Utilizing general human movement models to predict the spread of emerging infectious diseases in resource poor settings

M U G Kraemer,D A T Cummings,J S Brownstein,D L Smith,O J Brady,S E Ray,S I Hay,O G Pybus,A J Tatem,N R Faria,D M Pigott,D Bisanzio,R C Reiner,S Bhatt,N Golding

doi:10.1038/s41598-019-41192-3

Abstract

Human mobility is an important driver of geographic spread of infectious pathogens. Detailed information about human movements during outbreaks are, however, difficult to obtain and may not be available during future epidemics. The Ebola virus disease (EVD) outbreak in West Africa between 2014–16 demonstrated how quickly pathogens can spread to large urban centers following one cross-species transmission event. Here we describe a flexible transmission model to test the utility of generalised human movement models in estimating EVD cases and spatial spread over the course of the outbreak. A transmission model that includes a general model of human mobility significantly improves prediction of EVD’s incidence compared to models without this component. Human movement plays an important role not only to ignite the epidemic in locations previously disease free, but over the course of the entire epidemic. We also demonstrate important differences between countries in population mixing and the improved prediction attributable to movement metrics. Given their relative rareness, locally derived mobility data are unlikely to exist in advance of future epidemics or pandemics. Our findings show that transmission patterns derived from general human movement models can improve forecasts of spatio-temporal transmission patterns in places where local mobility data is unavailable.

Highlights

Www.nature.com/scientificreports can provide insightful predictions of disease invasion in resource-poor settings, including areas where mobility data are often unavailable
The Ebola virus disease (EVD) epidemic in West Africa caused at least 28,000 infections and resulted in more than 11,000 deaths[16]
We combined generalised human movement models with parameters inferred from open access mobile phone data with a flexible transmission model, to test whether the inclusion of mobility fluxes increased the predictive power of EVD cases in West Africa

Summary

Introduction

Www.nature.com/scientificreports can provide insightful predictions of disease invasion in resource-poor settings, including areas where mobility data are often unavailable. Detailed investigations of chains of transmission in Guinea have shown that continued unmonitored re-introductions into large urban centres, and subsequent inter-urban transmission events, led to the extensive geographical spread of the virus[22] Such information, incomplete, pose the question if re-occuring introductions have been the driver of the epidemic, a process observed for other diseases[23]. Phylogenetic studies of EVD in Sierra Leone and Liberia indicate that despite inter-country spread during the early phase (December 2013 to mid-March 2014) of the outbreak, most virus transmission occurred locally during the contracting phase of the outbreak and within national borders[17,33,34,35] Some of these changes may be explained by unofficial border closings, curfews, and restrictions on funeral gatherings[36]. Our software can be rapidly updated, applied to other pathogens, and is flexible enough to be tailored to baseline analyses and predictive mapping of future infectious disease outbreaks

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