Epidemic oscillations induced by social network control: the discontinuous case

TL;DR

This paper investigates how social network control measures, especially discontinuous ones like lockdowns, induce epidemic oscillations and waves, using compartmentalized models on graphs with real-world COVID-19 data.

Contribution

It introduces a framework for understanding epidemic oscillations caused by discontinuous social network control and provides analytical formulas for epidemic wave characteristics.

Findings

Containment effects are modeled as a renormalization of infection rate.

Analytical expressions for epidemic wave number and duration are derived.

Application to COVID-19 data estimates social network disruption during lockdowns.

Abstract

Epidemic spreading can be suppressed by the introduction of containment measures such as social distancing and lock downs. Yet, when such measures are relaxed, new epidemic waves and infection cycles may occur. Here we explore this issue in compartmentalized epidemic models on graphs in presence of a feedback between the infection state of the population and the structure of its social network for the case of discontinuous control. We show that in random graphs the effect of containment measures is simply captured by a renormalization of the effective infection rate that accounts for the change in the branching ratio of the network. In our simple setting, a piece-wise mean-field approximations can be used to derive analytical formulae for the number of epidemic waves and their length. A variant of the model with imperfect information is used to model data of the recent covid-19 epidemics in the Basque Country and Lombardy, where we estimate the extent of social network disruption during lock downs and characterize the dynamical attractors.