Identification of Patient Zero in Static and Temporal Networks - Robustness and Limitations

TL;DR

This paper investigates the limits and challenges of identifying the initial patient in epidemic spread within static and temporal networks, using analytic and simulation methods to assess detectability.

Contribution

It introduces a statistical inference framework for detecting patient-zero in arbitrary networks, analyzing its robustness and limitations with both theoretical and empirical data.

Findings

Detectability limits depend on spreading process characteristics.

Analytic calculations and Monte Carlo estimators effectively assess source detectability.

Application demonstrated on simulated STI spread over real temporal network.

Abstract

Detection of patient-zero can give new insights to the epidemiologists about the nature of first transmissions into a population. In this paper, we study the statistical inference problem of detecting the source of epidemics from a snapshot of spreading on an arbitrary network structure. By using exact analytic calculations and Monte Carlo estimators, we demonstrate the detectability limits for the SIR model, which primarily depend on the spreading process characteristics. Finally, we demonstrate the applicability of the approach in a case of a simulated sexually transmitted infection spreading over an empirical temporal network of sexual interactions.