The Gompertz Growth of COVID-19 Outbreaks is Caused by Super-Spreaders

TL;DR

This paper demonstrates that the Gompertz growth pattern in COVID-19 outbreaks arises from super-spreader nodes in scale-free social networks, highlighting key parameters for effective intervention strategies.

Contribution

It reveals that Gompertz growth is specific to scale-free networks and identifies critical network parameters influencing outbreak dynamics.

Findings

Gompertz growth occurs only in scale-free networks.

Super-spreaders with many contacts drive early rapid spread.

Interventions should target contact rate, infection probability, and recovery rate.

Abstract

In individual SARS-CoV-2 outbreaks, the count of confirmed cases and deaths follow a Gompertz growth function for locations of very different sizes. This lack of dependence on region size leads us to hypothesize that virus spread depends on universal properties of the network of social interactions. We test this hypothesis by simulating the propagation of a virus on networks of different topologies. Our main finding is that Gompertz growth observed for early outbreaks occurs only for a scale-free network, in which nodes with many more neighbors than average are common. These nodes that have very many neighbors are infected early in the outbreak and then spread the infection very rapidly. When these nodes are no longer infectious, the remaining nodes that have most neighbors take over and continue to spread the infection. In this way, the rate of spread is fastest at the very start and slows down immediately. Geometrically it is seen that the "surface" of the epidemic, the number of susceptible nodes in contact with the infected nodes, starts to rapidly decrease very early in the epidemic and as soon as the larger nodes have been infected. In our simulation, the speed and impact of an outbreak depend on three parameters: the average number of contacts each node makes, the probability of being infected by a neighbor, and the probability of recovery. Intelligent interventions to reduce the impact of future outbreaks need to focus on these critical parameters in order to minimize economic and social collateral damage.