Endogenous viral mutations, evolutionary selection, and containment policy design

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.

COVID-19 Pandemisinin Kompartman Modelleri: Sistematik Bir Literatür Taraması

“…AI based detector helps to reduce the mistaken alarm [58]. Other methods that are integrated with the compartmental modelling approach in the COVID-19 literature can be listed as follows: agent-based modelling [74], big data analytics [75], fuzzy set algorithm [76], Markov process [77], and system dynamics approaches [78]. Decision support systems were also developed for clinical decisions, allocation of resources, lock-down management, and optimization of response strategies [79].…”

Simulating the Evolutionary Response of a Viral Pandemic to Behaviour Change

“…Further, the potential impact of within-host evolution has been largely ignored, though SARS-CoV-2 has shown significant withinhost diversity (Tonkin-Hill et al, 2021;Lythgoe et al, 2021). Zhang et al (2022) demonstrated an abstract model of intra-and inter-host evolution in relation to SARS-CoV-2, but individual behaviours were not simulated; Mellacher (2022) presents a more detailed evolutionary model, but again individual behavioural responses are not represented.…”

V for vaccines and variants

“… 1 Mellacher ( 2022 ) uses a detailed epidemiological agent-based model to study the emergence and diffusion of mutations. The macroeconomic impact of these, however, is not explored.…”

“… 11 We model the emergence of a variant as an exogenous shock to the epidemiological scenario with vaccination. In more sophisticated epidemiological agent-based models such as Mellacher ( 2022 ), the emergence of variants is endogenous. While the endogenous emergence of variants is undoubtedly an appealing feature of a macro-epidemiological model, the exogeneity assumption is justified in our context because of the focus on a single region, Lombardy, within the context of a worldwide pandemic (indeed, all relevant variants of the SARS-COV2 virus discovered to date are held to have originated outside of Lombardy).…”