Identifying spatial invasion of pandemics on metapopulation networks via anatomizing arrival history

TL;DR

This paper introduces a novel method to identify the stochastic spatial spread of pandemics in metapopulation networks using arrival history data, employing an optimization algorithm and entropy-based measures.

Contribution

It presents a new reverse problem formulation and an efficient dynamical programming approach to infer invasion pathways without extensive parameter calibration.

Findings

Robust identification of actual invasion pathways in artificial networks

Effective application to empirical metapopulation data

High accuracy in reconstructing pandemic spread processes

Abstract

Spatial spread of infectious diseases among populations via the mobility of humans is highly stochastic and heterogeneous. Accurate forecast/mining of the spread process is often hard to be achieved by using statistical or mechanical models. Here we propose a new reverse problem, which aims to identify the stochastically spatial spread process itself from observable information regarding the arrival history of infectious cases in each subpopulation. We solved the problem by developing an efficient optimization algorithm based on dynamical programming, which comprises three procedures: i, anatomizing the whole spread process among all subpopulations into disjoint componential patches; ii, inferring the most probable invasion pathways underlying each patch via maximum likelihood estimation; iii, recovering the whole process by assembling the invasion pathways in each patch iteratively, without burdens in parameter calibrations and computer simulations. Based on the entropy theory, we introduced an identifiability measure to assess the difficulty level that an invasion pathway can be identified. Results on both artificial and empirical metapopulation networks show the robust performance in identifying actual invasion pathways driving pandemic spread.