Discovery of a missing disease spreader

TL;DR

This paper introduces a method to identify missing disease spreaders in a region using stochastic models and statistical analysis, demonstrated on SARS and flu data, to improve outbreak detection despite incomplete information.

Contribution

It develops a novel approach combining stochastic differential equations and statistical discriminators to detect hidden disease spreaders from case data.

Findings

Effective detection of missing spreaders in synthetic datasets

Successful application to SARS outbreak data

Identifies spreaders using moments of case distribution

Abstract

This study presents a method to discover an outbreak of an infectious disease in a region for which data are missing, but which is at work as a disease spreader. Node discovery for the spread of an infectious disease is defined as discriminating between the nodes which are neighboring to a missing disease spreader node, and the rest, given a dataset on the number of cases. The spread is described by stochastic differential equations. A perturbation theory quantifies the impact of the missing spreader on the moments of the number of cases. Statistical discriminators examine the mid-body or tail-ends of the probability density function, and search for the disturbance from the missing spreader. They are tested with computationally synthesized datasets, and applied to the SARS outbreak and flu pandemic.