strengthening the case for genetic and antigenic surveillance even in the early stages of an epidemic.
Highlights
In the autumn of 2021 it has become clear, that even wide-spread access to vaccines has not eliminated the threat of COVID-19, as new variants prove to be fitter and better able to escape immunity (Bernal et al 2021; Hoffmann et al 2021a, 2021b; Wall et al 2021)
I aim to shed light on two research questions which naturally arise in this situation: a) in which direction will the SARS-CoV2 virus causing COVID-19 evolve?, and b) which aggregate dynamics can we expect from an evolving virus in terms of infections and fatalities? In order to study these topics, i develop a parsimonious epidemiological model that simultaneously captures two types of viral evolution: a) Genetic variation: Mutations can change the characteristics of a virus in a way that alter its evolutionary fitness, e.g., by increasing its transmissibility (Chen et al 2020)
I give an overview of viral evolution under varying mutation parameters, and show how they are affected by social distancing
Summary
In the autumn of 2021 it has become clear, that even wide-spread access to vaccines has not (yet) eliminated the threat of COVID-19, as new variants prove to be fitter and better able to escape immunity (Bernal et al 2021; Hoffmann et al 2021a, 2021b; Wall et al 2021). Research on the influenza virus shows that genetic change is more gradual than antigenic evolution and that immune escape depends on the antigenic distance between two variants (Smith et al 2004) Both types of variation are subject to natural selection, which favors “useful” (Darwin 1859) variation, i.e., variation which increases the growth rate of the number of people infected by a variant. This paper is related to a literature to the study of evolutionary processes in economics as pioneered by Nelson and Winter (1982) This approach has been adopted in a fruitful way using agent-based models in the field of innovation economics (e.g., Ma and Nakamori, 2005; Dosi et al 2010), which inter alia considers the case of directed change due to evolutionary selection (Fanti, 2021; Hötte 2020; Mellacher and Scheuer 2021).
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