Phenotypic evolution of SARS-CoV-2: a statistical inference approach

Abstract

Since its emergence in late 2019, the SARS-CoV-2 virus has spread globally, causing the ongoing COVID-19 pandemic. In the fall of 2020, the Alpha variant (lineage B.1.1.7) was detected in England and spread rapidly, outcompeting the previous lineage. Yet, very little is known about the underlying modifications of the infection process that can explain this selective advantage. Here, we try to quantify how the Alpha variant differed from its predecessor on two phenotypic traits: the transmission rate and the duration of infectiousness. To this end, we analysed the joint epidemiological and evolutionary dynamics as a function of the Stringency Index, a measure of the amount of Non-Pharmaceutical Interventions. Assuming that these control measures reduce contact rates and transmission, we developed a two-step approach based on SEIR models and the analysis of a combination of epidemiological and evolutionary information. First, we quantify the link between the Stringency Index and the reduction in viral transmission. Secondly, based on a novel theoretical derivation of the selection gradient in an SEIR model, we infer the phenotype of the Alpha variant from its frequency changes. We show that its selective advantage is more likely to result from a higher transmission than from a longer infectious period. Our work illustrates how the analysis of the joint epidemiological and evolutionary dynamics of infectious diseases can help understand the phenotypic evolution driving pathogen adaptation.

Mutant emergence timing and population immunisation status impact epidemiological dynamics

“…On another note, Blanquart et al [2022], Park et al [2022], and Benhamou et al [2023] assumed a perfect immunity for infected individuals, but these works focused on the Alpha VOC earlier in the pandemic when there was less than 15% that had been infected [Hozé et al 2021]. Three years later, this assumption is not realistic any more.…”

“…It is possible to retrospectively explain the appearance of new VOCs if there is data available (e.g. Benhamou et al [2023]), although the analyses are rapidly quantitatively limited when immune escape is at play [Pearson et al 2021]. However, determining in advance what will be the phenotypic characteristics of a future circulating variant is less obvious and remains highly prospective.…”

Evolution of Virulence in Emerging Epidemics: From Theory to Experimental Evolution and Back

“…During the COVID-19 pandemic, for instance, the successive emergence and sweeps of SARS-CoV-2 variants of concern has raised many questions about the short and long-term evolution of the virus, especially in terms of transmission and virulence. Statistical inference based on demographic/epidemiological data (e.g., prevalence, deaths, hospital admissions) and genetic data (e.g., strain frequencies) quantified variants properties such as transmission, virulence, infectious period, immune escape or generation interval distribution -e.g., (Benhamou et al, 2023;Blanquart et al, 2022;Davies et al, 2021). In many scenarios, the frequency of a variant could be measured in different compartments, for example between naive and vaccinated hosts (e.g., for SARS-CoV-2 variants Omicron), or between infected hosts and an environmental compartment (e.g.…”

SARS-CoV-2 epidemiology, kinetics, and evolution: a narrative review

“…In general, and as summarised in the equation in Figure 7, assessing the origin of a transmissibility advantage is dicult as it can reside in dierent phenotypic traits. For example, dierences in reproduction number or generation interval can be dicult to disentangle [130,131]. Furthermore, the early estimation of this advantage requires a reactive genomic surveillance system or, at least, a routine key-mutation screening.…”