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Record W1978700210 · doi:10.1371/journal.pntd.0003056

Outbreak of Ebola Virus Disease in Guinea: Where Ecology Meets Economy

2014· editorial· en· W1978700210 on OpenAlexaff
Daniel G. Bausch, Lara Schwarz

Bibliographic record

VenuePLoS neglected tropical diseases · 2014
Typeeditorial
Languageen
FieldMedicine
TopicViral Infections and Outbreaks Research
Canadian institutionsMcGill University
Fundersnot available
KeywordsOutbreakEbola virusVirologyEcologyDiseaseEbolavirusEmerging infectious diseaseNew guineaGeographyBiologyMedicineEthnologyHistoryPathology

Abstract

fetched live from OpenAlex

Ebola virus is back, this time in West Africa, with over 350 cases and a 69% case fatality ratio at the time of this writing [1]. The culprit is the Zaire ebolavirus species, the most lethal Ebola virus known, with case fatality ratios up to 90%. The epicenter and site of first introduction is the region of Gueckedou in Guinea’s remote southeastern forest region, spilling over into various other regions of Guinea as well as to neighboring Liberia and Sierra Leone (Figure 1). News of this outbreak engenders three basic questions: (1) What in the world is Zaire ebolavirus doing in West Africa, far from its usual haunts in Central Africa? (2) Why Guinea, where no Ebola virus has ever been seen before? (3) Why now? We’ll have to wait for the outbreak to conclude and more data analysis to occur to answer these questions in detail, and even then we may never know, but some educated speculation may be illustrative. The Ebolavirus genus is comprised of five species, Zaire, Sudan, Tai Forest, Bundibugyo, and Reston, each associated with a consistent case fatality and more or less well-identified endemic area (Figure 2). Zaire ebolavirus had been previously found only in three Central African countries—the Democratic Republic of the Congo, Republic of the Congo, and Gabon. Thus, the logical assumption when Ebola virus turned up in Guinea was that this would be the Tai Forest species previously noted in Guinea’s neighbor, Cote d’Ivoire. How did Zaire ebolavirus get all the way over to West Africa? The two possibilities appear to be that the virus has always been present the region, but we just never noticed, or that it was recently introduced. The initial report and phylogenetic analyses on the Guinea outbreak suggested that the Zaire ebolavirus found in Guinea is a distinct strain from that noted in Central Africa [1], thus suggesting that the virus may not be a newcomer to the region. However, subsequent reworking and interpretations of the limited genetic data have cast some doubt on this conclusion [2]. If Zaire ebolavirus had been circulating for some time in Guinea, one might expect greater sequence variation than the 97% homogeneity noted relative to that isolated from Central Africa [1]. Phylogenetic arguments aside, if Ebola virus was present in Guinea, wouldn’t we have seen cases before? Not necessarily. Many pathogens may be maintained in animals with which humans normally have little contact, thus providing limited opportunity for infection. Furthermore, the proportion of infected animals may often be very low, so even frequent contact may not result in pathogen transmission. Even if human Ebola virus infection has occurred, it may not be recognized; contrary to popular concept, the clinical presentation of viral hemorrhagic fever is often very nonspecific, with frank bleeding seen in a minority of cases, so cases may be mistaken for other, more common diseases or, in the case of Guinea, Lassa fever, which is endemic in the area of the outbreak [3]. Nor are laboratory diagnostics routinely available in West Africa for most viral hemorrhagic fevers [4]. Ebola virus testing of human serum samples collected as far back as 1996 as part of surveillance for Lassa fever in the same region as the current outbreak could help reveal whether humans had exposure to Ebola virus prior to this outbreak [3]. We are presently organizing with collaborators to conduct ELISA antigen testing, PCR, and cell culture for Ebola virus on samples from persons who met the case definition for viral hemorrhagic fever but tested negative for Lassa fever. We will also test all samples for IgG antibody to Ebola virus to explore the prevalence of past exposure. Could Zaire ebolavirus have been recently introduced into Guinea from Central Africa? Introduction from a human traveler seems unlikely; there is little regular travel or trade between Central Africa and Guinea, and Gueckedou, the remote epicenter and presumed area of first introduction, is far off the beaten path, a minimum 12 hour drive over rough roads from the capitals of Guinea, Liberia, or Sierra Leone (Figure 1). Furthermore, with the average incubation period as well as time from disease onset until death in fatal cases both a little over a week, a human traveler would have to make the trip from Central Africa to Gueckedou rather rapidly. If Ebola virus was introduced into Guinea from afar, the more likely traveler was a bat. Although a virus has not yet been isolated, PCR and serologic evidence accumulated over the past decade suggests that fruit bats are the likely reservoir for Ebola virus. The hammer-headed fruit bat (Hypsignathus monstrosus), Franquet’s epauletted fruit bat (Epomops franqueti), and the little collared fruit bat (Myonycteris torquata) are among the leading candidates [5–9]. Many of these species are common across sub-Saharan Africa, including in Guinea, and/or may migrate long distances, raising the possibility that one of these wayward flyers may have carried Ebola virus to Guinea [8]. Introduction into humans may have then occurred through exposures related to hunting and consumption of fruit bats, as has been suspected in Ebola virus outbreaks in Gabon [8]. Similar customs have been reported in Guinea, prompting the Guinean government to impose a ban on

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.007
metaresearch head score (Gemma)0.028
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Editorial · Consensus signal: Editorial
Teacher disagreement score0.021
Threshold uncertainty score0.069

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0070.028
Meta-epidemiology (narrow)0.0040.001
Meta-epidemiology (broad)0.0030.002
Bibliometrics0.0060.003
Science and technology studies0.0030.002
Scholarly communication0.0070.004
Open science0.0030.002
Research integrity0.0120.016
Insufficient payload (model declined to judge)0.0210.011

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.011
GPT teacher head0.284
Teacher spread0.273 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreEditorial

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

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Citations290
Published2014
Admission routes1
Has abstractyes

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