Sensor-based localization of epidemic sources on human mobility networks

TL;DR

This paper presents a Bayesian, sensor-based method for localizing epidemic sources on human mobility networks, using first-arrival times and a Gaussian mixture model, demonstrated on a cholera outbreak in South Africa.

Contribution

It introduces a novel source detection approach combining mobility data, Bayesian inference, and Gaussian mixture modeling for waterborne diseases.

Findings

Effective localization with limited sensors

Incorporation of prior local condition information

Successful application to real cholera outbreak data

Abstract

We investigate the source detection problem in epidemiology, which is one of the most important issues for control of epidemics. Mathematically, we reformulate the problem as one of identifying the relevant component in a multivariate Gaussian mixture model. Focusing on the study of cholera and diseases with similar modes of transmission, we calibrate the parameters of our mixture model using human mobility networks within a stochastic, spatially explicit epidemiological model for waterborne disease. Furthermore, we adopt a Bayesian perspective, so that prior information on source location can be incorporated (e.g., reflecting the impact of local conditions). Posterior-based inference is performed, which permits estimates in the form of either individual locations or regions. Importantly, our estimator only requires first-arrival times of the epidemic by putative observers, typically located only at a small proportion of nodes. The proposed method is demonstrated within the context of the 2000-2002 cholera outbreak in the KwaZulu-Natal province of South Africa.