Does social distancing matter for infectious disease propagation? A SEIR model and Gompertz law based cellular automaton

TL;DR

This study uses a stochastic cellular automaton based on the SEIR model and Gompertz law to analyze how social distancing impacts the spread of infectious diseases like COVID-19, emphasizing the importance of testing.

Contribution

It introduces a cellular automaton model incorporating real-world data and examines the effects of neighborhood radius and testing on disease propagation.

Findings

Larger neighborhood radius increases epidemic spread.

Isolation alone may not prevent disease transmission.

Aggressive testing is effective in controlling infection peaks.

Abstract

In this paper, we present stochastic synchronous cellular automaton defined on a square lattice. The automaton rules are based on the SEIR (susceptible $\to$ exposed $\to$ infected $\to$ recovered) model with probabilistic parameters gathered from real-world data on human mortality and the characteristics of the SARS-CoV-2 disease. With computer simulations, we show the influence of the radius of the neighborhood on the number of infected and deceased agents in the artificial population. The increase in the radius of the neighborhood favors the spread of the epidemic. However, for a large range of interactions of exposed agents (who neither have symptoms of the disease nor have been diagnosed by appropriate tests), even isolation of infected agents cannot prevent successful disease propagation. This supports aggressive testing against disease as one of the useful strategies to prevent large peaks of infection in the spread of SARS-CoV-2-like disease.