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.

Additional 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 (3)

• Reaction-diffusion processes and metapopulation models in heterogeneous networks
• Thresholds for epidemic spreading in networks
• Information dynamics shape the networks of Internet-mediated prostitution

Cited by in corpus (7)

• Unification of theoretical approaches for epidemic spreading on complex networks
• Finding Patient Zero: Learning Contagion Source with Graph Neural Networks
• Matrix Product Belief Propagation for reweighted stochastic dynamics over graphs
• Targeted Recovery as an Effective Strategy against Epidemic Spreading
• Inference in conditioned dynamics through causality restoration
• The big bang of an epidemic
• Small-Coupling Dynamic Cavity: a Bayesian mean-field framework for epidemic inference