Social distancing and epidemic resurgence in agent-based Susceptible-Infectious-Recovered models

TL;DR

This study uses agent-based SIR models on various network structures to analyze how delaying social distancing relaxation and limiting long-distance contacts can prevent epidemic resurgence.

Contribution

It demonstrates through simulations that delaying the lifting of restrictions and maintaining low levels of long-distance connections effectively control epidemic resurgence.

Findings

Delaying restriction release by at least three times the outbreak peak reduces resurgence.

Limiting long-distance contacts to under five percent of total connectivity helps prevent epidemic waves.

Spatial and network structure significantly influence epidemic recurrence dynamics.

Abstract

Once an epidemic outbreak has been effectively contained through non-pharmaceutical interventions, a safe protocol is required for the subsequent release of social distancing restrictions to prevent a disastrous resurgence of the infection. We report individual-based numerical simulations of stochastic susceptible-infectious-recovered model variants on four distinct spatially organized lattice and network architectures wherein contact and mobility constraints are implemented. We robustly find that the intensity and spatial spread of the epidemic recurrence wave can be limited to a manageable extent provided release of these restrictions is delayed sufficiently (for a duration of at least thrice the time until the peak of the unmitigated outbreak) and long-distance connections are maintained on a low level (limited to less than five percent of the overall connectivity).