Profiling of a network behind an infectious disease outbreak

TL;DR

This paper introduces a method to infer the network structure and transmission parameters of an infectious disease spread using early outbreak data, combining stochastic models and likelihood estimation.

Contribution

It presents a novel inverse modeling approach that uncovers the effective network topology and parameters from outbreak data, integrating stochastic differential equations and likelihood methods.

Findings

Successfully applied to synthesized datasets

Validated with WHO SARS outbreak data

Accurately identifies network topology and transmission parameters

Abstract

Stochasticity and spatial heterogeneity are of great interest recently in studying the spread of an infectious disease. The presented method solves an inverse problem to discover the effectively decisive topology of a heterogeneous network and reveal the transmission parameters which govern the stochastic spreads over the network from a dataset on an infectious disease outbreak in the early growth phase. Populations in a combination of epidemiological compartment models and a meta-population network model are described by stochastic differential equations. Probability density functions are derived from the equations and used for the maximal likelihood estimation of the topology and parameters. The method is tested with computationally synthesized datasets and the WHO dataset on SARS outbreak.