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

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.

arXiv commentAdditional experiments and results regarding the detectability limits are included in v2. Supplemental material is added in the Ancillary files section on this arXiv page

References in corpus (10)

• Epidemic spreading in scale-free networks
• Reaction-diffusion processes and metapopulation models in heterogeneous networks
• Thresholds for epidemic spreading in networks
• On the use of human mobility proxy for the modeling of epidemics
• Locating the Source of Diffusion in Large-Scale Networks
• Inferring the origin of an epidemic with a dynamic message-passing algorithm
• Information dynamics shape the networks of Internet-mediated prostitution
• Bayesian inference of epidemics on networks via Belief Propagation
• Identifying the starting point of a spreading process in complex networks
• FastSIR Algorithm: A Fast Algorithm for simulation of epidemic spread in large networks by using SIR compartment model

Cited by in corpus (23)

• Modern temporal network theory: A colloquium
• Unification of theoretical approaches for epidemic spreading on complex networks
• Generalized Network Dismantling
• Locating the source of diffusion in complex networks by time-reversal backward spreading
• Dynamics of epidemic spreading with vaccination: impact of social pressure and engagement
• Sudden transitions in coupled opinion and epidemic dynamics with vaccination
• Individual-based approach to epidemic processes on arbitrary dynamic contact networks
• Neural Ordinary Differential Equation Control of Dynamics on Graphs
• Finding Patient Zero: Learning Contagion Source with Graph Neural Networks
• A Bayesian generative neural network framework for epidemic inference problems
• Simulating SIR processes on networks using weighted shortest paths
• Schemes of Propagation Models and Source Estimators for Rumor Source Detection in Online Social Networks: A Short Survey of a Decade of Research
• Unifying continuous, discrete, and hybrid susceptible-infected-recovered processes on networks
• Matrix Product Belief Propagation for reweighted stochastic dynamics over graphs
• Sensor-based localization of epidemic sources on human mobility networks
• Targeted Recovery as an Effective Strategy against Epidemic Spreading
• Inference in conditioned dynamics through causality restoration
• Predicting epidemic evolution on contact networks from partial observations
• Temporal Dynamics of Connectivity and Epidemic Properties of Growing Networks
• The big bang of an epidemic
• Small-Coupling Dynamic Cavity: a Bayesian mean-field framework for epidemic inference
• Morphological Inversion of Complex Diffusion
• Social Diffusion Sources Can Escape Detection