An objective criterion for cluster detection in stochastic epidemic models

TL;DR

This paper introduces an automatic, model-independent method for detecting the number and internal structure of clusters in epidemic and other phenomena, based on changes in log-likelihood.

Contribution

It develops a novel criterion using log-likelihood differences to identify clusters and their count, applicable across different models without prior assumptions.

Findings

Successfully validated on epidemic models with minimal temporal structure.

Effectively applied to earthquake clustering data.

Provides a model-independent approach for cluster detection.

Abstract

The correct identification of clusters is crucial for an accurate monitoring of the spread of a disease and also in many other natural, social and physical phenomena which exhibit an epidemic structure. Nevertheless, even when an accurate mathematical model is available, no simple tool exists which allows one to identify how many independent clusters are present and to link elements to the appropriate clusters. Here we develop an automatic method for the detection of the internal structure of the clusters and their number, independently of the model that describes the dynamics of the phenomenon. It is substantially based on the difference of the log-likelihood $\delta {\cal LL}$, that is evaluated when all elements are connected and when they are grouped into clusters. As a function of the number of connected elements $\delta {\cal LL}$ presents a change of slope and a singularity which can be both used in cluster identification. Our method is validated for an epidemic model with a minimal temporal structure and for the Epidemic Type Aftershock Sequence model describing the spatio-temporal clustering of earthquakes.