Finite-time scaling for epidemic processes with power-law superspreading events

TL;DR

This paper develops a finite-time scaling law for epidemic outbreaks with power-law superspreading events, revealing how infinite-variance superspreading influences epidemic phase transitions and outbreak survival probabilities.

Contribution

It introduces a universal finite-time scaling law for epidemics with power-law superspreading, highlighting the impact of infinite variance on epidemic phase transitions.

Findings

Survival probability follows a finite-time scaling law dependent on power-law exponent.

Phase transition between subcritical and supercritical states emerges in the infinite-time limit.

Infinite-variance superspreading introduces new epidemic phenomenology.

Abstract

Epidemics unfold by means of a spreading process from each infected individual to a random number of secondary cases. It has been claimed that the so-called superspreading events in COVID-19 are governed by a power-law tailed distribution of secondary cases, with no finite variance. Using a continuous-time branching process, we show that for such power-law superspreading the survival probability of an outbreak as a function of time and the basic reproductive number fulfills a "finite-time scaling" law (analogous to finite-size scaling) with universal-like characteristics only dependent on the power-law exponent. This clearly shows how the phase transition separating a subcritical and a supercritical phase emerges in the infinite-time limit (analogous to the thermodynamic limit). We quantify the counterintuitive hazards infinite-variance superspreading poses and conclude that superspreading only leads to new phenomenology in the infinite-variance case.