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Ebola Reservoir Peer Review

https://www.biorxiv.org/content/10.1101/2025.10.17.683141v1

Evidence of latency reshapes our understanding of Ebola virus reservoir dynamics

This paper, authored by a diverse set of scholars from around the world (Boston, Seattle, LA, Edinburgh, Kinshasa, and more) explores how the Ebola virus persists between the many deadly human outbreaks in the past 50+ years. The hemorrhagic fever that is associated with Ebola is extremely dangerous, and Infectious Disease experts are always using a statistical models, ecological factors, and surveillance data to try to predict when the next zoonotic spillover could occur and lead to another Ebola epidemic. This paper expands on prior research on the relatively similar genome of the 2021 Guinea outbreak with the 1976 Yambuku one by validating those few genetic changes with 19 other outbreaks and coming up with a new statistical model to better predict outbreaks. After compiling the phylogenetic data, the researchers determined that with only 96 mutations between the initial genomes (first patients) between the Sept 2025 outbreak and 1976 outbreaks in the Democratic Republic of Congo, the virus must exist in a dormant (non-replicating) state in reservoirs between outbreaks. This latency model, replacing the old continuous replication model, has significant implications in understanding the source and geographic distribution of EBOV outbreaks, as well as identifying human/animal reservoirs. Overall, while the methods mathematical discourse is dense, this is a well-written and thorough paper. There are a few items of consideration as listed below.

Major Strengths:

·      Genomic sequences collected from 19 independent Ebola outbreaks over the course of 50 years – data is representative and relevant, enabling the development of a robust model and phylogenetic inference.

·      Great data visualization through the root-to-tip regression figures and fleshed-out applications section

·      Usage of sophisticated Bayesian statistics and the BEAST X model to innovate upon prior conceptions and present the latency model

Major Weakness:

·       Dependence on genome sequences from human spillovers only. Though the data was representative, the lack of genome sequences directly from a reservoir site (no recorded living sample) makes the paper’s claim based solely on theory/statistics.

·       Better expansion on/explanation of possible reservoir hosts. The model cannot explain what animals/tissues (potential persistence in human tissue) can host the dormant Ebola virus, and this topic could have been more thoroughly explored for important implications.

·       The paper included some uncertainty regarding the root placement – authors said it was backed by their statistical analyses, but there are alternate possibilities as well. Including these alternate phylogenetic possibilities, along with explanations against them, could have strengthened the credibility of the trees presented.

              By using recent epidemiological data and establishing a strong and testable predictive model, I think it is highly important for this paper to be published to enable further research into these latent animal reservoirs and help infectious disease experts better control Ebola outbreaks.

Competing interests

The authors declare that they have no competing interests.

Use of Artificial Intelligence (AI)

The authors declare that they used generative AI to come up with new ideas for their review.