Interplay between evolutionary and epidemic time scales challenges the outcome of control policies

TL;DR

This paper extends the classical SIR epidemiological model to include viral evolution, revealing complex dynamics like superexponential growth and abrupt transitions, which challenge traditional control strategies.

Contribution

It introduces a minimal model extension allowing infectiousness to evolve, providing analytical insights into how evolution impacts epidemic outcomes and control effectiveness.

Findings

Viral evolution can cause superexponential outbreak growth.

Early lifting of control measures can worsen epidemic outcomes.

Reducing transmission slows viral evolution and decreases cases.

Abstract

The SIR model is the cornerstone model for mathematical epidemiology, explaining key epidemic features such as the second-order transition between disease-free and epidemic states, the initial exponential growth of outbreaks or the short-term benefits of control measures. Nonetheless, the classical SIR model assumes that pathogen traits remain fixed, thus neglecting viral evolution. Here we propose a minimal extension of the SIR model, allowing infectiousness to evolve. We show that such evolution can cause superexponential early growth of outbreaks, create abrupt epidemic transitions, and undermine the effectiveness of control policies, as lifting interventions too early can lead to worse epidemic scenarios than no action. We derive analytical expressions for the critical mutation rate and intervention time governing this behavior, and identify a strong asymmetry between control strategies: while shortening the infectious period hinders transmission without suppressing viral evolution, lowering transmission both reduces cases and slows down viral evolution.