Henipavirus zoonosis: outbreaks, animal hosts and potential new emergence
Bibliographic record
Abstract
Hendra virus (HeV) and Nipah virus (NiV) are biosafety level 4 zoonotic pathogens causing severe and often fatal neurological and respiratory disease. These agents have been recognized by the World Health Organization as top priority pathogens expected to result in severe future outbreaks. HeV has caused sporadic infections in horses and a small number of human cases in Australia since 1994. The NiV Malaysia genotype (NiV-M) was responsible for the 1998-1999 epizootic outbreak in pigs with spillover to humans in Malaysia and Singapore. Since 2001, the NiV Bangladesh genotype (NiV-B) has been the predominant strain leading to outbreaks almost every year in Bangladesh and India, with hundreds of infections in humans. The natural reservoir hosts of HeV and NiV are fruit bats, which carry the viruses without clinical manifestation. The transmission pathways of henipaviruses from bats to humans remain poorly understood. Transmissions are often bridged by an intermediate animal host, which amplifies and spreads the viruses to humans. Horses and pigs are known intermediate hosts for the HeV outbreaks in Australia and NiV-M epidemic in Malaysia and Singapore, respectively. During the NiV-B outbreaks in Bangladesh, following initial spillover thought to be through the consumption of date palm sap, the spread of infection was largely human-to-human transmission. Spillover of NiV-B in recent outbreaks in India is less understood, with the primary route of transmission from bat reservoir to the initial human infection case(s) unknown and no intermediate host established. This review aims to provide a concise update on the epidemiology of henipaviruses covering their previous and current outbreaks with emphasis on the known and potential role of livestock as intermediate hosts in disease transmission. Also included is an up-to-date summary of newly emerging henipa-like viruses and animal hosts. In these contexts we discuss knowledge gaps and new challenges in the field and propose potential future directions.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
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".