Since the index case in 2013, the West African Ebola epidemic has killed more than 11,000 people (World Heath Organization, 2016) and exposed national and international inadequacies in pandemic preparedness and response (Moon et al., 2015). In 2014 a zoonotic niche map for Ebola virus disease (EVD) was produced (Pigott et al., 2014) in part to assess the expected geographical extent of spillover risk. This research was then expanded to explore how changes in demography and international connectivity may have facilitated the establishment and rapid subsequent spread of the epidemic (Bogoch et al., 2015). The West African outbreak of EVD has again highlighted key information gaps that exist with respect to the broader epidemiology of Ebola virus, particularly concerning viral persistence in reservoirs (Funk and Piot, 2014; Mari Saez et al., 2015; Leendertz, 2016), and prompted a variety of questions concerning the role bats play in transmission (Leendertz et al., 2016). Identifying reservoirs of zoonotic disease is a complex process (Viana et al., 2014; Haydon et al., 2002) and whilst considerable sampling effort has been undertaken over the years (Kuhn, 2008; Leirs et al., 1999), isolation of Ebolavirus from living animals has been rare (Leroy et al., 2005). The original eLife study only incorporated the three bat species found to be RNA-positive (Leroy et al., 2005). Whilst this remains currently the best evidence for an animal reservoir species, it is important to consider that other sampling efforts may by chance represent false negatives, particularly if infection is rare.

Consequently, to contribute to these broader discussions, the original paper (Pigott et al., 2014) was updated with new occurrence data and expanded to consider a wider range of potential bat reservoir species. Bats remained the priority mammalian order given the previous viral isolation and the repeated anecdotal implications in previous outbreaks (Leroy et al., 2009; Mari Saez et al., 2015). Since there are a large number of bat species found in Africa, we defined three groupings, based upon the strength of evidence supporting their potential Ebola reservoir status. As a result, not only were the original three RNA-positive bats included (Leroy et al., 2005), but also those species with serological evidence of EVD infection (Olival and Hayman, 2014) and those identified through trait-based machine learning approaches as being similar to species already reporting filoviral infection (Han et al., 2016).

Six additional records of EVD were incorporated into the disease occurrence database: one human outbreak in the Democratic Republic of Congo (Maganga et al., 2014); two reports of infections in animals in Zambia (Ogawa et al., 2015); and three animal infections in Central African Republic(Morvan et al., 1999) (Figure 1). Of these new occurrences, two in southern Central African Republic are found in areas predicted to be at-risk by the previous model (Pigott et al., 2014), with the index case from the Democratic Republic of Congo located in close proximity (<10 km) to at-risk areas. The occurrences in Zambia and northern Central African Republic lie, respectively, to the south and north of previously predicted at-risk regions.

Figure 1

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Figure 2 depicts the three new consolidated bat distributions. The revised distribution of the Group 1 bats (i.e. those found to have been Ebolavirus RNA positive) is broadly consistent with that published in the original paper except that the peripheries of Central Africa are now predicted to be environmentally suitable for these bats, as well as some parts of East Africa, particularly Tanzania, Mozambique and Madagascar. The Group 2 and Group 3 bat species are predicted to be distributed across much of Africa stretching from West to East Sub-Saharan Africa, as well as much of the coastline of the continent.

Figure 2 with 3 supplements see all

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The revised niche map, incorporating the updated bat covariates and disease occurrence database, is presented in Figure 3. The map shows the predicted areas of environmental suitability for zoonotic Ebola virus transmission to be consistent with previous attempts, but the relative environmental suitability within this distribution differs from the previous estimates. Figure 3—figure supplement 1 demonstrates that Cameroon, Gabon, Republic of Congo and mainland Equatorial Guinea are now predicted to be more environmentally suitable than in the previous analysis. The regions of Central Africa (particularly Gabon and the Republic of Congo) identified as being most environmentally suitable for zoonotic EVD transmission in the previous analysis remain so in this analysis. The revised number of predicted at-risk countries, determined by thresholding the map by a probability that captures 95% of the occurrence dataset, is 23 (Table 1).

