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

Shen, Li and Song, Jiawei and Zhou, Yibo and Yuan, Xiaojie and Seery, Samuel and Fu, Ting and Liu, Xihao and Liu, Yihong and Shao, Zhongjun and Li, Rui and Liu, Kun (2025) Assessing the ecological resilience of Ebola virus in Africa and potential influencing factors based on a synthesized model. PLoS Neglected Tropical Diseases, 19 (2): e0012843. ISSN 1935-2727

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.