Assessing the ecological resilience of Ebola virus in Africa and potential influencing factors based on a synthesized model

Abstract

Background: The Ebola epidemic has persisted in Africa since it was firstly identified in 1976. However, few studies have focused on spatiotemporally assessing the ecological adaptability of this virus and the influence of multiple factors on outbreaks. This study quantitatively explores the ecological adaptability of Ebola virus and its response to different potential natural and anthropogenic factors from a spatiotemporal perspective.

Methodology: Based on historical Ebola cases and relevant environmental factors collected from 2014 to 2022 in Africa, the spatiotemporal distribution of Ebola adaptability is characterized by integrating four distinct ecological models into one synthesized spatiotemporal framework. Maxent and Generalized Additive Models were applied to further reveal the potential responses of the Ebola virus niche to its ever-changing environments.

Findings: Ebola habitats appear to aggregate across the sub-Saharan region and in north Zambia and Angola, covering approximately 16% of the African continent. Countries presently unaffected by Ebola but at increasing risk include Ethiopia, Tanzania, Côte d'Ivoire, Ghana, Cameroon, and Rwanda. In addition, among the thirteen key influencing factors, temperature seasonality and population density were identified as significantly influencing the ecological adaptability of Ebola. Specifically, those regions were prone to minimal seasonal variations in temperature. Both the potential anthropogenic influence and vegetation coverage demonstrate a rise-to-decline impact on the outbreaks of Ebola virus across Africa.

Conclusions: Our findings suggest new ways to effectively respond to potential Ebola outbreaks in Sub-Saharan Africa. We believe that this integrated modeling approach and response analysis provide a framework that can be extended to predict risk of other worldwide diseases from a similar epidemic study perspective.

Fig 1. Study area and spatial-temporal distribution of EVD in Africa since 2014 (A) Spatial distribution of EVD cases, and (B) Temporal variations of EVD cases and fatality ratio in different countries.

The base layers of the map were obtained from the openly available source via the Natural Earth (https://www.naturalearthdata.com/downloads/50m-cultural-vectors/).

Fig 2. Predicted ecologically suitable regions of Ebola virus based on Maxent, Bioclim, Domain, and GARP models with 13 independent, influential variables, i.e., bio3, bio4, bio6, bio7, bio11, bio12, bio14, NDVI, GPP, GPW, HII, LCCS, light.

The base layers of the map were obtained from the openly available source via the Natural Earth (https://www.naturalearthdata.com/downloads/50m-cultural-vectors/).

Fig 3. Potential distribution of Ebola outbreak occurrences based on (A) Maxent(B) Bioclim(C) Domain(D) GARP models plus ROC curves for (E) Maxent(F) Boclim(G) Domain, and (H) GARP.

The base layers of the map were obtained from the openly available source via the Natural Earth (https://www.naturalearthdata.com/downloads/50m-cultural-vectors/).

Fig 4. Map of potentially suitable habitats for Ebola virus in Africa and a closer look at four high-risk regions with (A) Integrated risk levels of Ebola based on results derived from four models(B) Western Africa region(C) Coastal regions of the Gulf of Guinea(D) Central Africa region, and (E) Eastern African region.

The base layers of the map were obtained from the openly available source via the Natural Earth (https://www.naturalearthdata.com/downloads/50m-cultural-vectors/).

Fig 5. Populations in Ebola risk areas within each country with (A) Spatial distribution of countries within the Ebola risk zone with reported Ebola outbreak (B) The spatial distribution of countries within the Ebola risk zone without any reported Ebola outbreaks, and (C) Histogram of populations at high risk of Ebola infection.

The base layers of the map were obtained from the openly available source via the Natural Earth (https://www.naturalearthdata.com/downloads/50m-cultural-vectors/).

Fig 6. Jackknife test of the importance of environment variables in Maxent, including Jackknife of regularized training gain, test gain and AUC.

Fig 7. Response curves of environmental factors and the probability of suitability for Ebola based on Maxent model.

Each curve (A-M) represents model-predicted probabilities associated with bio3, bio4, bio6, bio7, bio11, bio12, bio14, GPP, GPW, HII, LCCS, light, and NDVI. Curves display the mean response derived from 10 replicate Maxent runs (shown in red) with means and standard deviations (represented in blue, with two shades for categorical variables).

Fig 8. GAM model-based impact factors and ecological adaptability response of Ebola (A-M), final fitness classes with bio3, bio4, bio6, bio7, bio11, bio12, bio14, GPP, GPW, HII, LCCS, light, and NDVI.