Figure 3 with 2 supplements see all

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The similar AUC values (0.85 ± 0.04 compared to 0.8236 ± 0.080) between the previous and current iterations suggest that the updated model fits the new occurrence dataset as well as the previous model fitted the older dataset. Mean enhanced vegetation index (EVI) remains the highest relative predictor covariate for zoonotic EVD transmission while the relative importance of Group 1 bat distributions moved from being the fifth most important to the second. Mean night-time land surface temperature (LST), elevation and mean daytime LST complete the top five predictors (Table 2).

When separate bat layers were used in the model, as opposed to the consolidated covariates, the predictions were geographically similar (Figure 3—figure supplement 2) however, four bat species were identified as explaining more of the variation than the rest; Hypsignathus monstrosus, Epomops franqueti (from both of which Ebolavirus RNA has been isolated), Otomops martiensseni and Epomophorus labiatus (both from Group 3). The explanatory power of this model (evaluated using AUC) was comparable to the model results described above (AUC = 0.819 ± 0.080).

This research advance integrates new data as well as a more thorough consideration of the bat species that act as a reservoir for the virus in order to update our modelled estimate of the zoonotic niche of EVD. The area estimated to be at potential risk of zoonotic EVD transmission has now expanded to include Kenya and the influence of additional bat species demonstrates that continued focus should be placed on rigorously identifying reservoir species and the role they play in sustaining viral transmission (Leendertz, 2016). The fact that O. martiensseni and E. labiatus contribute explanatory power to the model, in comparison with their distributions on the eastern and southern periphery of reported cases of EVD (Figure 2—figure supplement 3) suggests that different regions of the continent may support transmission cycles with differing reservoir species. This, coupled with the potential for each of the pathogenic species of Ebolavirus potentially having differing distributions (Peterson et al., 2004), cannot currently be explored more rigorously due to insufficient data.

As with the original publication, it must be reiterated that environmental suitability does not inevitably lead to spillover events. Currently absolute human population residing in at-risk pixels is used as a proxy for spillover likelihood, however, a variety of factors will influence the outbreak risk within each location (Plowright et al., 2015) and only by including covariates that consider human behaviour (Woldehanna and Zimicki, 2015), patterns of susceptibility in other animals (Walsh et al., 2007; 2009), impacts of land use change (Rosales-Chilama et al., 2015) and within-host viral dynamics (Amman et al., 2012; Hayman, 2015) can an approximation of spillover risk be defined.

These updates demonstrate the ease and speed with which new data and covariate considerations can be incorporated within existing empirical models (Kraemer et al., 2016). As the wider discussion on EVD turns to focus on strategies to prevent or contain future spillover events as well as developing long-term in-country containment capacities (Currie et al., 2016), it is hoped that maps such as these convey the heterogeneities in spillover risk that exist within Africa. To better enable researchers and policymakers to consider EVD preparedness and necessary contingencies, a new online tool has been developed which allows users to interrogate the revised maps in more detail, in areas of specific interest (http://vizhub.healthdata.org/ebola). As part of this tool, the zoonotic niche output and Group 1 bat layers are now available, along with filters for identifying at-risk countries and locations of previous index cases from outbreaks.

Geographic datasets such as these provide context to broader discussions as our aspirations transition from controlling outbreaks to mitigating the risk of future spillover events prioritised by their potential for more widespread epidemics. Such data are particularly important for determining where best to investigate the frequency of potentially transmissible contacts between reservoir and susceptible species and humans. Previous niche maps served as an important impetus in the search for potential reservoirs (Peterson et al., 2004) and these iterations can continue to inform such work. As researchers and policy makers seek to resolve outstanding questions about EVD epidemiology, it is hoped that the continued updating and dissemination of this information can contribute to this discussion.

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Author details

1. David M Pigott

   1. Institute for Health Metrics and Evaluation, University of Washington, Seattle, United States
   2. Oxford Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, Oxford, United Kingdom

   Contribution

   DMP, Conception and design, Acquisition of data, Analysis and interpretation of data, Drafting or revising the article

   For correspondence

   pigottdm@uw.edu

   Competing interests

   No competing interests declared.

