![]() The majority of models attempting to predict the regional spread of EVD were limited to a single country 24, 25 and did not assess important characteristics such as the relative contribution of transmission from each district over time 26, 27, 28, 29. Such information, however incomplete, pose the question if re-occuring introductions have been the driver of the epidemic, a process observed for other diseases 23. 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. To our knowledge no transmission dynamical model has been fitted using mobility data from locations outside the region. No mobility estimates are available to investigate the spread of EVD in West Africa. These changes in human behaviour in part led to the spread of EVD that subsequently overwhelmed the countries’ poorly equipped health systems and revealed a lack of coordinated rapid response 20, 21. It has been hypothesised that the complex interplay between increased urbanisation over recent decades, and increased human mobility through porous borders in West Africa, contributed to the catastrophic nature of this outbreak 19. The rapid geographical expansion of the 2014–2016 epidemic stands in stark contrast to previous outbreaks of EVD 18. Phylogenetic analysis suggests that the outbreak caused by the Makona strain was triggered by a single cross-species transmission event from an animal reservoir near Meliandou, Guinea, with the subsequent outbreak sustained exclusively by human-to-human transmission 17. At the height of the outbreak in late 2014 the geographic extent of transmission was the widest ever recorded for Ebola virus, with cases reported in all districts in Sierra Leone (14/14) and Liberia (15/15) as well as in the majority of districts in Guinea (27/34) 16. The Ebola virus disease (EVD) epidemic in West Africa caused at least 28,000 infections and resulted in more than 11,000 deaths 16. Therefore we aim to test whether general human movement estimates can provide insightful predictions of disease invasion in resource-poor settings, including areas where mobility data are often unavailable. Hence, during an epidemic it is by no means certain that data on human movements in the outbreak location will be available in order to make predictions of disease spread 15. Previously, human mobility patterns have been inferred from a variety of sources, such as census surveys 10, mobile phone data (CDR) 11 or other mobile technologies 12, 13, but such data are often proprietary, expensive and time consuming to collect and process 14. Dispersal can vary seasonally 6 due to vacations 7, growing seasons 8, and religious events 9. The dispersal of a pathogen in space and time is limited structurally by the distribution and nature of transport infrastructure 3, which in turn are influenced by economic factors 4, 5. ![]() Whether the disease is transmitted in a location where an infectious person travels depends on the local characteristics such as population density and contact patterns, among others 2. The geographic spread of infectious pathogens may be driven by infected individuals travelling between areas of active transmission and disease-free areas 1. 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. Given their relative rareness, locally derived mobility data are unlikely to exist in advance of future epidemics or pandemics. We also demonstrate important differences between countries in population mixing and the improved prediction attributable to movement metrics. 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. A transmission model that includes a general model of human mobility significantly improves prediction of EVD’s incidence compared to models without this component. 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. 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. Detailed information about human movements during outbreaks are, however, difficult to obtain and may not be available during future epidemics. Human mobility is an important driver of geographic spread of infectious pathogens.
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