Uncovering self-similar patterns in infectious disease transmission trees: Development of a Bayesian Connectivity Algorithm
Journal
Physica A: Statistical Mechanics and its Applications
Journal Volume
682
Start Page
131179
ISSN
0378-4371
Date Issued
2026-01
Author(s)
Wang, Cheng-Hua
Abstract
Epidemic dynamics are effectively modeled using tree structures that represent the transmission pathways of infectious diseases. Analyzing these transmission trees offers key insights into the mechanisms driving power-law growth, especially in the early stages of an outbreak. However, the degree of self-similarity within these trees remains insufficiently understood. This study introduces a Bayesian Connectivity Algorithm (BCA) designed to detect Conditional Infectious Connectivity Structures (CICSs) that exhibit self-similar properties. Simulation results demonstrate that the BCA accurately identifies self-similar patterns, except in cases of high homogeneity in transmission abilities or shallow tree generations. Empirical analysis of Canadian COVID-19 data further confirms the robustness of these self-similar structures, even in the presence of disruptions in transmission chains, aligning with observed power-law growth. In summary, the BCA detects self-similarity in early epidemic transmission stages by identifying CICSs within transmission trees, providing critical insights for forecasting epidemic trajectories and characterizing early transmission dynamics.
Subjects
Bayesian inference
Complex social systems
Early stage
Epidemiology
Power-law
Self-similarity
SDGs
Publisher
Elsevier BV
Type
journal article