2. Anoushka I Millear

   Institute for Health Metrics and Evaluation, University of Washington, Seattle, United States

   Contribution

   AIM, Acquisition of data, Analysis and interpretation of data, Drafting or revising the article

   Competing interests

   No competing interests declared.

3. Lucas Earl

   Institute for Health Metrics and Evaluation, University of Washington, Seattle, United States

   Contribution

   LE, Analysis and interpretation of data, Drafting or revising the article

   Competing interests

   No competing interests declared.

4. Chloe Morozoff

   Institute for Health Metrics and Evaluation, University of Washington, Seattle, United States

   Contribution

   CM, Analysis and interpretation of data, Drafting or revising the article

   For correspondence

   chloemor@uw.edu

   Competing interests

   No competing interests declared.

   "This ORCID iD identifies the author of this article:" 0000-0002-3254-5553

5. Barbara A Han

   Cary Institute of Ecosystem Studies, New York, United States

   Contribution

   BAH, Acquisition of data, Analysis and interpretation of data, Drafting or revising the article

   Competing interests

   No competing interests declared.

6. Freya M Shearer

   Oxford Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, Oxford, United Kingdom

   Contribution

   FMS, Analysis and interpretation of data, Drafting or revising the article

   Competing interests

   No competing interests declared.

7. Daniel J Weiss

   1. Spatial Ecology and Epidemiology Group, University of Oxford, Oxford, United Kingdom
   2. Department of Zoology, University of Oxford, Oxford, United Kingdom

   Contribution

   DJW, Acquisition of data, Drafting or revising the article

   Competing interests

   No competing interests declared.

8. Oliver J Brady

   Oxford Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, Oxford, United Kingdom

   Contribution

   OJB, Analysis and interpretation of data, Drafting or revising the article

   Competing interests

   No competing interests declared.

9. Moritz UG Kraemer

   1. Spatial Ecology and Epidemiology Group, University of Oxford, Oxford, United Kingdom
   2. Department of Zoology, University of Oxford, Oxford, United Kingdom

   Contribution

   MUGK, Analysis and interpretation of data, Drafting or revising the article

   Competing interests

   No competing interests declared.

   "This ORCID iD identifies the author of this article:" 0000-0001-8838-7147

10. Catherine L Moyes

    Oxford Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, Oxford, United Kingdom

    Contribution

    CLM, Conception and design, Analysis and interpretation of data, Drafting or revising the article

    Competing interests

    No competing interests declared.

    "This ORCID iD identifies the author of this article:" 0000-0002-8028-4079

11. Samir Bhatt

    1. Spatial Ecology and Epidemiology Group, University of Oxford, Oxford, United Kingdom
    2. Department of Zoology, University of Oxford, Oxford, United Kingdom

    Contribution

    SB, Conception and design, Drafting or revising the article

    Competing interests

    No competing interests declared.

12. Peter W Gething

    1. Spatial Ecology and Epidemiology Group, University of Oxford, Oxford, United Kingdom
    2. Department of Zoology, University of Oxford, Oxford, United Kingdom

    Contribution

    PWG, Conception and design, Drafting or revising the article

    Competing interests

    No competing interests declared.

13. Nick Golding

    1. Oxford Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, Oxford, United Kingdom
    2. Department of BioSciences, University of Melbourne, Parkville, Australia

    Contribution

    NG, Conception and design, Analysis and interpretation of data, Drafting or revising the article

    Competing interests

    No competing interests declared.

14. Simon I Hay

    1. Institute for Health Metrics and Evaluation, University of Washington, Seattle, United States
    2. Oxford Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, Oxford, United Kingdom

    Contribution

    SIH, Conception and design, Acquisition of data, Analysis and interpretation of data, Drafting or revising the article

    Competing interests

    SIH: Reviewing editor, eLife

    "This ORCID iD identifies the author of this article:" 0000-0002-0611-7272

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