  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.
