Epidemiological geographic profiling for a meta-population network

TL;DR

This paper introduces a statistical method for epidemiological geographic profiling to identify likely sources of disease outbreaks within a meta-population network, demonstrated on SARS data, highlighting Hong Kong as the probable origin.

Contribution

The paper develops new algorithms for source detection in meta-population networks using geographic profiling, applied to real-world SARS data to identify likely outbreak origins.

Findings

Hong Kong identified as the most probable source of SARS outbreak.

The method remains effective despite missing or unreliable data from China.

Hong Kong's influence as a spreader surpasses that of China in the network.

Abstract

Epidemiological geographic profiling is a statistical method for making inferences about likely areas of a source from the geographical distribution of patients. Epidemiological geographic profiling algorithms are developed to locate a source from the dataset on the number of new cases for a meta-population network model. It is found from the WHO dataset on the SARS outbreak that Hong Kong remains the most likely source throughout the period of observation. This reasoning is pertinent under the restricted circumstance that the number of reported probable cases in China was missing, unreliable, and incomprehensive. It may also imply that globally connected Hong Kong was more influential as a spreader than China. Singapore, Taiwan, Canada, and the United States follow Hong Kong in the likeliness ranking list.