Viral epidemics in a cell culture: novel high resolution data and their interpretation by a percolation theory based model

TL;DR

This study provides high-resolution data on viral spread in a static cell culture system and interprets it using a percolation theory model, revealing insights into epidemic dynamics in immobile populations.

Contribution

First detailed quantitative analysis of viral epidemics in a highly reproducible in vitro system of immobile cells using a percolation theory framework.

Findings

Viral spread follows percolation-like behavior.

Infected clusters exhibit fractal dimensions consistent with percolation theory.

The model reproduces key features of the observed epidemic dynamics.

Abstract

Because of its relevance to everyday life, the spreading of viral infections has been of central interest in a variety of scientific communities involved in fighting, preventing and theoretically interpreting epidemic processes. Recent large scale observations have resulted in major discoveries concerning the overall features of the spreading process in systems with highly mobile susceptible units, but virtually no data are available about observations of infection spreading for a very large number of immobile units. Here we present the first detailed quantitative documentation of percolation-type viral epidemics in a highly reproducible in vitro system consisting of tens of thousands of virtually motionless cells. We use a confluent astroglial monolayer in a Petri dish and induce productive infection in a limited number of cells with a genetically modified herpesvirus strain. This approach allows extreme high resolution tracking of the spatio-temporal development of the epidemic. We show that a simple model is capable of reproducing the basic features of our observations, i.e., the observed behaviour is likely to be applicable to many different kinds of systems. Statistical physics inspired approaches to our data, such as fractal dimension of the infected clusters as well as their size distribution, seem to fit into a percolation theory based interpretation. We suggest that our observations may be used to model epidemics in more complex systems, which are difficult to study in isolation.